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Chen Z, Ren H, Li Q, Li X. Motion correction and super-resolution for multi-slice cardiac magnetic resonance imaging via an end-to-end deep learning approach. Comput Med Imaging Graph 2024; 115:102389. [PMID: 38692199 PMCID: PMC11144076 DOI: 10.1016/j.compmedimag.2024.102389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 03/08/2024] [Accepted: 04/19/2024] [Indexed: 05/03/2024]
Abstract
Accurate reconstruction of a high-resolution 3D volume of the heart is critical for comprehensive cardiac assessments. However, cardiac magnetic resonance (CMR) data is usually acquired as a stack of 2D short-axis (SAX) slices, which suffers from the inter-slice misalignment due to cardiac motion and data sparsity from large gaps between SAX slices. Therefore, we aim to propose an end-to-end deep learning (DL) model to address these two challenges simultaneously, employing specific model components for each challenge. The objective is to reconstruct a high-resolution 3D volume of the heart (VHR) from acquired CMR SAX slices (VLR). We define the transformation from VLR to VHR as a sequential process of motion correction and super-resolution. Accordingly, our DL model incorporates two distinct components. The first component conducts motion correction by predicting displacement vectors to re-position each SAX slice accurately. The second component takes the motion-corrected SAX slices from the first component and performs the super-resolution to fill the data gaps. These two components operate in a sequential way, and the entire model is trained end-to-end. Our model significantly reduced inter-slice misalignment from originally 3.33±0.74 mm to 1.36±0.63 mm and generated accurate high resolution 3D volumes with Dice of 0.974±0.010 for left ventricle (LV) and 0.938±0.017 for myocardium in a simulation dataset. When compared to the LAX contours in a real-world dataset, our model achieved Dice of 0.945±0.023 for LV and 0.786±0.060 for myocardium. In both datasets, our model with specific components for motion correction and super-resolution significantly enhance the performance compared to the model without such design considerations. The codes for our model are available at https://github.com/zhennongchen/CMR_MC_SR_End2End.
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Affiliation(s)
- Zhennong Chen
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Hui Ren
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Quanzheng Li
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Xiang Li
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, USA.
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2
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Córdova-Aquino J, Medellín-Castillo HI. Assessment of the elastic stiffness of human cardiac fibres after an apical infarction using finite element simulation. Proc Inst Mech Eng H 2023; 237:1261-1274. [PMID: 37865815 DOI: 10.1177/09544119231204184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2023]
Abstract
Several research works in the literature have focused on understanding the post-infarction ventricular remodelling phenomenon, but few works have considered the evaluation of the elastic behaviour of the cardiac tissue after a myocardial infarction. This paper presents an investigation focused on predicting the elastic performance of the human heart after a left ventricular apical infarction. The aim is to understand the elastic alterations of the cardiac fibres at different periods after an apical infarct. For this purpose, a hybrid method based on pressure and volume measurements of the left ventricle (LV) at different periods of ventricular remodelling, and the Finite Element Method (FEM), is developed. In addition, several performance indexes are defined to evaluate the heart performance during the ventricular remodelling process. The results show that during the first 2 weeks after a heart infarction, the cardiac fibres must support a much higher structural overload than during normal conditions. This structural overload is proportional to the aneurysm size but diminishes with the time, together with a significant reduction of the ventricular pumping capacity.
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Soundappan D, Fung ASY, Loewenstein DE, Playford D, Strange G, Kozor R, Otton J, Ugander M. Decreased diastolic hydraulic forces incrementally associate with survival beyond conventional measures of diastolic dysfunction. Sci Rep 2023; 13:16396. [PMID: 37773251 PMCID: PMC10541860 DOI: 10.1038/s41598-023-41694-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/30/2023] [Indexed: 10/01/2023] Open
Abstract
Decreased hydraulic forces during diastole contribute to reduced left ventricular (LV) filling and heart failure with preserved ejection fraction. However, their association with diastolic function and patient outcomes are unknown. The aim of this retrospective, cross-sectional study was to determine the mechanistic association between diastolic hydraulic forces, estimated by echocardiography as the atrioventricular area difference (AVAD), and both diastolic function and survival. Patients (n = 5176, median [interquartile range] 5.5 [5.0-6.1] years follow-up, 1213 events) were selected from the National Echo Database Australia (NEDA) based on the presence of relevant transthoracic echocardiographic measures, LV ejection fraction (LVEF) ≥ 50%, heart rate 50-100 beats/minute, the absence of moderate or severe valvular disease, and no prior prosthetic valve surgery. NEDA contains echocardiographic and linked national death index mortality outcome data from 1985 to 2019. AVAD was calculated as the cross-sectional area difference between the LV and left atrium. LV diastolic dysfunction was graded according to 2016 guidelines. AVAD was weakly associated with E/e', left atrial volume index, and LVEF (multivariable global R2 = 0.15, p < 0.001), and not associated with e' and peak tricuspid regurgitation velocity. Decreased AVAD was independently associated with poorer survival, and demonstrated improved model discrimination after adjustment for diastolic function grading (C-statistic [95% confidence interval] 0.644 [0.629-0.660] vs 0.606 [0.592-0.621], p < 0.001) and E/e' (0.649 [0.635-0.664] vs 0.634 [0.618-0.649], p < 0.001), respectively. Therefore, decreased hydraulic forces, estimated by AVAD, are weakly associated with diastolic dysfunction and demonstrate an incremental prognostic association with survival beyond conventional measures used to grade diastolic dysfunction.
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Affiliation(s)
- Dhnanjay Soundappan
- Kolling Institute, Royal North Shore Hospital, and University of Sydney, Sydney, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney, Australia
| | - Angus S Y Fung
- Kolling Institute, Royal North Shore Hospital, and University of Sydney, Sydney, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney, Australia
| | - Daniel E Loewenstein
- Kolling Institute, Royal North Shore Hospital, and University of Sydney, Sydney, Australia
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - David Playford
- School of Medicine, University of Notre Dame, Fremantle, Australia
| | - Geoffrey Strange
- School of Medicine, University of Notre Dame, Fremantle, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Rebecca Kozor
- Kolling Institute, Royal North Shore Hospital, and University of Sydney, Sydney, Australia
| | - James Otton
- Department of Cardiology, Liverpool Hospital, University of New South Wales, Liverpool, Australia
| | - Martin Ugander
- Kolling Institute, Royal North Shore Hospital, and University of Sydney, Sydney, Australia.
- St Vincent's Clinical School, University of New South Wales, Sydney, Australia.
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden.
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4
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Hermida U, Stojanovski D, Raman B, Ariga R, Young AA, Carapella V, Carr-White G, Lukaschuk E, Piechnik SK, Kramer CM, Desai MY, Weintraub WS, Neubauer S, Watkins H, Lamata P. Left ventricular anatomy in obstructive hypertrophic cardiomyopathy: beyond basal septal hypertrophy. Eur Heart J Cardiovasc Imaging 2023; 24:807-818. [PMID: 36441173 PMCID: PMC10229266 DOI: 10.1093/ehjci/jeac233] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 10/21/2022] [Accepted: 10/23/2022] [Indexed: 11/29/2022] Open
Abstract
AIMS Obstructive hypertrophic cardiomyopathy (oHCM) is characterized by dynamic obstruction of the left ventricular (LV) outflow tract (LVOT). Although this may be mediated by interplay between the hypertrophied septal wall, systolic anterior motion of the mitral valve, and papillary muscle abnormalities, the mechanistic role of LV shape is still not fully understood. This study sought to identify the LV end-diastolic morphology underpinning oHCM. METHODS AND RESULTS Cardiovascular magnetic resonance images from 2398 HCM individuals were obtained as part of the NHLBI HCM Registry. Three-dimensional LV models were constructed and used, together with a principal component analysis, to build a statistical shape model capturing shape variations. A set of linear discriminant axes were built to define and quantify (Z-scores) the characteristic LV morphology associated with LVOT obstruction (LVOTO) under different physiological conditions and the relationship between LV phenotype and genotype. The LV remodelling pattern in oHCM consisted not only of basal septal hypertrophy but a combination with LV lengthening, apical dilatation, and LVOT inward remodelling. Salient differences were observed between obstructive cases at rest and stress. Genotype negative cases showed a tendency towards more obstructive phenotypes both at rest and stress. CONCLUSIONS LV anatomy underpinning oHCM consists of basal septal hypertrophy, apical dilatation, LV lengthening, and LVOT inward remodelling. Differences between oHCM cases at rest and stress, as well as the relationship between LV phenotype and genotype, suggest different mechanisms for LVOTO. Proposed Z-scores render an opportunity of redefining management strategies based on the relationship between LV anatomy and LVOTO.
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Affiliation(s)
- Uxio Hermida
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
| | - David Stojanovski
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
| | - Betty Raman
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Rina Ariga
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Alistair A Young
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
| | - Valentina Carapella
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
| | - Gerry Carr-White
- Department of Cardiovascular Imaging, School of Biomedical Engineering and Imaging Sciences, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - Elena Lukaschuk
- NIHR Oxford Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Stefan K Piechnik
- NIHR Oxford Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Christopher M Kramer
- Division of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, VA, USA
| | - Milind Y Desai
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland, OH, USA
| | - William S Weintraub
- MedStar Health Research Institute, Georgetown University, Washington, DC, USA
| | - Stefan Neubauer
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Hugh Watkins
- NIHR Oxford Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Pablo Lamata
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
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5
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Govil S, Crabb BT, Deng Y, Dal Toso L, Puyol-Antón E, Pushparajah K, Hegde S, Perry JC, Omens JH, Hsiao A, Young AA, McCulloch AD. A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot. J Cardiovasc Magn Reson 2023; 25:15. [PMID: 36849960 PMCID: PMC9969707 DOI: 10.1186/s12968-023-00924-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 01/25/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Cardiac shape modeling is a useful computational tool that has provided quantitative insights into the mechanisms underlying dysfunction in heart disease. The manual input and time required to make cardiac shape models, however, limits their clinical utility. Here we present an end-to-end pipeline that uses deep learning for automated view classification, slice selection, phase selection, anatomical landmark localization, and myocardial image segmentation for the automated generation of three-dimensional, biventricular shape models. With this approach, we aim to make cardiac shape modeling a more robust and broadly applicable tool that has processing times consistent with clinical workflows. METHODS Cardiovascular magnetic resonance (CMR) images from a cohort of 123 patients with repaired tetralogy of Fallot (rTOF) from two internal sites were used to train and validate each step in the automated pipeline. The complete automated pipeline was tested using CMR images from a cohort of 12 rTOF patients from an internal site and 18 rTOF patients from an external site. Manually and automatically generated shape models from the test set were compared using Euclidean projection distances, global ventricular measurements, and atlas-based shape mode scores. RESULTS The mean absolute error (MAE) between manually and automatically generated shape models in the test set was similar to the voxel resolution of the original CMR images for end-diastolic models (MAE = 1.9 ± 0.5 mm) and end-systolic models (MAE = 2.1 ± 0.7 mm). Global ventricular measurements computed from automated models were in good agreement with those computed from manual models. The average mean absolute difference in shape mode Z-score between manually and automatically generated models was 0.5 standard deviations for the first 20 modes of a reference statistical shape atlas. CONCLUSIONS Using deep learning, accurate three-dimensional, biventricular shape models can be reliably created. This fully automated end-to-end approach dramatically reduces the manual input required to create shape models, thereby enabling the rapid analysis of large-scale datasets and the potential to deploy statistical atlas-based analyses in point-of-care clinical settings. Training data and networks are available from cardiacatlas.org.
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Affiliation(s)
- Sachin Govil
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412 USA
| | - Brendan T. Crabb
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412 USA
| | - Yu Deng
- Department of Biomedical Engineering, King’s College London, London, UK
| | - Laura Dal Toso
- Department of Biomedical Engineering, King’s College London, London, UK
| | | | | | - Sanjeet Hegde
- Department of Pediatrics, University of California San Diego, La Jolla, CA USA
- Division of Cardiology, Rady Children’s Hospital San Diego, San Diego, CA USA
| | - James C. Perry
- Department of Pediatrics, University of California San Diego, La Jolla, CA USA
- Division of Cardiology, Rady Children’s Hospital San Diego, San Diego, CA USA
| | - Jeffrey H. Omens
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412 USA
| | - Albert Hsiao
- Department of Radiology, University of California San Diego, La Jolla, CA USA
| | - Alistair A. Young
- Department of Biomedical Engineering, King’s College London, London, UK
| | - Andrew D. McCulloch
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412 USA
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6
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Mauger CA, Gilbert K, Suinesiaputra A, Bluemke DA, Wu CO, Lima JAC, Young AA, Ambale-Venkatesh B. Multi-Ethnic Study of Atherosclerosis: Relationship between Left Ventricular Shape at Cardiac MRI and 10-year Outcomes. Radiology 2023; 306:e220122. [PMID: 36125376 PMCID: PMC9870985 DOI: 10.1148/radiol.220122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 07/20/2022] [Accepted: 08/08/2022] [Indexed: 02/03/2023]
Abstract
Background Left ventricular (LV) subclinical remodeling is associated with adverse outcomes and indicates mechanisms of disease development. Standard metrics such as LV mass and volumes may not capture the full range of remodeling. Purpose To quantify the relationship between LV three-dimensional shape at MRI and incident cardiovascular events over 10 years. Materials and Methods In this retrospective study, 5098 participants from the Multi-Ethnic Study of Atherosclerosis who were free of clinical cardiovascular disease underwent cardiac MRI from 2000 to 2002. LV shape models were automatically generated using a machine learning workflow. Event-specific remodeling signatures were computed using partial least squares regression, and random survival forests were used to determine which features were most associated with incident heart failure (HF), coronary heart disease (CHD), and cardiovascular disease (CVD) events over a 10-year follow-up period. The discrimination improvement of adding LV shape to traditional cardiovascular risk factors, coronary artery calcium scores, and N-terminal pro-brain natriuretic peptide levels was assessed using the index of prediction accuracy and time-dependent area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival curves were used to illustrate the ability of remodeling signatures to predict the end points. Results Overall, 4618 participants had sufficient three-dimensional MRI information to generate patient-specific LV models (mean age, 60.6 years ± 9.9 [SD]; 2540 women). Among these participants, 147 had HF, 317 had CHD, and 455 had CVD events. The addition of LV remodeling signatures to traditional cardiovascular risk factors improved the mean AUC for 10-year survival prediction and achieved better performance than LV mass and volumes; HF (AUC, 0.83 ± 0.01 and 0.81 ± 0.01, respectively; P < .05), CHD (AUC, 0.77 ± 0.01 and 0.75 ± 0.01, respectively; P < .05), and CVD (AUC, 0.78 ± 0.0 and 0.76 ± 0.0, respectively; P < .05). Kaplan-Meier analysis demonstrated that participants with high-risk HF remodeling signatures had a 10-year survival rate of 56% compared with 95% for those with low-risk scores. Conclusion Left ventricular event-specific remodeling signatures were more predictive of heart failure, coronary heart disease, and cardiovascular disease events over 10 years than standard mass and volume measures and enable an automatic personalized medicine approach to tracking remodeling. © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
| | | | - Avan Suinesiaputra
- From the Department of Anatomy and Medical Imaging, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton,
Auckland 1023, New Zealand (C.A.M.); Auckland Bioengineering Institute,
University of Auckland, Auckland, New Zealand (C.A.M., K.G.); Department of
Biomedical Engineering, King’s College London, London, UK (A.S., A.A.Y.);
Department of Radiology, University of Wisconsin School of Medicine and Public
Health, Madison, Wis (D.A.B.); and Department of Cardiology, Johns Hopkins
Medical Center, Baltimore, Md (C.O.W., J.A.C.L., B.A.V.)
| | - David A. Bluemke
- From the Department of Anatomy and Medical Imaging, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton,
Auckland 1023, New Zealand (C.A.M.); Auckland Bioengineering Institute,
University of Auckland, Auckland, New Zealand (C.A.M., K.G.); Department of
Biomedical Engineering, King’s College London, London, UK (A.S., A.A.Y.);
Department of Radiology, University of Wisconsin School of Medicine and Public
Health, Madison, Wis (D.A.B.); and Department of Cardiology, Johns Hopkins
Medical Center, Baltimore, Md (C.O.W., J.A.C.L., B.A.V.)
| | - Colin O. Wu
- From the Department of Anatomy and Medical Imaging, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton,
Auckland 1023, New Zealand (C.A.M.); Auckland Bioengineering Institute,
University of Auckland, Auckland, New Zealand (C.A.M., K.G.); Department of
Biomedical Engineering, King’s College London, London, UK (A.S., A.A.Y.);
Department of Radiology, University of Wisconsin School of Medicine and Public
Health, Madison, Wis (D.A.B.); and Department of Cardiology, Johns Hopkins
Medical Center, Baltimore, Md (C.O.W., J.A.C.L., B.A.V.)
| | - João A. C. Lima
- From the Department of Anatomy and Medical Imaging, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton,
Auckland 1023, New Zealand (C.A.M.); Auckland Bioengineering Institute,
University of Auckland, Auckland, New Zealand (C.A.M., K.G.); Department of
Biomedical Engineering, King’s College London, London, UK (A.S., A.A.Y.);
Department of Radiology, University of Wisconsin School of Medicine and Public
Health, Madison, Wis (D.A.B.); and Department of Cardiology, Johns Hopkins
Medical Center, Baltimore, Md (C.O.W., J.A.C.L., B.A.V.)
| | - Alistair A. Young
- From the Department of Anatomy and Medical Imaging, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton,
Auckland 1023, New Zealand (C.A.M.); Auckland Bioengineering Institute,
University of Auckland, Auckland, New Zealand (C.A.M., K.G.); Department of
Biomedical Engineering, King’s College London, London, UK (A.S., A.A.Y.);
Department of Radiology, University of Wisconsin School of Medicine and Public
Health, Madison, Wis (D.A.B.); and Department of Cardiology, Johns Hopkins
Medical Center, Baltimore, Md (C.O.W., J.A.C.L., B.A.V.)
| | - Bharath Ambale-Venkatesh
- From the Department of Anatomy and Medical Imaging, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton,
Auckland 1023, New Zealand (C.A.M.); Auckland Bioengineering Institute,
University of Auckland, Auckland, New Zealand (C.A.M., K.G.); Department of
Biomedical Engineering, King’s College London, London, UK (A.S., A.A.Y.);
Department of Radiology, University of Wisconsin School of Medicine and Public
Health, Madison, Wis (D.A.B.); and Department of Cardiology, Johns Hopkins
Medical Center, Baltimore, Md (C.O.W., J.A.C.L., B.A.V.)
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7
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Williams JG, Marlevi D, Bruse JL, Nezami FR, Moradi H, Fortunato RN, Maiti S, Billaud M, Edelman ER, Gleason TG. Aortic Dissection is Determined by Specific Shape and Hemodynamic Interactions. Ann Biomed Eng 2022; 50:1771-1786. [PMID: 35943618 PMCID: PMC11262626 DOI: 10.1007/s10439-022-02979-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 05/11/2022] [Indexed: 12/30/2022]
Abstract
The aim of this study was to determine whether specific three-dimensional aortic shape features, extracted via statistical shape analysis (SSA), correlate with the development of thoracic ascending aortic dissection (TAAD) risk and associated aortic hemodynamics. Thirty-one patients followed prospectively with ascending thoracic aortic aneurysm (ATAA), who either did (12 patients) or did not (19 patients) develop TAAD, were included in the study, with aortic arch geometries extracted from computed tomographic angiography (CTA) imaging. Arch geometries were analyzed with SSA, and unsupervised and supervised (linked to dissection outcome) shape features were extracted with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), respectively. We determined PLS-DA to be effective at separating dissection and no-dissection patients ([Formula: see text]), with decreased tortuosity and more equal ascending and descending aortic diameters associated with higher dissection risk. In contrast, neither PCA nor traditional morphometric parameters (maximum diameter, tortuosity, or arch volume) were effective at separating dissection and no-dissection patients. The arch shapes associated with higher dissection probability were supported with hemodynamic insight. Computational fluid dynamics (CFD) simulations revealed a correlation between the PLS-DA shape features and wall shear stress (WSS), with higher maximum WSS in the ascending aorta associated with increased risk of dissection occurrence. Our work highlights the potential importance of incorporating higher dimensional geometric assessment of aortic arch anatomy in TAAD risk assessment, and in considering the interdependent influences of arch shape and hemodynamics as mechanistic contributors to TAAD occurrence.
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Affiliation(s)
- Jessica G Williams
- Thoracic and Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - David Marlevi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Jan L Bruse
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009, Donostia-San Sebastián, Spain
| | - Farhad R Nezami
- Thoracic and Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Hamed Moradi
- School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
| | - Ronald N Fortunato
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA
| | - Spandan Maiti
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marie Billaud
- Thoracic and Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Thomas G Gleason
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA.
- University of Maryland School of Medicine, 110 S, Paca Street, 7th Floor, Baltimore, MD, 21201, USA.
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8
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Marciniak M, van Deutekom AW, Toemen L, Lewandowski AJ, Gaillard R, Young AA, Jaddoe VWV, Lamata P. A three-dimensional atlas of child's cardiac anatomy and the unique morphological alterations associated with obesity. Eur Heart J Cardiovasc Imaging 2022; 23:1645-1653. [PMID: 34931224 PMCID: PMC9671403 DOI: 10.1093/ehjci/jeab271] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 12/06/2021] [Indexed: 11/13/2022] Open
Abstract
AIMS Statistical shape models (SSMs) of cardiac anatomy provide a new approach for analysis of cardiac anatomy. In adults, specific cardiac morphologies associate with cardiovascular risk factors and early disease stages. However, the relationships between morphology and risk factors in children remain unknown. We propose an SSM of the paediatric left ventricle to describe its morphological variability, examine its relationship with biometric parameters and identify adverse anatomical remodelling associated with obesity. METHODS AND RESULTS This cohort includes 2631 children (age 10.2 ± 0.6 years), mostly Western European (68.3%) with a balanced sex distribution (51.3% girls) from Generation R study. Cardiac magnetic resonance short-axis cine scans were segmented. Three-dimensional left ventricular (LV) meshes are automatically fitted to the segmentations to reconstruct the anatomies. We analyse the relationships between the LV anatomical features and participants' body surface area (BSA), age, and sex, and search for features uniquely related to obesity based on body mass index (BMI). In the SSM, 19 modes described over 90% of the population's LV shape variability. Main modes of variation were related to cardiac size, sphericity, and apical tilting. BSA, age, and sex were mostly correlated with modes describing LV size and sphericity. The modes correlated uniquely with BMI suggested that obese children present with septo-lateral tilting (R2 = 4.0%), compression in the antero-posterior direction (R2 = 3.3%), and decreased eccentricity (R2 = 2.0%). CONCLUSIONS We describe the variability of the paediatric heart morphology and identify anatomical features related to childhood obesity that could aid in risk stratification. Web service is released to provide access to the new shape parameters.
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Affiliation(s)
- Maciej Marciniak
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Kings’ College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
| | - Arend W van Deutekom
- Department of Paediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Paediatrics, Generation R Study Group, Erasmus MC, University Medical Center, Wytemaweg 80, 3015 CN Rotterdam, the Netherlands
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford Cardiovascular Clinical Research Facility, University of Oxford, Level 1 Oxford Heart Centre, John Radliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK
| | - Liza Toemen
- Department of Paediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Paediatrics, Generation R Study Group, Erasmus MC, University Medical Center, Wytemaweg 80, 3015 CN Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands
| | - Adam J Lewandowski
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford Cardiovascular Clinical Research Facility, University of Oxford, Level 1 Oxford Heart Centre, John Radliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK
| | - Romy Gaillard
- Department of Paediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Paediatrics, Generation R Study Group, Erasmus MC, University Medical Center, Wytemaweg 80, 3015 CN Rotterdam, the Netherlands
| | - Alistair A Young
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Kings’ College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
| | - Vincent W V Jaddoe
- Department of Paediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Paediatrics, Generation R Study Group, Erasmus MC, University Medical Center, Wytemaweg 80, 3015 CN Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands
| | - Pablo Lamata
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Kings’ College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
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9
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Bouwmeester TA, van de Velde L, Galenkamp H, Postema PG, Westerhof BE, van den Born BJH, Collard D. Association between the reflection magnitude and blood pressure in a multiethnic cohort: the Healthy Life in an Urban Setting study. J Hypertens 2022; 40:2263-2270. [PMID: 35950966 PMCID: PMC9553245 DOI: 10.1097/hjh.0000000000003256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 12/02/2022]
Abstract
AIMS Reflection magnitude (RM), the ratio of the amplitudes of the backward and forward central arterial pressure waves, has been shown to predict cardiovascular events. However, the association with blood pressure (BP) and hypertension is unclear. METHODS We assessed RM in 10 195 individuals of Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish and Moroccan origin aged between 18 and 70 years (54.2% female) participating in the Healthy Life in an Urban Setting study. To determine RM, central arterial pressure and flow were reconstructed from finger BP. Hypertension was defined based on office-BP and medication. Associations with BP, hypertension, and hypertensive organ damage were assessed using linear regression models with correction for relevant covariates. RESULTS Mean RM was 62.5% (standard deviation [SD] 8.0) in men and 63.8% (SD 8.1) in women. RM was lowest in Dutch and highest in South-Asian and African participants. RM increased linearly with 1.35 (95% confidence interval [CI] 1.23-1.46) for every 10 mmHg increase in systolic BP from 120 mmHg onwards, while the relation with diastolic BP was nonlinear. RM was 2.40 (95% CI 2.04-2.76) higher in hypertensive men and 3.82 (95% CI 3.46-4.19) higher in hypertensive women compared to normotensive men and women. In hypertensive men and women with ECG-based left ventricular hypertrophy or albuminuria RM was 1.64 (95% CI 1.09-2.20) and 0.94 (95% CI 0.37-1.52) higher compared to hypertensive participants without hypertensive organ damage. CONCLUSION RM is associated with BP, hypertension and hypertensive organ damage, and may in part explain disparities in hypertension associated cardiovascular risk.
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Affiliation(s)
- Thomas A. Bouwmeester
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam
| | - Lennart van de Velde
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam
- Faculty of Science and Technology, Multi-Modality Medical Imaging Group, Technical Medical Centre, University of Twente, Enschede
| | - Henrike Galenkamp
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam
| | - Pieter G. Postema
- Department of Cardiology, Heart Center, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences
| | - Berend E. Westerhof
- Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Bert-Jan H. van den Born
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam
| | - Didier Collard
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam
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10
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Bouwmeester TA, van de Velde L, Galenkamp H, Postema PG, Westerhof BE, van den Born BJH, Collard D. Association between the reflection magnitude and blood pressure in a multiethnic cohort: the Healthy Life in an Urban Setting study. J Hypertens 2022; 40:2263-2270. [DOI: https:/doi.org/10.1097%2fhjh.0000000000003256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Aims:
Reflection magnitude (RM), the ratio of the amplitudes of the backward and forward central arterial pressure waves, has been shown to predict cardiovascular events. However, the association with blood pressure (BP) and hypertension is unclear.
Methods:
We assessed RM in 10 195 individuals of Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish and Moroccan origin aged between 18 and 70 years (54.2% female) participating in the Healthy Life in an Urban Setting study. To determine RM, central arterial pressure and flow were reconstructed from finger BP. Hypertension was defined based on office-BP and medication. Associations with BP, hypertension, and hypertensive organ damage were assessed using linear regression models with correction for relevant covariates.
Results:
Mean RM was 62.5% (standard deviation [SD] 8.0) in men and 63.8% (SD 8.1) in women. RM was lowest in Dutch and highest in South-Asian and African participants. RM increased linearly with 1.35 (95% confidence interval [CI] 1.23–1.46) for every 10 mmHg increase in systolic BP from 120 mmHg onwards, while the relation with diastolic BP was nonlinear. RM was 2.40 (95% CI 2.04–2.76) higher in hypertensive men and 3.82 (95% CI 3.46–4.19) higher in hypertensive women compared to normotensive men and women. In hypertensive men and women with ECG-based left ventricular hypertrophy or albuminuria RM was 1.64 (95% CI 1.09–2.20) and 0.94 (95% CI 0.37–1.52) higher compared to hypertensive participants without hypertensive organ damage.
Conclusion:
RM is associated with BP, hypertension and hypertensive organ damage, and may in part explain disparities in hypertension associated cardiovascular risk.
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Affiliation(s)
- Thomas A. Bouwmeester
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam
| | - Lennart van de Velde
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam
- Faculty of Science and Technology, Multi-Modality Medical Imaging Group, Technical Medical Centre, University of Twente, Enschede
| | - Henrike Galenkamp
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam
| | - Pieter G. Postema
- Department of Cardiology, Heart Center, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences
| | - Berend E. Westerhof
- Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Bert-Jan H. van den Born
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam
| | - Didier Collard
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam
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11
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Govil S, Hegde S, Perry JC, Omens JH, McCulloch AD. An Atlas-Based Analysis of Biventricular Mechanics in Tetralogy of Fallot. STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. STACOM (WORKSHOP) 2022; 13593:112-122. [PMID: 37251544 PMCID: PMC10226763 DOI: 10.1007/978-3-031-23443-9_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The current study proposes an efficient strategy for exploiting the statistical power of cardiac atlases to investigate whether clinically significant variations in ventricular shape are sufficient to explain corresponding differences in ventricular wall motion directly, or if they are indirect markers of altered myocardial mechanical properties. This study was conducted in a cohort of patients with repaired tetralogy of Fallot (rTOF) that face long-term right ventricular (RV) and/or left ventricular (LV) dysfunction as a consequence of adverse remodeling. Features of biventricular end-diastolic (ED) shape associated with RV apical dilation, LV dilation, RV basal bulging, and LV conicity correlated with components of systolic wall motion (SWM) that contribute most to differences in global systolic function. A finite element analysis of systolic biventricular mechanics was employed to assess the effect of perturbations in these ED shape modes on corresponding components of SWM. Perturbations to ED shape modes and myocardial contractility explained observed variation in SWM to varying degrees. In some cases, shape markers were partial determinants of systolic function and, in other cases, they were indirect markers for altered myocardial mechanical properties. Patients with rTOF may benefit from an atlas-based analysis of biventricular mechanics to improve prognosis and gain mechanistic insight into underlying myocardial pathophysiology.
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Affiliation(s)
- Sachin Govil
- Department of Bioengineering, University of California San Diego, San Diego, USA
| | - Sanjeet Hegde
- Division of Cardiology, Rady Children's Hospital San Diego, San Diego, USA
| | - James C Perry
- Division of Cardiology, Rady Children's Hospital San Diego, San Diego, USA
| | - Jeffrey H Omens
- Department of Bioengineering, University of California San Diego, San Diego, USA
| | - Andrew D McCulloch
- Department of Bioengineering, University of California San Diego, San Diego, USA
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12
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Bhatia D, Kanakatte A, Ghose A. Cardiac Anomaly Detection from Cine MRI Images using Physiological Features and Random Forest Classifier. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3801-3804. [PMID: 36085817 DOI: 10.1109/embc48229.2022.9871368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Computer-aided diagnosis (CAD) with cine MRI is a foremost research topic to enable improved, faster, and more accurate diagnosis of cardiovascular diseases (CVD). However, current approaches that use manual visualization or conventional clinical indices can lack accuracy for borderline cases. Also, manual visualization of 3D/4D MR data is time-consuming and expert-dependent. We try to simplify this process by creating an end-to-end automated CAD system that segments the critical substructures of the heart. The new domain-related physiological features are then calculated from the segmented regions. These features are fed to a random forest classifier that identifies the anomaly. We have obtained a very high accuracy when testing this end-to-end approach on the Automated Cardiac Diagnosis challenge (ACDC) dataset (4 pathologies, 1 normal). To prove the generalizability of the method we have blind-tested this approach on M&Ms-2 dataset which is a multi-center, multi-vendor, and multi-disease dataset with better than 90% accuracy.
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13
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Moal O, Roger E, Lamouroux A, Younes C, Bonnet G, Moal B, Lafitte S. Explicit and automatic ejection fraction assessment on 2D cardiac ultrasound with a deep learning-based approach. Comput Biol Med 2022; 146:105637. [PMID: 35617727 DOI: 10.1016/j.compbiomed.2022.105637] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/01/2022] [Accepted: 04/29/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND Ejection fraction (EF) is a key parameter for assessing cardiovascular functions in cardiac ultrasound, but its manual assessment is time-consuming and subject to high inter and intra-observer variability. Deep learning-based methods have the potential to perform accurate fully automatic EF predictions but suffer from a lack of explainability and interpretability. This study proposes a fully automatic method to reliably and explicitly evaluate the biplane left ventricular EF on 2D echocardiography following the recommended modified Simpson's rule. METHODS A deep learning model was trained on apical 4 and 2-chamber echocardiography to segment the left ventricle and locate the mitral valve. Predicted segmentations are then validated with a statistical shape model, which detects potential failures that could impact the EF evaluation. Finally, the end-diastolic and end-systolic frames are identified based on the remaining LV segmentations' areas and EF is estimated on all available cardiac cycles. RESULTS Our approach was trained on a dataset of 783 patients. Its performances were evaluated on an internal and external dataset of respectively 200 and 450 patients. On the internal dataset, EF assessment achieved a mean absolute error of 6.10% and a bias of 1.56 ± 7.58% using multiple cardiac cycles. The approach evaluated EF with a mean absolute error of 5.39% and a bias of -0.74 ± 7.12% on the external dataset. CONCLUSION Following the recommended guidelines, we proposed an end-to-end fully automatic approach that achieves state-of-the-art performance in biplane EF evaluation while giving explicit details to clinicians.
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Affiliation(s)
| | | | | | | | - Guillaume Bonnet
- Hôpital Cardiologique Haut Lévêque, CHU de Bordeaux, CIC 0005, Pessac, France.
| | | | - Stephane Lafitte
- Hôpital Cardiologique Haut Lévêque, CHU de Bordeaux, CIC 0005, Pessac, France.
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14
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Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networks. Med Image Anal 2022; 78:102383. [DOI: 10.1016/j.media.2022.102383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 11/24/2021] [Accepted: 02/01/2022] [Indexed: 11/16/2022]
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15
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Suinesiaputra A, Mauger CA, Ambale-Venkatesh B, Bluemke DA, Dam Gade J, Gilbert K, Janse MHA, Hald LS, Werkhoven C, Wu CO, Lima JAC, Young AA. Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis. Front Cardiovasc Med 2022; 8:807728. [PMID: 35127868 PMCID: PMC8813768 DOI: 10.3389/fcvm.2021.807728] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 12/24/2021] [Indexed: 12/23/2022] Open
Abstract
The Multi-Ethnic Study of Atherosclerosis (MESA), begun in 2000, was the first large cohort study to incorporate cardiovascular magnetic resonance (CMR) to study the mechanisms of cardiovascular disease in over 5,000 initially asymptomatic participants, and there is now a wealth of follow-up data over 20 years. However, the imaging technology used to generate the CMR images is no longer in routine use, and methods trained on modern data fail when applied to such legacy datasets. This study aimed to develop a fully automated CMR analysis pipeline that leverages the ability of machine learning algorithms to enable extraction of additional information from such a large-scale legacy dataset, expanding on the original manual analyses. We combined the original study analyses with new annotations to develop a set of automated methods for customizing 3D left ventricular (LV) shape models to each CMR exam and build a statistical shape atlas. We trained VGGNet convolutional neural networks using a transfer learning sequence between two-chamber, four-chamber, and short-axis MRI views to detect landmarks. A U-Net architecture was used to detect the endocardial and epicardial boundaries in short-axis images. The landmark detection network accurately predicted mitral valve and right ventricular insertion points with average error distance <2.5 mm. The agreement of the network with two observers was excellent (intraclass correlation coefficient >0.9). The segmentation network produced average Dice score of 0.9 for both myocardium and LV cavity. Differences between the manual and automated analyses were small, i.e., <1.0 ± 2.6 mL/m2 for indexed LV volume, 3.0 ± 6.4 g/m2 for indexed LV mass, and 0.6 ± 3.3% for ejection fraction. In an independent atlas validation dataset, the LV atlas built from the fully automated pipeline showed similar statistical relationships to an atlas built from the manual analysis. Hence, the proposed pipeline is not only a promising framework to automatically assess additional measures of ventricular function, but also to study relationships between cardiac morphologies and future cardiac events, in a large-scale population study.
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Affiliation(s)
- Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Charlène A. Mauger
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | | | - David A. Bluemke
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Josefine Dam Gade
- Department of Biomedical Engineering and Informatics, School of Medicine and Health, Aalborg University, Aalborg, Denmark
| | - Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Markus H. A. Janse
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Line Sofie Hald
- Department of Biomedical Engineering and Informatics, School of Medicine and Health, Aalborg University, Aalborg, Denmark
| | - Conrad Werkhoven
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Colin O. Wu
- Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Baltimore, MD, United States
| | | | - Alistair A. Young
- Faculty of Life Sciences & Medicine, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
- *Correspondence: Alistair A. Young
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16
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Maxime DF, Pamela M, Patrick C, Nicolas D. Characterizing interactions between cardiac shape and deformation by non-linear manifold learning. Med Image Anal 2021; 75:102278. [PMID: 34731772 DOI: 10.1016/j.media.2021.102278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 09/08/2021] [Accepted: 10/18/2021] [Indexed: 10/20/2022]
Abstract
In clinical routine, high-dimensional descriptors of the cardiac function such as shape and deformation are reduced to scalars (e.g. volumes or ejection fraction), which limit the characterization of complex diseases. Besides, these descriptors undergo interactions depending on disease, which may bias their computational analysis. In this paper, we aim at characterizing such interactions by unsupervised manifold learning. We propose to use a sparsified version of Multiple Manifold Learning to align the latent spaces encoding each descriptor and weighting the strength of the alignment depending on each pair of samples. While this framework was up to now only applied to link different datasets from the same manifold, we demonstrate its relevance to characterize the interactions between different but partially related descriptors of the cardiac function (shape and deformation). We benchmark our approach against linear and non-linear embedding strategies, among which the fusion of manifolds by Multiple Kernel Learning, the independent embedding of each descriptor by Diffusion Maps, and a strict alignment based on pairwise correspondences. We first evaluated the methods on a synthetic dataset from a 0D cardiac model where the interactions between descriptors are fully controlled. Then, we transfered them to a population of right ventricular meshes from 310 subjects (100 healthy and 210 patients with right ventricular disease) obtained from 3D echocardiography, where the link between shape and deformation is key for disease understanding. Our experiments underline the relevance of jointly considering shape and deformation descriptors, and that manifold alignment is preferable over fusion for our application. They also confirm at a finer scale the characteristic traits of the right ventricular diseases in our population.
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Affiliation(s)
- Di Folco Maxime
- Univ Lyon, UCBL, Inserm, INSA Lyon, CNRS, CREATIS, UMR5220, U1294,Villeurbanne 69621, France.
| | - Moceri Pamela
- Centre Hospitalier Universitaire de Nice, Service de Cardiologie, Nice, France
| | - Clarysse Patrick
- Univ Lyon, UCBL, Inserm, INSA Lyon, CNRS, CREATIS, UMR5220, U1294,Villeurbanne 69621, France
| | - Duchateau Nicolas
- Univ Lyon, UCBL, Inserm, INSA Lyon, CNRS, CREATIS, UMR5220, U1294,Villeurbanne 69621, France
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17
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Zakeri A, Hokmabadi A, Ravikumar N, Frangi AF, Gooya A. A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment. Med Image Anal 2021; 75:102276. [PMID: 34753021 DOI: 10.1016/j.media.2021.102276] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 10/10/2021] [Accepted: 10/15/2021] [Indexed: 11/16/2022]
Abstract
Automatic shape anomaly detection in large-scale imaging data can be useful for screening suboptimal segmentations and pathologies altering the cardiac morphology without intensive manual labour. We propose a deep probabilistic model for local anomaly detection in sequences of heart shapes, modelled as point sets, in a cardiac cycle. A deep recurrent encoder-decoder network captures the spatio-temporal dependencies to predict the next shape in the cycle and thus derive the outlier points that are attributed to excessive deviations from the network prediction. A predictive mixture distribution models the inlier and outlier classes via Gaussian and uniform distributions, respectively. A Gibbs sampling Expectation-Maximisation (EM) algorithm computes soft anomaly scores of the points via the posterior probabilities of each class in the E-step and estimates the parameters of the network and the predictive distribution in the M-step. We demonstrate the versatility of the method using two shape datasets derived from: (i) one million biventricular CMR images from 20,000 participants in the UK Biobank (UKB), and (ii) routine diagnostic imaging from Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Image (M&Ms). Experiments show that the detected shape anomalies in the UKB dataset are mostly associated with poor segmentation quality, and the predicted shape sequences show significant improvement over the input sequences. Furthermore, evaluations on U-Net based shapes from the M&Ms dataset reveals that the anomalies are attributable to the underlying pathologies that affect the ventricles. The proposed model can therefore be used as an effective mechanism to sift shape anomalies in large-scale cardiac imaging pipelines for further analysis.
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Affiliation(s)
- Arezoo Zakeri
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.
| | - Alireza Hokmabadi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK
| | - Nishant Ravikumar
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK
| | - Alejandro F Frangi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK
| | - Ali Gooya
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.
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18
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Forsch N, Govil S, Perry JC, Hegde S, Young AA, Omens JH, McCulloch AD. Computational analysis of cardiac structure and function in congenital heart disease: Translating discoveries to clinical strategies. JOURNAL OF COMPUTATIONAL SCIENCE 2021; 52:101211. [PMID: 34691293 PMCID: PMC8528218 DOI: 10.1016/j.jocs.2020.101211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Increased availability and access to medical image data has enabled more quantitative approaches to clinical diagnosis, prognosis, and treatment planning for congenital heart disease. Here we present an overview of long-term clinical management of tetralogy of Fallot (TOF) and its intersection with novel computational and data science approaches to discovering biomarkers of functional and prognostic importance. Efforts in translational medicine that seek to address the clinical challenges associated with cardiovascular diseases using personalized and precision-based approaches are then discussed. The considerations and challenges of translational cardiovascular medicine are reviewed, and examples of digital platforms with collaborative, cloud-based, and scalable design are provided.
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Affiliation(s)
- Nickolas Forsch
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Sachin Govil
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - James C Perry
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Sanjeet Hegde
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Alistair A Young
- Department of Biomedical Engineering, King’s College London, London, UK
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, NZ
| | - Jeffrey H Omens
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Deparment of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Andrew D McCulloch
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Deparment of Medicine, University of California San Diego, La Jolla, CA, USA
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19
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Raisi-Estabragh Z, Harvey NC, Neubauer S, Petersen SE. Cardiovascular magnetic resonance imaging in the UK Biobank: a major international health research resource. Eur Heart J Cardiovasc Imaging 2021; 22:251-258. [PMID: 33164079 PMCID: PMC7899275 DOI: 10.1093/ehjci/jeaa297] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/12/2020] [Indexed: 12/12/2022] Open
Abstract
The UK Biobank (UKB) is a health research resource of major international importance, incorporating comprehensive characterization of >500 000 men and women recruited between 2006 and 2010 from across the UK. There is prospective tracking of health outcomes for all participants through linkages with national cohorts (death registers, cancer registers, electronic hospital records, and primary care records). The dataset has been enhanced with the UKB imaging study, which aims to scan a subset of 100 000 participants. The imaging protocol includes magnetic resonance imaging of the brain, heart, and abdomen, carotid ultrasound, and whole-body dual X-ray absorptiometry. Since its launch in 2015, over 48 000 participants have completed the imaging study with scheduled completion in 2023. Repeat imaging of 10 000 participants has been approved and commenced in 2019. The cardiovascular magnetic resonance (CMR) scan provides detailed assessment of cardiac structure and function comprising bright blood anatomic assessment (sagittal, coronal, and axial), left and right ventricular cine images (long and short axes), myocardial tagging, native T1 mapping, aortic flow, and imaging of the thoracic aorta. The UKB is an open access resource available to health researchers across all scientific disciplines from both academia and industry with no preferential access or exclusivity. In this paper, we consider how we may best utilize the UKB CMR data to advance cardiovascular research and review notable achievements to date.
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Affiliation(s)
- Zahra Raisi-Estabragh
- William Harvey Research Institute, Centre for Advanced Cardiovascular Imaging, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, Department of Cardiac Imaging, St. Bartholomew's Hospital, Barts Health NHS Trust, London EC1A 7BE, UK
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, SO16 6YD, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, OX3 9DU, UK
| | - Steffen E Petersen
- William Harvey Research Institute, Centre for Advanced Cardiovascular Imaging, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, Department of Cardiac Imaging, St. Bartholomew's Hospital, Barts Health NHS Trust, London EC1A 7BE, UK
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20
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Cetin I, Raisi-Estabragh Z, Petersen SE, Napel S, Piechnik SK, Neubauer S, Gonzalez Ballester MA, Camara O, Lekadir K. Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank. Front Cardiovasc Med 2020; 7:591368. [PMID: 33240940 PMCID: PMC7667130 DOI: 10.3389/fcvm.2020.591368] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 10/06/2020] [Indexed: 12/25/2022] Open
Abstract
Cardiovascular magnetic resonance (CMR) radiomics is a novel technique for advanced cardiac image phenotyping by analyzing multiple quantifiers of shape and tissue texture. In this paper, we assess, in the largest sample published to date, the performance of CMR radiomics models for identifying changes in cardiac structure and tissue texture due to cardiovascular risk factors. We evaluated five risk factor groups from the first 5,065 UK Biobank participants: hypertension (n = 1,394), diabetes (n = 243), high cholesterol (n = 779), current smoker (n = 320), and previous smoker (n = 1,394). Each group was randomly matched with an equal number of healthy comparators (without known cardiovascular disease or risk factors). Radiomics analysis was applied to short axis images of the left and right ventricles at end-diastole and end-systole, yielding a total of 684 features per study. Sequential forward feature selection in combination with machine learning (ML) algorithms (support vector machine, random forest, and logistic regression) were used to build radiomics signatures for each specific risk group. We evaluated the degree of separation achieved by the identified radiomics signatures using area under curve (AUC), receiver operating characteristic (ROC), and statistical testing. Logistic regression with L1-regularization was the optimal ML model. Compared to conventional imaging indices, radiomics signatures improved the discrimination of risk factor vs. healthy subgroups as assessed by AUC [diabetes: 0.80 vs. 0.70, hypertension: 0.72 vs. 0.69, high cholesterol: 0.71 vs. 0.65, current smoker: 0.68 vs. 0.65, previous smoker: 0.63 vs. 0.60]. Furthermore, we considered clinical interpretation of risk-specific radiomics signatures. For hypertensive individuals and previous smokers, the surface area to volume ratio was smaller in the risk factor vs. healthy subjects; perhaps reflecting a pattern of global concentric hypertrophy in these conditions. In the diabetes subgroup, the most discriminatory radiomics feature was the median intensity of the myocardium at end-systole, which suggests a global alteration at the myocardial tissue level. This study confirms the feasibility and potential of CMR radiomics for deeper image phenotyping of cardiovascular health and disease. We demonstrate such analysis may have utility beyond conventional CMR metrics for improved detection and understanding of the early effects of cardiovascular risk factors on cardiac structure and tissue.
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Affiliation(s)
- Irem Cetin
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Sandy Napel
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Stefan K. Piechnik
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Miguel A. Gonzalez Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Oscar Camara
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Karim Lekadir
- Departament de Matematiques i Informatica, Universitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain
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21
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Abstract
Classification of heart failure is based on the left ventricular ejection fraction (EF): preserved EF, midrange EF, and reduced EF. There remains an unmet need for further heart failure phenotyping of ventricular structure-function relationships. Because of high spatiotemporal resolution, cardiac magnetic resonance (CMR) remains the reference modality for quantification of ventricular contractile function. The authors aim to highlight novel frameworks, including theranostic use of ferumoxytol, to enable more efficient evaluation of ventricular function in heart failure patients who are also frequently anemic, and to discuss emerging quantitative CMR approaches for evaluation of ventricular structure-function relationships in heart failure.
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22
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Discretely-constrained deep network for weakly supervised segmentation. Neural Netw 2020; 130:297-308. [DOI: 10.1016/j.neunet.2020.07.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 06/17/2020] [Accepted: 07/11/2020] [Indexed: 11/18/2022]
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23
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Narayan HK, Xu R, Forsch N, Govil S, Iukuridze D, Lindenfeld L, Adler E, Hegde S, Tremoulet A, Ky B, Armenian S, Omens J, McCulloch AD. Atlas-based measures of left ventricular shape may improve characterization of adverse remodeling in anthracycline-exposed childhood cancer survivors: a cross-sectional imaging study. CARDIO-ONCOLOGY 2020; 6:13. [PMID: 32782827 PMCID: PMC7414730 DOI: 10.1186/s40959-020-00069-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 07/31/2020] [Indexed: 11/28/2022]
Abstract
Background Adverse cardiac remodeling is an important precursor to anthracycline-related cardiac dysfunction, however conventional remodeling indices are limited. We sought to examine the utility of statistical atlas-derived measures of ventricular shape to improve the identification of adverse anthracycline-related remodeling in childhood cancer survivors. Methods We analyzed cardiac magnetic resonance imaging from a cross-sectional cohort of 20 childhood cancer survivors who were treated with low (< 250 mg/m2 [N = 10]) or high (≥250 mg/m2 [N = 10]) dose anthracyclines, matched 1:1 by sex and age between dose groups. We reconstructed 3D computational models of left ventricular end-diastolic shape for each subject and assessed the ability of conventional remodeling indices (volume, mass, and mass to volume ratio) vs. shape modes derived from a statistical shape atlas of an asymptomatic reference population to stratify anthracycline-related remodeling. We compared conventional parameters and five atlas-based shape modes: 1) between survivors and the reference population (N = 1991) using multivariable linear regression, and 2) within survivors by anthracycline dose (low versus high) using two-sided T-tests, multivariable logistic regression, and receiver operating characteristic curves. Results Compared with the reference population, survivors had differences in conventional measures (lower volume and mass) and shape modes (corresponding to lower overall size and lower sphericity; all p < 0.001). Among survivors, differences in a shape mode corresponding to increased basal cavity size and altered mitral annular orientation in the high-dose group were observed (p = 0.039). Collectively, atlas-based shape modes in conjunction with conventional measures discriminated survivors who received low vs. high anthracycline dosage (area under the curve [AUC] 0.930, 95% confidence interval 0.816, 1.00) significantly better than conventional measures alone (AUC 0.710, 95% confidence interval 0.473, 0.947; AUC comparison p = 0.0498). Conclusions Compared with a reference population, heart size is smaller in anthracycline-exposed childhood cancer survivors. Atlas-based measures of left ventricular shape may improve the detection of anthracycline dose-related remodeling differences.
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Affiliation(s)
- Hari K Narayan
- Department of Pediatrics, University of California San Diego, 9500 Gilman Drive #0831, La Jolla, CA 92093-0831 USA
| | - Ronghui Xu
- Department of Family Medicine and Public Health, University of California San Diego, 9500 Gilman Drive #0628, La Jolla, CA 92093-0628 USA.,Department of Mathematics, University of California San Diego, 9500 Gilman Drive #0112, La Jolla, CA 92093-0112 USA
| | - Nickolas Forsch
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412 USA
| | - Sachin Govil
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412 USA
| | - David Iukuridze
- Department of Pediatrics, University of California San Diego, 9500 Gilman Drive #0831, La Jolla, CA 92093-0831 USA
| | - Lanie Lindenfeld
- Department of Population Sciences, City of Hope, 1500 E. Duarte Rd, Duarte, CA 91010 USA
| | - Eric Adler
- Department of Medicine, University of California San Diego, 9500 Gilman Drive #8811, La Jolla, CA 92093-8811 USA
| | - Sanjeet Hegde
- Department of Pediatrics, University of California San Diego, 9500 Gilman Drive #0831, La Jolla, CA 92093-0831 USA
| | - Adriana Tremoulet
- Department of Pediatrics, University of California San Diego, 9500 Gilman Drive #0831, La Jolla, CA 92093-0831 USA
| | - Bonnie Ky
- Department of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104 USA
| | - Saro Armenian
- Department of Population Sciences, City of Hope, 1500 E. Duarte Rd, Duarte, CA 91010 USA
| | - Jeffrey Omens
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412 USA.,Department of Medicine, University of California San Diego, 9500 Gilman Drive #8811, La Jolla, CA 92093-8811 USA
| | - Andrew D McCulloch
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412 USA.,Department of Medicine, University of California San Diego, 9500 Gilman Drive #8811, La Jolla, CA 92093-8811 USA
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24
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Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, Foran D, Do N, Golemati S, Kurc T, Huang K, Nikita KS, Veasey BP, Zervakis M, Saltz JH, Pattichis CS. AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE J Biomed Health Inform 2020; 24:1837-1857. [PMID: 32609615 PMCID: PMC8580417 DOI: 10.1109/jbhi.2020.2991043] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.
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25
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Chen C, Bai W, Davies RH, Bhuva AN, Manisty CH, Augusto JB, Moon JC, Aung N, Lee AM, Sanghvi MM, Fung K, Paiva JM, Petersen SE, Lukaschuk E, Piechnik SK, Neubauer S, Rueckert D. Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images. Front Cardiovasc Med 2020; 7:105. [PMID: 32714943 PMCID: PMC7344224 DOI: 10.3389/fcvm.2020.00105] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 05/20/2020] [Indexed: 11/22/2022] Open
Abstract
Background: Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g., same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. Methods: We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). Results: The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium, and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. Conclusions: The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task.
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Affiliation(s)
- Chen Chen
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Wenjia Bai
- Data Science Institute, Imperial College London, London, United Kingdom.,Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Rhodri H Davies
- Institute of Cardiovascular Science, University College London, London, United Kingdom.,Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - Anish N Bhuva
- Institute of Cardiovascular Science, University College London, London, United Kingdom.,Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - Charlotte H Manisty
- Institute of Cardiovascular Science, University College London, London, United Kingdom.,Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - Joao B Augusto
- Institute of Cardiovascular Science, University College London, London, United Kingdom.,Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - James C Moon
- Institute of Cardiovascular Science, University College London, London, United Kingdom.,Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - Nay Aung
- Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom.,NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
| | - Aaron M Lee
- Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom.,NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
| | - Mihir M Sanghvi
- Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom.,NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
| | - Kenneth Fung
- Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom.,NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
| | - Jose Miguel Paiva
- Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom.,NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
| | - Steffen E Petersen
- Department of Cardiovascular Imaging, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom.,NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
| | - Elena Lukaschuk
- NIHR BRC Oxford, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, London, United Kingdom
| | - Stefan K Piechnik
- NIHR BRC Oxford, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, London, United Kingdom
| | - Stefan Neubauer
- NIHR BRC Oxford, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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26
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Gilbert K, Mauger C, Young AA, Suinesiaputra A. Artificial Intelligence in Cardiac Imaging With Statistical Atlases of Cardiac Anatomy. Front Cardiovasc Med 2020; 7:102. [PMID: 32695795 PMCID: PMC7338378 DOI: 10.3389/fcvm.2020.00102] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 05/14/2020] [Indexed: 12/14/2022] Open
Abstract
In many cardiovascular pathologies, the shape and motion of the heart provide important clues to understanding the mechanisms of the disease and how it progresses over time. With the advent of large-scale cardiac data, statistical modeling of cardiac anatomy has become a powerful tool to provide automated, precise quantification of the status of patient-specific heart geometry with respect to reference populations. Powered by supervised or unsupervised machine learning algorithms, statistical cardiac shape analysis can be used to automatically identify and quantify the severity of heart diseases, to provide morphometric indices that are optimally associated with clinical factors, and to evaluate the likelihood of adverse outcomes. Recently, statistical cardiac atlases have been integrated with deep neural networks to enable anatomical consistency of cardiac segmentation, registration, and automated quality control. These combinations have already shown significant improvements in performance and avoid gross anatomical errors that could make the results unusable. This current trend is expected to grow in the near future. Here, we aim to provide a mini review highlighting recent advances in statistical atlasing of cardiac function in the context of artificial intelligence in cardiac imaging.
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Affiliation(s)
- Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Charlène Mauger
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.,Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.,Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.,Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, Leeds, United Kingdom.,School of Medicine, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
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27
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Strocchi M, Augustin CM, Gsell MAF, Karabelas E, Neic A, Gillette K, Razeghi O, Prassl AJ, Vigmond EJ, Behar JM, Gould J, Sidhu B, Rinaldi CA, Bishop MJ, Plank G, Niederer SA. A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations. PLoS One 2020; 15:e0235145. [PMID: 32589679 PMCID: PMC7319311 DOI: 10.1371/journal.pone.0235145] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 06/09/2020] [Indexed: 12/12/2022] Open
Abstract
Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients. To facilitate the development of new simulation and model analysis techniques by groups without direct access to medical data, image analysis techniques and meshing tools, we have created the first publicly available virtual cohort of twenty-four four-chamber hearts. Our cohort was built from heart failure patients, age 67±14 years. We segmented four-chamber heart geometries from end-diastolic (ED) CT images and generated linear tetrahedral meshes with an average edge length of 1.1±0.2mm. Ventricular fibres were added in the ventricles with a rule-based method with an orientation of -60° and 80° at the epicardium and endocardium, respectively. We additionally refined the meshes to an average edge length of 0.39±0.10mm to show that all given meshes can be resampled to achieve an arbitrary desired resolution. We ran simulations for ventricular electrical activation and free mechanical contraction on all 1.1mm-resolution meshes to ensure that our meshes are suitable for electro-mechanical simulations. Simulations for electrical activation resulted in a total activation time of 149±16ms. Free mechanical contractions gave an average left ventricular (LV) and right ventricular (RV) ejection fraction (EF) of 35±1% and 30±2%, respectively, and a LV and RV stroke volume (SV) of 95±28mL and 65±11mL, respectively. By making the cohort publicly available, we hope to facilitate large cohort computational studies and to promote the development of cardiac computational electro-mechanics for clinical applications.
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Affiliation(s)
- Marina Strocchi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | | | | | - Elias Karabelas
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | | | - Karli Gillette
- Institute of Biophysics, Medical University of Graz, Graz, Steiermark, Austria
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | - Anton J. Prassl
- Institute of Biophysics, Medical University of Graz, Graz, Steiermark, Austria
| | - Edward J. Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- University of Bordeaux, IMB, UMR 5251, F-33400 Talence, France
| | - Jonathan M. Behar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Justin Gould
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Baldeep Sidhu
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Christopher A. Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Martin J. Bishop
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Steiermark, Austria
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
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28
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Liu D, Dangi S, Schwarz KQ, Linte CA. Combining Statistical Shape Model and Principal Component Analysis to Estimate Left Ventricular Volume and Ejection Fraction. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2020; 11319:113190E. [PMID: 32699463 PMCID: PMC7375748 DOI: 10.1117/12.2550650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Left ventricular ejection fraction (LVEF) assessment is instrumental for cardiac health diagnosis, patient management, and patient eligibility for participation in clinical studies. Due to its non-invasiveness and low operational cost, ultrasound (US) imaging is the most commonly used imaging modality to image the heart and assess LVEF. Even though 3D US imaging technology is becoming more available, cardiologists dominantly use 2D US imaging to visualize the LV blood pool and interpret its area changes between end-systole and end-diastole. Our previous work showed that LVEF estimates based on area changes are significantly lower than the true volume-based estimates by as much as 13%,1 which could lead to unnecessary and costly therapeutic decisions. Acquiring volumetric information about the LV blood pool necessitates either time-consuming 3D reconstruction or 3D US image acquisition. Here, we propose a method that leverages on a statistical shape model (SSM) constructed from 13 landmarks depicting the LV endocardial border to estimate a new patient's LV volume and LVEF. Two methods to estimate the 3D LV geometry with and without size normalization were employed. The SSM was built using the 13 landmarks from 50 training patient image datasets. Subsequently, the Mahalanobis distance (with size normalization) or the vector distance (without size normalization) between an incoming patient's LV landmarks and each shape in the SSM were used to determine the weights each training patient contributed to describing the new, incoming patient's LV geometry and associated blood pool volume. We tested the proposed method to estimate the LV volumes and LVEF for 16 new test patients. The estimated LVEFs based on Mahalanobis distance and vector distance were within 2.9% and 1.1%, respectively, of the ground truth LVEFs calculated from the 3D reconstructed LV volumes. Furthermore, the viability of using fewer principal components (PCs) to estimate the LV volume was explored by reducing the number of PCs retained when projecting landmarks onto PCA space. LVEF estimated based on 3 PCs, 5 PCs, and 10 PCs are within 6.6%, 5.4%, and 3.3%, respectively, of LVEF estimates using the full set of 39 PCs.
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Affiliation(s)
- Dawei Liu
- Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
| | - Shusil Dangi
- Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
| | - Karl Q Schwarz
- Medicine, Cardiology, University of Rochester Medical Center, Rochester, NY, USA
- Anesthesiology and Perioperative Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Cristian A Linte
- Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
- Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA
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29
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Liu D, Peck I, Dangi S, Schwarz KQ, Linte CA. A Statistical Shape Model Approach for Computing Left Ventricle Volume and Ejection Fraction Using Multi-plane Ultrasound Images. VIPIMAGE 2019 : PROCEEDINGS OF THE VII ECCOMAS THEMATIC CONFERENCE ON COMPUTATIONAL VISION AND MEDICAL IMAGE PROCESSING, OCTOBER 16-18, 2019, PORTO, PORTUGAL. VIPIMAGE (CONFERENCE) (2019 : PORTO, PORTUGAL) 2019; 34:540-550. [PMID: 32661520 PMCID: PMC7357900 DOI: 10.1007/978-3-030-32040-9_55] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Assessing the left ventricular ejection fraction (LVEF) accurately requires 3D volumetric data of the LV. Cardiologists either have no access to 3D ultrasound (US) systems or prefer to visually estimate LVEF based on 2D US images. To facilitate the consistent estimation of the end-diastolic and end-systolic blood pool volume and LVEF based on 3D data without extensive complicated manual input, we propose a statistical shape model (SSM) based on 13 key anchor points-the LV apex (1), mitral valve hinges (6), and the midpoints of the endocardial contours (6)-identified from the LV endocardial contour of the tri-plane 2D US images. We use principal component analysis (PCA) to identify the principle modes of variation needed to represent the LV shapes, which enables us to estimate an incoming LV as a linear combination of the principle components (PC). For a new, incoming patient image, its 13 anchor points are projected onto the PC space; its shape is compared to each LV shape in the SSM based on Mahalanobis distance, which is normalized with respect to the LV size, as well as direct vector distance (i.e., PCA distance), without any size normalization. These distances are used to determine the weight each training shape in the SSM contributes to the description of the new patient LV shape. Finally, the new patient's LV systolic and diastolic volumes are estimated as the weighted average of the training volumes in the SSM. To assess our proposed method, we compared the SSM-based estimates of diastolic, systolic, stroke volumes, and LVEF with those computed directly from 16 tri-plane 2D US imaging datasets using the GE Echo-Pac PC clinical platform. The estimated LVEF based on Mahalanobis distance and PCA distance were within 6.8% and 1.7% of the reference LVEF computed using the GE Echo-Pac PC clinical platform.
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Affiliation(s)
- Dawei Liu
- Rochester Institute of Technology, 1 Lomb Memorial Drive, Rochester, NY 14623, USA
| | - Isabelle Peck
- Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA
| | - Shusil Dangi
- Rochester Institute of Technology, 1 Lomb Memorial Drive, Rochester, NY 14623, USA
| | - Karl Q Schwarz
- University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642, USA
| | - Cristian A Linte
- Rochester Institute of Technology, 1 Lomb Memorial Drive, Rochester, NY 14623, USA
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30
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Mauger C, Gilbert K, Lee AM, Sanghvi MM, Aung N, Fung K, Carapella V, Piechnik SK, Neubauer S, Petersen SE, Suinesiaputra A, Young AA. Right ventricular shape and function: cardiovascular magnetic resonance reference morphology and biventricular risk factor morphometrics in UK Biobank. J Cardiovasc Magn Reson 2019; 21:41. [PMID: 31315625 PMCID: PMC6637624 DOI: 10.1186/s12968-019-0551-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 06/14/2019] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND The associations between cardiovascular disease (CVD) risk factors and the biventricular geometry of the right ventricle (RV) and left ventricle (LV) have been difficult to assess, due to subtle and complex shape changes. We sought to quantify reference RV morphology as well as biventricular variations associated with common cardiovascular risk factors. METHODS A biventricular shape atlas was automatically constructed using contours and landmarks from 4329 UK Biobank cardiovascular magnetic resonance (CMR) studies. A subdivision surface geometric mesh was customized to the contours using a diffeomorphic registration algorithm, with automatic correction of slice shifts due to differences in breath-hold position. A reference sub-cohort was identified consisting of 630 participants with no CVD risk factors. Morphometric scores were computed using linear regression to quantify shape variations associated with four risk factors (high cholesterol, high blood pressure, obesity and smoking) and three disease factors (diabetes, previous myocardial infarction and angina). RESULTS The atlas construction led to an accurate representation of 3D shapes at end-diastole and end-systole, with acceptable fitting errors between surfaces and contours (average error less than 1.5 mm). Atlas shape features had stronger associations than traditional mass and volume measures for all factors (p < 0.005 for each). High blood pressure was associated with outward displacement of the LV free walls, but inward displacement of the RV free wall and thickening of the septum. Smoking was associated with a rounder RV with inward displacement of the RV free wall and increased relative wall thickness. CONCLUSION Morphometric relationships between biventricular shape and cardiovascular risk factors in a large cohort show complex interactions between RV and LV morphology. These can be quantified by z-scores, which can be used to study the morphological correlates of disease.
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Affiliation(s)
- Charlène Mauger
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Kathleen Gilbert
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Aaron M. Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
| | - Mihir M. Sanghvi
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
| | - Kenneth Fung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
| | - Valentina Carapella
- Oxford NIHR Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Stefan K. Piechnik
- Oxford NIHR Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Stefan Neubauer
- Oxford NIHR Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Alistair A. Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, King’s College London, London, UK
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Ruijsink B, Puyol-Antón E, Oksuz I, Sinclair M, Bai W, Schnabel JA, Razavi R, King AP. Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function. JACC Cardiovasc Imaging 2019; 13:684-695. [PMID: 31326477 PMCID: PMC7060799 DOI: 10.1016/j.jcmg.2019.05.030] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/26/2019] [Accepted: 05/16/2019] [Indexed: 12/13/2022]
Abstract
Objectives This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output. Background Analysis of cine CMR imaging using deep learning (DL) algorithms could automate ventricular function assessment. However, variable image quality, variability in phenotypes of disease, and unavoidable weaknesses in training of DL algorithms currently prevent their use in clinical practice. Methods The framework consists of a pre-analysis DL image QC, followed by a DL algorithm for biventricular segmentation in long-axis and short-axis views, myocardial feature-tracking (FT), and a post-analysis QC to detect erroneous results. The study validated the framework in healthy subjects and cardiac patients by comparison against manual analysis (n = 100) and evaluation of the QC steps’ ability to detect erroneous results (n = 700). Next, this method was used to obtain reference values for cardiac function metrics from the UK Biobank. Results Automated analysis correlated highly with manual analysis for left and right ventricular volumes (all r > 0.95), strain (circumferential r = 0.89, longitudinal r > 0.89), and filling and ejection rates (all r ≥ 0.93). There was no significant bias for cardiac volumes and filling and ejection rates, except for right ventricular end-systolic volume (bias +1.80 ml; p = 0.01). The bias for FT strain was <1.3%. The sensitivity of detection of erroneous output was 95% for volume-derived parameters and 93% for FT strain. Finally, reference values were automatically derived from 2,029 CMR exams in healthy subjects. Conclusions The study demonstrates a DL-based framework for automated, quality-controlled characterization of cardiac function from cine CMR, without the need for direct clinician oversight.
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Affiliation(s)
- Bram Ruijsink
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Adult and Paediatric Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, London, United Kingdom.
| | - Esther Puyol-Antón
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Ilkay Oksuz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Matthew Sinclair
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Wenjia Bai
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Department of Medicine, Imperial College London, London, United Kingdom
| | - Julia A Schnabel
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Adult and Paediatric Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, London, United Kingdom
| | - Andrew P King
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Gilbert K, Bai W, Mauger C, Medrano-Gracia P, Suinesiaputra A, Lee AM, Sanghvi MM, Aung N, Piechnik SK, Neubauer S, Petersen SE, Rueckert D, Young AA. Independent Left Ventricular Morphometric Atlases Show Consistent Relationships with Cardiovascular Risk Factors: A UK Biobank Study. Sci Rep 2019; 9:1130. [PMID: 30718635 PMCID: PMC6362245 DOI: 10.1038/s41598-018-37916-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 12/17/2018] [Indexed: 01/08/2023] Open
Abstract
Left ventricular (LV) mass and volume are important indicators of clinical and pre-clinical disease processes. However, much of the shape information present in modern imaging examinations is currently ignored. Morphometric atlases enable precise quantification of shape and function, but there has been no objective comparison of different atlases in the same cohort. We compared two independent LV atlases using MRI scans of 4547 UK Biobank participants: (i) a volume atlas derived by automatic non-rigid registration of image volumes to a common template, and (ii) a surface atlas derived from manually drawn epicardial and endocardial surface contours. The strength of associations between atlas principal components and cardiovascular risk factors (smoking, diabetes, high blood pressure, high cholesterol and angina) were quantified with logistic regression models and five-fold cross validation, using area under the ROC curve (AUC) and Akaike Information Criterion (AIC) metrics. Both atlases exhibited similar principal components, showed similar relationships with risk factors, and had stronger associations (higher AUC and lower AIC) than a reference model based on LV mass and volume, for all risk factors (DeLong p < 0.05). Morphometric variations associated with each risk factor could be quantified and visualized and were similar between atlases. UK Biobank LV shape atlases are robust to construction method and show stronger relationships with cardiovascular risk factors than mass and volume.
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Affiliation(s)
- Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Wenjia Bai
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Charlene Mauger
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Pau Medrano-Gracia
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Aaron M Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Mihir M Sanghvi
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Stefan K Piechnik
- Oxford NIHR Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Stefan Neubauer
- Oxford NIHR Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.
- Department of Biomedical Engineering, King's College London, London, UK.
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Bieging ET, Morris A, Wilson BD, McGann CJ, Marrouche NF, Cates J. Left atrial shape predicts recurrence after atrial fibrillation catheter ablation. J Cardiovasc Electrophysiol 2018; 29:966-972. [PMID: 29846999 DOI: 10.1111/jce.13641] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 03/25/2018] [Accepted: 03/28/2018] [Indexed: 01/13/2023]
Abstract
INTRODUCTION Multiple markers left atrium (LA) remodeling, including LA shape, correlate with outcomes in atrial fibrillation (AF). Catheter ablation is an important treatment of AF, but better tools are needed to determine which patients will benefit. In this study, we use particle-based modeling to quantitatively assess LA shape, and determine to what degree it predicts AF recurrence after catheter ablation. METHODS AND RESULTS There were 254 patients enrolled in the DECAAF study who underwent cardiac magnetic resonance imaging of the LA prior to AF ablation and were followed for recurrence for up to 475 days. We performed particle-based shape modeling on each patient's LA shape. We selected shape parameters using the LASSO method and factor analysis, and then added them to a Cox regression model, which included multiple clinical parameters and LA fibrosis. We computed Harrell's C-statistic with and without shape in the model. We used the model to stratify patients into recurrence risk classes by both shape and shape and fibrosis combined. Three shape parameters were selected for inclusion. The C-statistic increased from 0.68 to 0.72 when shape was added to the model (P < 0.05). Visualized shapes showed that a more round LA shape with a shorter, more laterally rotated appendage was predictive of recurrence. CONCLUSION LA shape is an independent predictor of recurrence after AF ablation. When combined with LA fibrosis, shape analysis using PBM may improve patient selection for ablation.
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Affiliation(s)
- Erik T Bieging
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, UT, USA
| | - Alan Morris
- Comprehensive Arrhythmia Research and Management Center, University of Utah, Salt Lake City, UT, USA
| | - Brent D Wilson
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, UT, USA
| | | | - Nassir F Marrouche
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, UT, USA.,Comprehensive Arrhythmia Research and Management Center, University of Utah, Salt Lake City, UT, USA
| | - Joshua Cates
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
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Carminati M, Piazzese C, Pepi M, Tamborini G, Gripari P, Pontone G, Krause R, Auricchio A, Lang R, Caiani E. A statistical shape model of the left ventricle from real-time 3D echocardiography and its application to myocardial segmentation of cardiac magnetic resonance images. Comput Biol Med 2018; 96:241-251. [DOI: 10.1016/j.compbiomed.2018.03.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 03/21/2018] [Accepted: 03/21/2018] [Indexed: 10/17/2022]
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Nappi C, Gaudieri V, Acampa W, Assante R, Zampella E, Mainolfi CG, Petretta M, Germano G, Cuocolo A. Comparison of left ventricular shape by gated SPECT imaging in diabetic and nondiabetic patients with normal myocardial perfusion: A propensity score analysis. J Nucl Cardiol 2018; 25:394-403. [PMID: 28808939 DOI: 10.1007/s12350-017-1009-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 06/06/2017] [Indexed: 01/06/2023]
Abstract
BACKGROUND Diabetes mellitus induces structural and functional cardiac alterations that can result in heart failure. Left ventricular (LV) shape is a dynamic component of cardiac geometry influencing its contractile function. However, few data are available comparing LV shape index in diabetic and nondiabetic patients without overt coronary artery disease after balancing for coronary risk factors. METHODS We studied 1168 patients with normal myocardial perfusion and normal LV ejection fraction on stress gated single-photon emission computed tomography (SPECT) imaging. To account for differences in baseline characteristics between diabetic and nondiabetic patients, we created a propensity score-matched cohort considering clinical variables, coronary risk factors, and stress type. RESULTS Before matching, diabetic patients were older, had higher prevalence of male gender and coronary risk factors, and higher end-diastolic and end-systolic LV shape index. After matching, all clinical characteristics were comparable between diabetic and nondiabetic patients, but diabetic patients still had higher end-diastolic and end-systolic LV shape index (both P < .001). At multivariable linear regression analysis, diabetes was a strong predictor of end-systolic LV shape index in the overall study population and in the propensity-matched cohort. CONCLUSIONS Diabetic patients have higher values of LV shape index compared to nondiabetic patients also after balancing clinical characteristics by propensity score analysis. Shape indexes assessment by gated SPECT may be useful for identifying early LV remodeling in patients with diabetes.
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Affiliation(s)
- Carmela Nappi
- Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Valeria Gaudieri
- Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy
| | - Wanda Acampa
- Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy
- Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy
| | - Roberta Assante
- Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Emilia Zampella
- Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Ciro Gabriele Mainolfi
- Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Mario Petretta
- Department of Translational Medical Sciences, University Federico II, Naples, Italy
| | - Guido Germano
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy.
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Suinesiaputra A, Ablin P, Albà X, Alessandrini M, Allen J, Bai W, Çimen S, Claes P, Cowan BR, D’hooge J, Duchateau N, Ehrhardt J, Frangi AF, Gooya A, Grau V, Lekadir K, Lu A, Mukhopadhyay A, Oksuz I, Parajuli N, Pennec X, Pereañez M, Pinto C, Piras P, Rohé MM, Rueckert D, Säring D, Sermesant M, Siddiqi K, Tabassian M, Teresi L, Tsaftaris SA, Wilms M, Young AA, Zhang X, Medrano-Gracia P. Statistical shape modeling of the left ventricle: myocardial infarct classification challenge. IEEE J Biomed Health Inform 2018; 22:503-515. [PMID: 28103561 PMCID: PMC5857476 DOI: 10.1109/jbhi.2017.2652449] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to (1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and (2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website1.
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Affiliation(s)
- Avan Suinesiaputra
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Pierre Ablin
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Xènia Albà
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Martino Alessandrini
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Jack Allen
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Wenjia Bai
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Serkan Çimen
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Peter Claes
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Brett R. Cowan
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Jan D’hooge
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Nicolas Duchateau
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Jan Ehrhardt
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Alejandro F. Frangi
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Ali Gooya
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Vicente Grau
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Karim Lekadir
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Allen Lu
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Anirban Mukhopadhyay
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Ilkay Oksuz
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Nripesh Parajuli
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Xavier Pennec
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Marco Pereañez
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Catarina Pinto
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Paolo Piras
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Marc-Michel Rohé
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Daniel Rueckert
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Dennis Säring
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Maxime Sermesant
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Kaleem Siddiqi
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Mahdi Tabassian
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Luciano Teresi
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Sotirios A. Tsaftaris
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Matthias Wilms
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Alistair A. Young
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Xingyu Zhang
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
| | - Pau Medrano-Gracia
- AS, XZ, BRC, AAY and PM-G are with the Department of Anatomy and Medical Imaging, Auckland, New Zealand. WB and DR are with Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. AM is with Zuse Institute Berlin, Germany. IO and SAT are with IMT Institute for Advanced Studies Lucca, Italy. SAT is also with the University of Edinburgh, UK. JA and VG are with the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. PA and KS are with School of Computer Science and Centre for Intelligent Machines, McGill University. KL, MP, and XA are with Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain. SÇ, AG, CP and AFF are with the Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, UK. PP is with Department Structural Engineering & Geotechnics, Sapienza, Università di Roma, Italy. LT is with Dept. Mathematics & Physics, Roma Tre University, Italy. MT, MA and JD are with the Department of Cardiovascular Sciences, KU Leuven, Belgium. PC is with the Department of Electrical Engineering-ESAT, KU Leuven, Belgium. MT and MA are also with the Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. JE and MW are with the Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. DS is with the University of Applied Sciences Wedel, Wedel, Germany. NP and AL are with the Department of Electrical Engineering and Biomedical Engineering, Yale University, New Haven, CT, USA. M-MR, ND, MS and XP are with the Inria Sophia-Antipolis, Asclepios Research Group, France
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Wagner JL, Landeck BF, Hunter K. Quantification of Left Ventricular Shape Differentiates Pediatric Pulmonary Hypertension Subjects From Matched Controls. JOURNAL OF ENGINEERING AND SCIENCE IN MEDICAL DIAGNOSTICS AND THERAPY 2018; 1:0110071-110077. [PMID: 35832296 PMCID: PMC8597644 DOI: 10.1115/1.4038408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 11/05/2017] [Indexed: 06/15/2023]
Abstract
Changes in left ventricle (LV) shape are observed in patients with pulmonary hypertension (PH). Quantification of ventricular shape could serve as a tool to noninvasively monitor pediatric patients with PH. Decomposing the shape of a ventricle into a series of components and magnitudes will facilitate differentiation of healthy and PH subjects. Parasternal short-axis echo images acquired from 53 pediatric subjects with PH and 53 age and sex-matched normal control subjects underwent speckle tracking using Velocity Vector Imaging (Siemens) to produce a series of x,y coordinates tracing the LV endocardium in each frame. Coordinates were converted to polar format after which the Fourier transform was used to derive shape component magnitudes in each frame. Magnitudes of the first 11 components were normalized to heart size (magnitude/LV length as measured on apical view) and analyzed across a single cardiac cycle. Logistic regression was used to test predictive power of the method. Fourier decomposition produced a series of shape components from short-axis echo views of the LV. Mean values for all 11 components analyzed were significantly different between groups (p < 0.05). The accuracy index of the receiver operator curve was 0.85. Quantification of LV shape can differentiate normal pediatric subjects from those with PH. Shape analysis is a promising method to precisely describe shape changes observed in PH. Differences between groups speak to intraventricular coupling that occurs in right ventricular (RV) overload. Further analysis investigating the correlation of shape to clinical parameters is underway.
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Affiliation(s)
- Jennifer L Wagner
- Department of Bioengineering, University of Colorado, 12705 E. Montview Boulevard, Suite 100, Aurora, CO 80045 e-mail:
| | - Bruce F Landeck
- School of Medicine, University of Colorado, Aurora, CO 80045
| | - Kendall Hunter
- Department of Bioengineering, University of Colorado, Aurora, CO 80045
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Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease. J Cardiovasc Transl Res 2018; 11:123-132. [PMID: 29294215 DOI: 10.1007/s12265-017-9778-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 12/18/2017] [Indexed: 12/18/2022]
Abstract
Approximately 1% of all babies are born with some form of congenital heart defect. Many serious forms of CHD can now be surgically corrected after birth, which has led to improved survival into adulthood. However, many patients require serial monitoring to evaluate progression of heart failure and determine timing of interventions. Accurate multidimensional quantification of regional heart shape and function is required for characterizing these patients. A computational atlas of single ventricle and biventricular heart shape and function enables quantification of remodeling in terms of z scores in relation to specific reference populations. Progression of disease can then be monitored effectively by longitudinal evaluation of z scores. A biomechanical analysis of cardiac function in relation to population variation enables investigation of the underlying mechanisms for developing pathology. Here, we summarize recent progress in this field, with examples in single ventricle and biventricular congenital pathologies.
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Luo G, Dong S, Wang K, Zuo W, Cao S, Zhang H. Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images. IEEE Trans Biomed Eng 2017; 65:1924-1934. [PMID: 29035205 DOI: 10.1109/tbme.2017.2762762] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Left ventricular (LV) volume estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address a direct LV volume prediction task. METHODS In this paper, we propose a direct volume prediction method based on the end-to-end deep convolutional neural networks. We study the end-to-end LV volume prediction method in items of the data preprocessing, network structure, and multiview fusion strategy. The main contributions of this paper are the following aspects. First, we propose a new data preprocessing method on cardiac magnetic resonance (CMR). Second, we propose a new network structure for end-to-end LV volume estimation. Third, we explore the representational capacity of different slices and propose a fusion strategy to improve the prediction accuracy. RESULTS The evaluation results show that the proposed method outperforms other state-of-the-art LV volume estimation methods on the open accessible benchmark datasets. The clinical indexes derived from the predicted volumes agree well with the ground truth ( ${\rm{EDV:R}}^{{\rm 2}}={\text{0.974}}$, ${\rm{RMSE\,}}= {\text{9.6}}{\rm{\,ml}}$; ${\rm{ESV:R}}^{{\rm 2}}={\text{0.976}}$, ${\rm{RMSE}}= {\text{7.1}}\,{\text{ml}}$; ${\rm{EF:R}}^{{\rm 2}} ={\text{0.828}}$, ${\rm{RMSE}}= {\text{4.71}}\% $). CONCLUSION Experimental results prove that the proposed method may be useful for the LV volume prediction task. SIGNIFICANCE The proposed method not only has application potential for cardiac diseases screening for large-scale CMR data, but also can be extended to other medical image research fields.
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Morphologically normalized left ventricular motion indicators from MRI feature tracking characterize myocardial infarction. Sci Rep 2017; 7:12259. [PMID: 28947754 PMCID: PMC5612925 DOI: 10.1038/s41598-017-12539-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 09/08/2017] [Indexed: 01/12/2023] Open
Abstract
We characterized motion attributes arising from LV spatio-temporal analysis of motion distributions in myocardial infarction. Time-varying 3D finite element shape models were obtained in 300 Controls and 300 patients with myocardial infarction. Inter-individual left ventricular shape differences were eliminated using parallel transport to the grand mean of all cases. The first three principal component (PC) scores were used to characterize trajectory attributes. Scores were tested with ANOVA/MANOVA using patient disease status (Infarcts vs. Controls) as a factor. Infarcted patients had significantly different magnitude, orientation and shape of left ventricular trajectories in comparison to Controls. Significant differences were found for the angle between PC scores 1 and 2 in the endocardium, and PC scores 1 and 3 in the epicardium. The largest differences were found in the magnitude of endocardial motion. Endocardial PC scores in shape space showed the highest classification power using support vector machine, with higher total accuracy in comparison to previous methods. Shape space performed better than size-and-shape space for both epicardial and endocardial features. In conclusion, LV spatio-temporal motion attributes accurately characterize the presence of infarction. This approach is easily generalizable to different pathologies, enabling more precise study of the pathophysiological consequences of a wide spectrum of cardiac diseases.
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Zhang X, Medrano-Gracia P, Ambale-Venkatesh B, Bluemke DA, Cowan BR, Finn JP, Kadish AH, Lee DC, Lima JAC, Young AA, Suinesiaputra A. Orthogonal decomposition of left ventricular remodeling in myocardial infarction. Gigascience 2017; 6:1-15. [PMID: 28327972 PMCID: PMC5791439 DOI: 10.1093/gigascience/gix005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 01/29/2017] [Indexed: 01/19/2023] Open
Abstract
Left ventricular size and shape are important for quantifying cardiac remodeling in
response to cardiovascular disease. Geometric remodeling indices have
been shown to have prognostic value in predicting adverse events in the clinical
literature, but these often describe interrelated shape changes. We developed a novel
method for deriving orthogonal remodeling components directly from any
(moderately independent) set of clinical remodeling indices. Results: Six clinical
remodeling indices (end-diastolic volume index, sphericity, relative wall thickness,
ejection fraction, apical conicity, and longitudinal shortening) were evaluated using
cardiac magnetic resonance images of 300 patients with myocardial infarction, and 1991
asymptomatic subjects, obtained from the Cardiac Atlas Project. Partial least squares
(PLS) regression of left ventricular shape models resulted in remodeling
components that were optimally associated with each remodeling index. A
Gram–Schmidt orthogonalization process, by which remodeling components were successively
removed from the shape space in the order of shape variance explained, resulted in a set
of orthonormal remodeling components. Remodeling scores could then be
calculated that quantify the amount of each remodeling component present in each case. A
one-factor PLS regression led to more decoupling between scores from the different
remodeling components across the entire cohort, and zero correlation between clinical
indices and subsequent scores. Conclusions: The PLS orthogonal remodeling components had
similar power to describe differences between myocardial infarction patients and
asymptomatic subjects as principal component analysis, but were better associated with
well-understood clinical indices of cardiac remodeling. The data and analyses are
available from www.cardiacatlas.org.
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Affiliation(s)
- Xingyu Zhang
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Pau Medrano-Gracia
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Bharath Ambale-Venkatesh
- The Donald W. Reynolds Cardiovascular Clinical Research Center, The Johns Hopkins University, Baltimore, USA
| | - David A Bluemke
- National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland, USA
| | - Brett R Cowan
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - J Paul Finn
- Department of Radiology, UCLA, Los Angeles, USA
| | - Alan H Kadish
- Feinberg Cardiovascular Research Institute, Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Daniel C Lee
- Feinberg Cardiovascular Research Institute, Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Joao A C Lima
- The Donald W. Reynolds Cardiovascular Clinical Research Center, The Johns Hopkins University, Baltimore, USA
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
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Hydraulic forces contribute to left ventricular diastolic filling. Sci Rep 2017; 7:43505. [PMID: 28256604 PMCID: PMC5334655 DOI: 10.1038/srep43505] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 01/27/2017] [Indexed: 01/20/2023] Open
Abstract
Myocardial active relaxation and restoring forces are known determinants of left ventricular (LV) diastolic function. We hypothesize the existence of an additional mechanism involved in LV filling, namely, a hydraulic force contributing to the longitudinal motion of the atrioventricular (AV) plane. A prerequisite for the presence of a net hydraulic force during diastole is that the atrial short-axis area (ASA) is smaller than the ventricular short-axis area (VSA). We aimed (a) to illustrate this mechanism in an analogous physical model, (b) to measure the ASA and VSA throughout the cardiac cycle in healthy volunteers using cardiovascular magnetic resonance imaging, and (c) to calculate the magnitude of the hydraulic force. The physical model illustrated that the anatomical difference between ASA and VSA provides the basis for generating a hydraulic force during diastole. In volunteers, VSA was greater than ASA during 75-100% of diastole. The hydraulic force was estimated to be 10-60% of the peak driving force of LV filling (1-3 N vs 5-10 N). Hydraulic forces are a consequence of left heart anatomy and aid LV diastolic filling. These findings suggest that the relationship between ASA and VSA, and the associated hydraulic force, should be considered when characterizing diastolic function and dysfunction.
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Dawes TJW, de Marvao A, Shi W, Fletcher T, Watson GMJ, Wharton J, Rhodes CJ, Howard LSGE, Gibbs JSR, Rueckert D, Cook SA, Wilkins MR, O'Regan DP. Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study. Radiology 2017; 283:381-390. [PMID: 28092203 PMCID: PMC5398374 DOI: 10.1148/radiol.2016161315] [Citation(s) in RCA: 121] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Applying machine learning of complex motion phenotypes obtained from cardiac MR images allows more accurate prediction of patient outcomes in pulmonary hypertension. Purpose To determine if patient survival and mechanisms of right ventricular failure in pulmonary hypertension could be predicted by using supervised machine learning of three-dimensional patterns of systolic cardiac motion. Materials and Methods The study was approved by a research ethics committee, and participants gave written informed consent. Two hundred fifty-six patients (143 women; mean age ± standard deviation, 63 years ± 17) with newly diagnosed pulmonary hypertension underwent cardiac magnetic resonance (MR) imaging, right-sided heart catheterization, and 6-minute walk testing with a median follow-up of 4.0 years. Semiautomated segmentation of short-axis cine images was used to create a three-dimensional model of right ventricular motion. Supervised principal components analysis was used to identify patterns of systolic motion that were most strongly predictive of survival. Survival prediction was assessed by using difference in median survival time and area under the curve with time-dependent receiver operating characteristic analysis for 1-year survival. Results At the end of follow-up, 36% of patients (93 of 256) died, and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and free wall, coupled with reduced basal longitudinal motion. When added to conventional imaging and hemodynamic, functional, and clinical markers, three-dimensional cardiac motion improved survival prediction (area under the receiver operating characteristic curve, 0.73 vs 0.60, respectively; P < .001) and provided greater differentiation according to difference in median survival time between high- and low-risk groups (13.8 vs 10.7 years, respectively; P < .001). Conclusion A machine-learning survival model that uses three-dimensional cardiac motion predicts outcome independent of conventional risk factors in patients with newly diagnosed pulmonary hypertension. Online supplemental material is available for this article.
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Affiliation(s)
- Timothy J W Dawes
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
| | - Antonio de Marvao
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
| | - Wenzhe Shi
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
| | - Tristan Fletcher
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
| | - Geoffrey M J Watson
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
| | - John Wharton
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
| | - Christopher J Rhodes
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
| | - Luke S G E Howard
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
| | - J Simon R Gibbs
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
| | - Daniel Rueckert
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
| | - Stuart A Cook
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
| | - Martin R Wilkins
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
| | - Declan P O'Regan
- From the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.)
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Pennell DJ, Baksi AJ, Prasad SK, Mohiaddin RH, Alpendurada F, Babu-Narayan SV, Schneider JE, Firmin DN. Review of Journal of Cardiovascular Magnetic Resonance 2015. J Cardiovasc Magn Reson 2016; 18:86. [PMID: 27846914 PMCID: PMC5111217 DOI: 10.1186/s12968-016-0305-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 11/02/2016] [Indexed: 12/14/2022] Open
Abstract
There were 116 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2015, which is a 14 % increase on the 102 articles published in 2014. The quality of the submissions continues to increase. The 2015 JCMR Impact Factor (which is published in June 2016) rose to 5.75 from 4.72 for 2014 (as published in June 2015), which is the highest impact factor ever recorded for JCMR. The 2015 impact factor means that the JCMR papers that were published in 2013 and 2014 were cited on average 5.75 times in 2015. The impact factor undergoes natural variation according to citation rates of papers in the 2 years following publication, and is significantly influenced by highly cited papers such as official reports. However, the progress of the journal's impact over the last 5 years has been impressive. Our acceptance rate is <25 % and has been falling because the number of articles being submitted has been increasing. In accordance with Open-Access publishing, the JCMR articles go on-line as they are accepted with no collating of the articles into sections or special thematic issues. For this reason, the Editors have felt that it is useful once per calendar year to summarize the papers for the readership into broad areas of interest or theme, so that areas of interest can be reviewed in a single article in relation to each other and other recent JCMR articles. The papers are presented in broad themes and set in context with related literature and previously published JCMR papers to guide continuity of thought in the journal. We hope that you find the open-access system increases wider reading and citation of your papers, and that you will continue to send your quality papers to JCMR for publication.
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Affiliation(s)
- D. J. Pennell
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - A. J. Baksi
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - S. K. Prasad
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - R. H. Mohiaddin
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - F. Alpendurada
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - S. V. Babu-Narayan
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - J. E. Schneider
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - D. N. Firmin
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
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McLeod K, Wall S, Leren IS, Saberniak J, Haugaa KH. Ventricular structure in ARVC: going beyond volumes as a measure of risk. J Cardiovasc Magn Reson 2016; 18:73. [PMID: 27756409 PMCID: PMC5069945 DOI: 10.1186/s12968-016-0291-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 10/04/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Altered right ventricular structure is an important feature of Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC), but is challenging to quantify objectively. The aim of this study was to go beyond ventricular volumes and diameters and to explore if the shape of the right and left ventricles could be assessed and related to clinical measures. We used quantifiable computational methods to automatically identify and analyse malformations in ARVC patients from Cardiovascular Magnetic Resonance (CMR) images. Furthermore, we investigated how automatically extracted structural features were related to arrhythmic events. METHODS A retrospective cross-sectional feasibility study was performed on CMR short axis cine images of 27 ARVC patients and 21 ageing asymptomatic control subjects. All images were segmented at the end-diastolic (ED) and end-systolic (ES) phases of the cardiac cycle to create three-dimensional (3D) bi-ventricle shape models for each subject. The most common components to single- and bi-ventricular shape in the ARVC population were identified and compared to those obtained from the control group. The correlations were calculated between identified ARVC shapes and parameters from the 2010 Task Force Criteria, in addition to clinical outcomes such as ventricular arrhythmias. RESULTS Bi-ventricle shape for the ARVC population showed, as ordered by prevalence with the percent of total variance in the population explained by each shape: global dilation/shrinking of both ventricles (44 %), elongation/shortening at the right ventricle (RV) outflow tract (15 %), tilting at the septum (10 %), shortening/lengthening of both ventricles (7 %), and bulging/shortening at both the RV inflow and outflow (5 %). Bi-ventricle shapes were significantly correlated to several clinical diagnostic parameters and outcomes, including (but not limited to) correlations between global dilation and electrocardiography (ECG) major criteria (p = 0.002), and base-to-apex lengthening and history of arrhythmias (p = 0.003). Classification of ARVC vs. control using shape modes yielded high sensitivity (96 %) and moderate specificity (81 %). CONCLUSION We presented for the first time an automatic method for quantifying and analysing ventricular shapes in ARVC patients from CMR images. Specific ventricular shape features were highly correlated with diagnostic indices in ARVC patients and yielded high classification sensitivity. Ventricular shape analysis may be a novel approach to classify ARVC disease, and may be used in diagnosis and in risk stratification for ventricular arrhythmias.
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Affiliation(s)
- Kristin McLeod
- Cardiac Modelling Department, Simula Research Laboratory, PO Box 134, Oslo, Norway
- Center for Cardiological Innovation, Oslo, Norway
| | - Samuel Wall
- Cardiac Modelling Department, Simula Research Laboratory, PO Box 134, Oslo, Norway
- Center for Cardiological Innovation, Oslo, Norway
| | - Ida Skrinde Leren
- Department of Cardiology and Institute for Surgical Research, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- University of Oslo, Oslo, Norway
- Center for Cardiological Innovation, Oslo, Norway
| | - Jørg Saberniak
- Department of Cardiology and Institute for Surgical Research, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- University of Oslo, Oslo, Norway
- Center for Cardiological Innovation, Oslo, Norway
| | - Kristina Hermann Haugaa
- Department of Cardiology and Institute for Surgical Research, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- University of Oslo, Oslo, Norway
- Center for Cardiological Innovation, Oslo, Norway
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Ambale-Venkatesh B, Yoneyama K, Sharma RK, Ohyama Y, Wu CO, Burke GL, Shea S, Gomes AS, Young AA, Bluemke DA, Lima JA. Left ventricular shape predicts different types of cardiovascular events in the general population. Heart 2016; 103:499-507. [PMID: 27694110 DOI: 10.1136/heartjnl-2016-310052] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Revised: 08/31/2016] [Accepted: 09/05/2016] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE To investigate whether sphericity volume index (SVI), an indicator of left ventricular (LV) remodelling, predicts incident cardiovascular events (coronary heart disease, CHD; all cardiovascular disease, CVD; heart failure, HF; atrial fibrillation, AF) over 10 years of follow-up in a multiethnic population (Multi-Ethnic Study of Atherosclerosis). METHODS 5004 participants free of known CVD had magnetic resonance imaging (MRI) in 2000-2002. Cine images were analysed to compute, [Formula: see text] equivalent to LV volume/volume of sphere with length of LV as the diameter. The highest (greatest sphericity) and lowest (lowest sphericity) quintiles of SVI were compared against the reference group (2-4 quintiles combined). Risk-factor adjusted hazard's ratio (HR) from Cox regression assessed the predictive performance of SVI at end-diastole (ED) and end-systole (ES) to predict incident outcomes over 10 years in retrospective interpretation of prospective data. RESULTS At baseline, participants were aged 61±10 years; 52% men and 39%/13%/26%/22% Cauc/Chinese/Afr-Amer/Hispanic. Low sphericity was associated with higher Framingham CVD risk, greater coronary calcium score and higher N-terminal pro-brain natriuretic peptide (NT-proBNP); while increased sphericity was associated with higher NT-proBNP and lower ejection fraction. Low sphericity predicted incident CHD (HR: 1.48, 1.55-2.59 at ED) and CVD (HR: 1.82, 1.47-2.27 at ED). However, both low (HR: 1.81, 1.20-2.73 at ES) and high (HR: 2.21, 1.41-3.46 at ES) sphericity predicted incident HF. High sphericity also predicted AF. CONCLUSIONS In a multiethnic population free of CVD at baseline, lowest sphericity was a predictor of incident CHD, CVD and HF over a 10-year follow-up period. Extreme sphericity was a strong predictor of incident HF and AF. SVI improved risk prediction models beyond established risk factors only for HF, but not for all CVD or CHD.
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Affiliation(s)
| | | | | | | | - Colin O Wu
- National Institutes of Health, Bethesda, Maryland, USA
| | - Gregory L Burke
- Wake Forest University Health Sciences, Winston-Salem, North Carolina, USA
| | - Steven Shea
- Columbia University, New York, New York, USA
| | | | | | | | - João Ac Lima
- Johns Hopkins University, Baltimore, Maryland, USA
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Medrano-Gracia P, Ormiston J, Webster M, Beier S, Young A, Ellis C, Wang C, Smedby Ö, Cowan B. A computational atlas of normal coronary artery anatomy. EUROINTERVENTION 2016; 12:845-54. [DOI: 10.4244/eijv12i7a139] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Farrar G, Suinesiaputra A, Gilbert K, Perry JC, Hegde S, Marsden A, Young AA, Omens JH, McCulloch AD. Atlas-Based Ventricular Shape Analysis for Understanding Congenital Heart Disease. PROGRESS IN PEDIATRIC CARDIOLOGY 2016; 43:61-69. [PMID: 28082823 DOI: 10.1016/j.ppedcard.2016.07.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Congenital heart disease is associated with abnormal ventricular shape that can affect wall mechanics and may be predictive of long-term adverse outcomes. Atlas-based parametric shape analysis was used to analyze ventricular geometries of eight adolescent or adult single-ventricle CHD patients with tricuspid atresia and Fontans. These patients were compared with an "atlas" of non-congenital asymptomatic volunteers, resulting in a set of z-scores which quantify deviations from the control population distribution on a patient-by-patient basis. We examined the potential of these scores to: (1) quantify abnormalities of ventricular geometry in single ventricle physiologies relative to the normal population; (2) comprehensively quantify wall motion in CHD patients; and (3) identify possible relationships between ventricular shape and wall motion that may reflect underlying functional defects or remodeling in CHD patients. CHD ventricular geometries at end-diastole and end-systole were individually compared with statistical shape properties of an asymptomatic population from the Cardiac Atlas Project. Shape analysis-derived model properties, and myocardial wall motions between end-diastole and end-systole, were compared with physician observations of clinical functional parameters. Relationships between altered shape and altered function were evaluated via correlations between atlas-based shape and wall motion scores. Atlas-based shape analysis identified a diverse set of specific quantifiable abnormalities in ventricular geometry or myocardial wall motion in all subjects. Moreover, this initial cohort displayed significant relationships between specific shape abnormalities such as increased ventricular sphericity and functional defects in myocardial deformation, such as decreased long-axis wall motion. These findings suggest that atlas-based ventricular shape analysis may be a useful new tool in the management of patients with CHD who are at risk of impaired ventricular wall mechanics and chamber remodeling.
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Affiliation(s)
- Genevieve Farrar
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, NZ
| | - Kathleen Gilbert
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, NZ
| | - James C Perry
- Division of Cardiology, Rady Children's Hospital, San Diego, CA, USA; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Sanjeet Hegde
- Division of Cardiology, Rady Children's Hospital, San Diego, CA, USA; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Alison Marsden
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, NZ
| | - Jeffrey H Omens
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA; Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Andrew D McCulloch
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA; Department of Medicine, University of California San Diego, La Jolla, CA, USA
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Captur G, Manisty C, Moon JC. Cardiac MRI evaluation of myocardial disease. Heart 2016; 102:1429-35. [PMID: 27354273 DOI: 10.1136/heartjnl-2015-309077] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Accepted: 04/28/2016] [Indexed: 01/15/2023] Open
Abstract
Cardiovascular magnetic resonance (CMR) is a key imaging technique for cardiac phenotyping with a major clinical role. It can assess advanced aspects of cardiac structure and function, scar burden and other myocardial tissue characteristics but there is new information that can now be derived. This can fill many of the gaps in our knowledge with the potential to change thinking, disease classifications and definitions as well as patient care. Established techniques such as the late gadolinium enhancement technique are now embedded in clinical care. New techniques are coming through. Myocardial tissue characterisation techniques, particularly myocardial mapping can precisely measure tissue magnetisation-T1, T2, T2* and also the extracellular volume. These change in disease. Key biological pathways are now open for scrutiny including focal fibrosis (scar) and diffuse fibrosis, inflammation, metabolism and infiltration. Other new areas to engage in where major insights are growing include detailed assessments of myocardial mechanics and performance, spectroscopy and hyperpolarised CMR. In spite of the advances, challenges remain, particularly surrounding utilisation, technical development to improve accuracy, reproducibility and deliverability, and the role of multidisciplinary research to understand the detailed pathological basis of the MR signal changes. Collectively, these new developments are galvanising CMR uptake and having a major translational impact on healthcare globally and it is steadily becoming key imaging tool.
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Affiliation(s)
- Gabriella Captur
- UCL Biological Mass Spectrometry Laboratory, Institute of Child Health and Great Ormond Street Hospital, London, UK NIHR University College London Hospitals Biomedical Research Centre, Maple House Suite A, 149 Tottenham Court Road, London, UK
| | - Charlotte Manisty
- UCL Institute of Cardiovascular Science, University College London, London, UK The Cardiovascular Magnetic Resonance Imaging Unit and The Center for Rare Cardiovascular Diseases Unit, Barts Heart Center, St Bartholomew's Hospital, London, UK
| | - James C Moon
- NIHR University College London Hospitals Biomedical Research Centre, Maple House Suite A, 149 Tottenham Court Road, London, UK UCL Institute of Cardiovascular Science, University College London, London, UK The Cardiovascular Magnetic Resonance Imaging Unit and The Center for Rare Cardiovascular Diseases Unit, Barts Heart Center, St Bartholomew's Hospital, London, UK
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Suinesiaputra A, McCulloch AD, Nash MP, Pontre B, Young AA. Cardiac image modelling: Breadth and depth in heart disease. Med Image Anal 2016; 33:38-43. [PMID: 27349830 DOI: 10.1016/j.media.2016.06.027] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Revised: 06/16/2016] [Accepted: 06/16/2016] [Indexed: 01/09/2023]
Abstract
With the advent of large-scale imaging studies and big health data, and the corresponding growth in analytics, machine learning and computational image analysis methods, there are now exciting opportunities for deepening our understanding of the mechanisms and characteristics of heart disease. Two emerging fields are computational analysis of cardiac remodelling (shape and motion changes due to disease) and computational analysis of physiology and mechanics to estimate biophysical properties from non-invasive imaging. Many large cohort studies now underway around the world have been specifically designed based on non-invasive imaging technologies in order to gain new information about the development of heart disease from asymptomatic to clinical manifestations. These give an unprecedented breadth to the quantification of population variation and disease development. Also, for the individual patient, it is now possible to determine biophysical properties of myocardial tissue in health and disease by interpreting detailed imaging data using computational modelling. For these population and patient-specific computational modelling methods to develop further, we need open benchmarks for algorithm comparison and validation, open sharing of data and algorithms, and demonstration of clinical efficacy in patient management and care. The combination of population and patient-specific modelling will give new insights into the mechanisms of cardiac disease, in particular the development of heart failure, congenital heart disease, myocardial infarction, contractile dysfunction and diastolic dysfunction.
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Affiliation(s)
- Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | | | - Martyn P Nash
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand; Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Beau Pontre
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand; Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
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