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Backhaus SJ, Nasopoulou A, Lange T, Schulz A, Evertz R, Kowallick JT, Hasenfuß G, Lamata P, Schuster A. Left Atrial Roof Enlargement Is a Distinct Feature of Heart Failure With Preserved Ejection Fraction. Circ Cardiovasc Imaging 2024; 17:e016424. [PMID: 39012942 PMCID: PMC11251503 DOI: 10.1161/circimaging.123.016424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 05/29/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND It remains unknown to what extent intrinsic atrial cardiomyopathy or left ventricular diastolic dysfunction drive atrial remodeling and functional failure in heart failure with preserved ejection fraction (HFpEF). Computational 3-dimensional (3D) models fitted to cardiovascular magnetic resonance allow state-of-the-art anatomic and functional assessment, and we hypothesized to identify a phenotype linked to HFpEF. METHODS Patients with exertional dyspnea and diastolic dysfunction on echocardiography (E/e', >8) were prospectively recruited and classified as HFpEF or noncardiac dyspnea based on right heart catheterization. All patients underwent rest and exercise-stress right heart catheterization and cardiovascular magnetic resonance. Computational 3D anatomic left atrial (LA) models were generated based on short-axis cine sequences. A fully automated pipeline was developed to segment cardiovascular magnetic resonance images and build 3D statistical models of LA shape and find the 3D patterns discriminant between HFpEF and noncardiac dyspnea. In addition, atrial morphology and function were quantified by conventional volumetric analyses and deformation imaging. A clinical follow-up was conducted after 24 months for the evaluation of cardiovascular hospitalization. RESULTS Beyond atrial size, the 3D LA models revealed roof dilation as the main feature found in masked HFpEF (diagnosed during exercise-stress only) preceding a pattern shift to overall atrial size in overt HFpEF (diagnosed at rest). Characteristics of the 3D model were integrated into the LA HFpEF shape score, a biomarker to characterize the gradual remodeling between noncardiac dyspnea and HFpEF. The LA HFpEF shape score was able to discriminate HFpEF (n=34) to noncardiac dyspnea (n=34; area under the curve, 0.81) and was associated with a risk for atrial fibrillation occurrence (hazard ratio, 1.02 [95% CI, 1.01-1.04]; P=0.003), as well as cardiovascular hospitalization (hazard ratio, 1.02 [95% CI, 1.00-1.04]; P=0.043). CONCLUSIONS LA roof dilation is an early remodeling pattern in masked HFpEF advancing to overall LA enlargement in overt HFpEF. These distinct features predict the occurrence of atrial fibrillation and cardiovascular hospitalization. REGISTRATION URL: https://www.clinicaltrials.gov; Unique identifier: NCT03260621.
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Affiliation(s)
- Sören J. Backhaus
- Department of Cardiology, Campus Kerckhoff of the Justus-Liebig-University Giessen, Kerckhoff-Clinic, Bad Nauheim, Germany (S.J.B.)
- German Center for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Bad Nauheim, Germany (S.J.B.)
| | - Anastasia Nasopoulou
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, United Kingdom (A.N., P.L.)
| | - Torben Lange
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Germany (T.L., A. Schulz, R.E., G.H., A. Schuster)
- DZHK, Partner Site Lower Saxony, Germany (T.L., A. Schulz, R.E., J.T.K., G.H., A. Schuster)
| | - Alexander Schulz
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Germany (T.L., A. Schulz, R.E., G.H., A. Schuster)
- DZHK, Partner Site Lower Saxony, Germany (T.L., A. Schulz, R.E., J.T.K., G.H., A. Schuster)
| | - Ruben Evertz
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Germany (T.L., A. Schulz, R.E., G.H., A. Schuster)
- DZHK, Partner Site Lower Saxony, Germany (T.L., A. Schulz, R.E., J.T.K., G.H., A. Schuster)
| | - Johannes T. Kowallick
- DZHK, Partner Site Lower Saxony, Germany (T.L., A. Schulz, R.E., J.T.K., G.H., A. Schuster)
- FORUM Radiology, Rosdorf, Germany (J.T.K.)
| | - Gerd Hasenfuß
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Germany (T.L., A. Schulz, R.E., G.H., A. Schuster)
- DZHK, Partner Site Lower Saxony, Germany (T.L., A. Schulz, R.E., J.T.K., G.H., A. Schuster)
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, United Kingdom (A.N., P.L.)
| | - Andreas Schuster
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Germany (T.L., A. Schulz, R.E., G.H., A. Schuster)
- DZHK, Partner Site Lower Saxony, Germany (T.L., A. Schulz, R.E., J.T.K., G.H., A. Schuster)
- FORUM Cardiology, Rosdorf, Germany (A. Schuster)
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (A. Schuster)
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Beetz M, Banerjee A, Ossenberg-Engels J, Grau V. Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images. Med Image Anal 2023; 90:102975. [PMID: 37804586 DOI: 10.1016/j.media.2023.102975] [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: 09/13/2022] [Revised: 07/08/2023] [Accepted: 09/18/2023] [Indexed: 10/09/2023]
Abstract
Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI acquisitions. Its key component is a multi-class point cloud completion network (PCCN) capable of correcting both the sparsity and misalignment issues of the 3D reconstruction task in a unified model. We first evaluate the PCCN on a large synthetic dataset of biventricular anatomies and observe Chamfer distances between reconstructed and gold standard anatomies below or similar to the underlying image resolution for multiple levels of slice misalignment. Furthermore, we find a reduction in reconstruction error compared to a benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean surface distance, respectively. We then apply the PCCN as part of our automated reconstruction pipeline to 1000 subjects from the UK Biobank study in a cross-domain transfer setting and demonstrate its ability to reconstruct accurate and topologically plausible biventricular heart meshes with clinical metrics comparable to the previous literature. Finally, we investigate the robustness of our proposed approach and observe its capacity to successfully handle multiple common outlier conditions.
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Affiliation(s)
- Marcel Beetz
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK.
| | - Abhirup Banerjee
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK; Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK.
| | - Julius Ossenberg-Engels
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
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Sander J, de Vos BD, Bruns S, Planken N, Viergever MA, Leiner T, Išgum I. Reconstruction and completion of high-resolution 3D cardiac shapes using anisotropic CMRI segmentations and continuous implicit neural representations. Comput Biol Med 2023; 164:107266. [PMID: 37494823 DOI: 10.1016/j.compbiomed.2023.107266] [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] [Received: 04/10/2023] [Revised: 06/26/2023] [Accepted: 07/16/2023] [Indexed: 07/28/2023]
Abstract
Since the onset of computer-aided diagnosis in medical imaging, voxel-based segmentation has emerged as the primary methodology for automatic analysis of left ventricle (LV) function and morphology in cardiac magnetic resonance images (CMRI). In standard clinical practice, simultaneous multi-slice 2D cine short-axis MR imaging is performed under multiple breath-holds resulting in highly anisotropic 3D images. Furthermore, sparse-view CMRI often lacks whole heart coverage caused by large slice thickness and often suffers from inter-slice misalignment induced by respiratory motion. Therefore, these volumes only provide limited information about the true 3D cardiac anatomy which may hamper highly accurate assessment of functional and anatomical abnormalities. To address this, we propose a method that learns a continuous implicit function representing 3D LV shapes by training an auto-decoder. For training, high-resolution segmentations from cardiac CT angiography are used. The ability of our approach to reconstruct and complete high-resolution shapes from manually or automatically obtained sparse-view cardiac shape information is evaluated by using paired high- and low-resolution CMRI LV segmentations. The results show that the reconstructed LV shapes have an unconstrained subvoxel resolution and appear smooth and plausible in through-plane direction. Furthermore, Bland-Altman analysis reveals that reconstructed high-resolution ventricle volumes are closer to the corresponding reference volumes than reference low-resolution volumes with bias of [limits of agreement] -3.51 [-18.87, 11.85] mL, and 12.96 [-10.01, 35.92] mL respectively. Finally, the results demonstrate that the proposed approach allows recovering missing shape information and can indirectly correct for limited motion-induced artifacts.
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Affiliation(s)
- Jörg Sander
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center location University of Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.
| | - Bob D de Vos
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center location University of Amsterdam, The Netherlands
| | - Steffen Bruns
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center location University of Amsterdam, The Netherlands
| | - Nils Planken
- Department of Radiology and Nuclear Medicine,Amsterdam University Medical Center location University of Amsterdam, Amsterdam, The Netherlands
| | - Max A Viergever
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Tim Leiner
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center location University of Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands; Department of Radiology and Nuclear Medicine,Amsterdam University Medical Center location University of Amsterdam, Amsterdam, The Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
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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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Ghafarinatanzi M, Perie D. Estimation of anisotropic properties of CMR patient-specific left ventricle using the virtual field method. Biomech Model Mechanobiol 2023; 22:695-710. [PMID: 36692846 DOI: 10.1007/s10237-022-01675-1] [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: 05/30/2022] [Accepted: 12/08/2022] [Indexed: 01/25/2023]
Abstract
Left ventricle (LV) myocardial dysfunction has been recently investigated using the estimation of isotropic myocardial stiffness from magnetic resonance imaging (MRI). However, Myocardium is known to have a 3D complex geometry with anisotropic stiffness. The assessment of the anisotropy properties characterizes structural changes in myocardium as a consequence of heart failure (HF). From image data, the virtual field method (VFM) can determine material stiffness in a non-invasive manner. In the present work, the objective is to compare two inverse identification methods, given the isotropic and anisotropic models in the characterization of properties of myocardium in acute lymphoblastic leukemia (ALL) survivors using VFM and MRI. Two types of VFM approach are presented. Using the first, the virtual displacements (VFs) allow whole-field LV to be imposed into VFM formulation and caused to directly estimate two independent parameters from isotropic constitutive relation. With the second, anisotropic parameters are estimated using piece-wise (Finite element-based) VFM. The resulting values showed significant differences between the subjects in comparative study of leukemia survivors, and variance in estimated parameters by two different VFM approach. This approach would be an efficient tool to characterize early cardiac dysfunction. This work elucidates the benefits and shortcomings of using VFM to determine anisotropic parameters of LV myocardium in linear elastic and of using the FEM application to generate meshes of patient-specific LVs from MRI images.
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Affiliation(s)
- Mehdi Ghafarinatanzi
- Department of Mechanical Engineering, Polytechnique Montreal, Station Centre-Ville, P.O. Box 6079, Montréal, QC, H3C 3A7, Canada. .,Sainte-Justine University Health Center, Research Center, Montreal, Canada.
| | - Delphine Perie
- Department of Mechanical Engineering, Polytechnique Montreal, Station Centre-Ville, P.O. Box 6079, Montréal, QC, H3C 3A7, Canada.,Sainte-Justine University Health Center, Research Center, Montreal, Canada
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6
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Beetz M, Corral Acero J, Banerjee A, Eitel I, Zacur E, Lange T, Stiermaier T, Evertz R, Backhaus SJ, Thiele H, Bueno-Orovio A, Lamata P, Schuster A, Grau V. Interpretable cardiac anatomy modeling using variational mesh autoencoders. Front Cardiovasc Med 2022; 9:983868. [PMID: 36620629 PMCID: PMC9813669 DOI: 10.3389/fcvm.2022.983868] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022] Open
Abstract
Cardiac anatomy and function vary considerably across the human population with important implications for clinical diagnosis and treatment planning. Consequently, many computer-based approaches have been developed to capture this variability for a wide range of applications, including explainable cardiac disease detection and prediction, dimensionality reduction, cardiac shape analysis, and the generation of virtual heart populations. In this work, we propose a variational mesh autoencoder (mesh VAE) as a novel geometric deep learning approach to model such population-wide variations in cardiac shapes. It embeds multi-scale graph convolutions and mesh pooling layers in a hierarchical VAE framework to enable direct processing of surface mesh representations of the cardiac anatomy in an efficient manner. The proposed mesh VAE achieves low reconstruction errors on a dataset of 3D cardiac meshes from over 1,000 patients with acute myocardial infarction, with mean surface distances between input and reconstructed meshes below the underlying image resolution. We also find that it outperforms a voxelgrid-based deep learning benchmark in terms of both mean surface distance and Hausdorff distance while requiring considerably less memory. Furthermore, we explore the quality and interpretability of the mesh VAE's latent space and showcase its ability to improve the prediction of major adverse cardiac events over a clinical benchmark. Finally, we investigate the method's ability to generate realistic virtual populations of cardiac anatomies and find good alignment between the synthesized and gold standard mesh populations in terms of multiple clinical metrics.
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Affiliation(s)
- Marcel Beetz
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Jorge Corral Acero
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Abhirup Banerjee
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Ingo Eitel
- University Heart Center Lübeck, Medical Clinic II, Cardiology, Angiology, and Intensive Care Medicine, Lübeck, Germany
- University Hospital Schleswig-Holstein, Lübeck, Germany
- German Centre for Cardiovascular Research, Partner Site Lübeck, Lübeck, Germany
| | - Ernesto Zacur
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Torben Lange
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Göttingen, Germany
| | - Thomas Stiermaier
- University Heart Center Lübeck, Medical Clinic II, Cardiology, Angiology, and Intensive Care Medicine, Lübeck, Germany
- University Hospital Schleswig-Holstein, Lübeck, Germany
- German Centre for Cardiovascular Research, Partner Site Lübeck, Lübeck, Germany
| | - Ruben Evertz
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Göttingen, Germany
| | - Sören J. Backhaus
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Göttingen, Germany
| | - Holger Thiele
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
- Leipzig Heart Institute, Leipzig, Germany
| | | | - Pablo Lamata
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Andreas Schuster
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Göttingen, Germany
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
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Kim WJC, Beqiri A, Lewandowski AJ, Puyol-Antón E, Markham DC, King AP, Leeson P, Lamata P. Beyond Simpson's Rule: Accounting for Orientation and Ellipticity Assumptions. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:2476-2485. [PMID: 36137846 PMCID: PMC9810537 DOI: 10.1016/j.ultrasmedbio.2022.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 06/21/2022] [Accepted: 07/24/2022] [Indexed: 06/16/2023]
Abstract
Simpson's biplane rule (SBR) is considered the gold standard method for left ventricle (LV) volume quantification from echocardiography but relies on a summation-of-disks approach that makes assumptions about LV orientation and cross-sectional shape. We aim to identify key limiting factors in SBR and to develop a new robust standard for volume quantification. Three methods for computing LV volume were studied: (i) SBR, (ii) addition of a truncated basal cone (TBC) to SBR and (iii) a novel method of basal-oriented disks (BODs). Three retrospective cohorts representative of the young, adult healthy and heart failure populations were used to study the impact of anatomical variations in volume computations. Results reveal how basal slanting can cause over- and underestimation of volume, with errors by SBR and TBC >10 mL for slanting angles >6°. Only the BOD method correctly accounted for basal slanting, reducing relative volume errors by SBR from -2.23 ± 2.21% to -0.70 ± 1.91% in the adult population and similar qualitative performance in the other two cohorts. In conclusion, the summation of basal oriented disks, a novel interpretation of SBR, is a more accurate and precise method for estimating LV volume.
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Affiliation(s)
- Woo-Jin Cho Kim
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Arian Beqiri
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Ultromics Ltd, Oxford, UK
| | - Adam J Lewandowski
- Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, University of Oxford, Oxford, UK
| | - Esther Puyol-Antón
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | - Andrew P King
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Paul Leeson
- Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, University of Oxford, Oxford, UK
| | - Pablo Lamata
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
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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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Corral Acero J, Schuster A, Zacur E, Lange T, Stiermaier T, Backhaus SJ, Thiele H, Bueno-Orovio A, Lamata P, Eitel I, Grau V. Understanding and Improving Risk Assessment After Myocardial Infarction Using Automated Left Ventricular Shape Analysis. JACC Cardiovasc Imaging 2022; 15:1563-1574. [PMID: 35033494 PMCID: PMC9444994 DOI: 10.1016/j.jcmg.2021.11.027] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 11/29/2021] [Accepted: 11/30/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Left ventricular ejection fraction (LVEF) and end-systolic volume (ESV) remain the main imaging biomarkers for post-acute myocardial infarction (AMI) risk stratification. However, they are limited to global systolic function and fail to capture functional and anatomical regional abnormalities, hindering their performance in risk stratification. OBJECTIVES This study aimed to identify novel 3-dimensional (3D) imaging end-systolic (ES) shape and contraction descriptors toward risk-related features and superior prognosis in AMI. METHODS A multicenter cohort of AMI survivors (n = 1,021; median age 63 years; 74.5% male) who underwent cardiac magnetic resonance (CMR) at a median of 3 days after infarction were considered for this study. The clinical endpoint was the 12-month rate of major adverse cardiac events (MACE; n = 73), consisting of all-cause death, reinfarction, and new congestive heart failure. A fully automated pipeline was developed to segment CMR images, build 3D statistical models of shape and contraction in AMI, and find the 3D patterns related to MACE occurrence. RESULTS The novel ES shape markers proved to be superior to ESV (median cross-validated area under the receiver-operating characteristic curve 0.681 [IQR: 0.679-0.684] vs 0.600 [IQR: 0.598-0.602]; P < 0.001); and 3D contraction to LVEF (0.716 [IQR: 0.714-0.718] vs 0.681 [IQR: 0.679-0.684]; P < 0.001) in MACE occurrence prediction. They also contributed to a significant improvement in a multivariable setting including CMR markers, cardiovascular risk factors, and basic patient characteristics (0.747 [IQR: 0.745-0.749]; P < 0.001). Based on these novel 3D descriptors, 3 impairments caused by AMI were identified: global, anterior, and basal, the latter being the most complementary signature to already known predictors. CONCLUSIONS The quantification of 3D differences in ES shape and contraction, enabled by a fully automated pipeline, improves post-AMI risk prediction and identifies shape and contraction patterns related to MACE occurrence.
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Affiliation(s)
- Jorge Corral Acero
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
| | - Andreas Schuster
- University Medical Center Göttingen, Department of Cardiology and Pneumology, Georg-August University, Göttingen, Germany; German Centre for Cardiovascular Research, Göttingen, Germany
| | - Ernesto Zacur
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Torben Lange
- University Medical Center Göttingen, Department of Cardiology and Pneumology, Georg-August University, Göttingen, Germany; German Centre for Cardiovascular Research, Göttingen, Germany
| | - Thomas Stiermaier
- University Heart Center Lübeck, Medical Clinic II, Cardiology, Angiology, and Intensive Care Medicine, Lübeck, Germany; University Hospital Schleswig-Holstein, Lübeck, Germany; German Centre for Cardiovascular Research, Lübeck, Germany
| | - Sören J Backhaus
- University Medical Center Göttingen, Department of Cardiology and Pneumology, Georg-August University, Göttingen, Germany; German Centre for Cardiovascular Research, Göttingen, Germany
| | - Holger Thiele
- Heart Center Leipzig at University of Leipzig, Department of Internal Medicine and Cardiology, Leipzig, Germany; Leipzig Heart Institute, Leipzig, Germany
| | | | - Pablo Lamata
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Ingo Eitel
- University Heart Center Lübeck, Medical Clinic II, Cardiology, Angiology, and Intensive Care Medicine, Lübeck, Germany; University Hospital Schleswig-Holstein, Lübeck, Germany; German Centre for Cardiovascular Research, Lübeck, Germany
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
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10
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Mîra A, Lamata P, Pushparajah K, Abraham G, Mauger CA, McCulloch AD, Omens JH, Bissell MM, Blair Z, Huffaker T, Tandon A, Engelhardt S, Koehler S, Pickardt T, Beerbaum P, Sarikouch S, Latus H, Greil G, Young AA, Hussain T. Le Cœur en Sabot: shape associations with adverse events in repaired tetralogy of Fallot. J Cardiovasc Magn Reson 2022; 24:46. [PMID: 35922806 PMCID: PMC9351245 DOI: 10.1186/s12968-022-00877-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Maladaptive remodelling mechanisms occur in patients with repaired tetralogy of Fallot (rToF) resulting in a cycle of metabolic and structural changes. Biventricular shape analysis may indicate mechanisms associated with adverse events independent of pulmonary regurgitant volume index (PRVI). We aimed to determine novel remodelling patterns associated with adverse events in patients with rToF using shape and function analysis. METHODS Biventricular shape and function were studied in 192 patients with rToF (median time from TOF repair to baseline evaluation 13.5 years). Linear discriminant analysis (LDA) and principal component analysis (PCA) were used to identify shape differences between patients with and without adverse events. Adverse events included death, arrhythmias, and cardiac arrest with median follow-up of 10 years. RESULTS LDA and PCA showed that shape characteristics pertaining to adverse events included a more circular left ventricle (LV) (decreased eccentricity), dilated (increased sphericity) LV base, increased right ventricular (RV) apical sphericity, and decreased RV basal sphericity. Multivariate LDA showed that the optimal discriminative model included only RV apical ejection fraction and one PCA mode associated with a more circular and dilated LV base (AUC = 0.77). PRVI did not add value, and shape changes associated with increased PRVI were not predictive of adverse outcomes. CONCLUSION Pathological remodelling patterns in patients with rToF are significantly associated with adverse events, independent of PRVI. Mechanisms related to incident events include LV basal dilation with a reduced RV apical ejection fraction.
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Affiliation(s)
- Anna Mîra
- Department of Biomedical Engineering, King's College London, 1 Lambeth Palace Road, London, SE1 7EU, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, King's College London, 1 Lambeth Palace Road, London, SE1 7EU, UK
| | - Kuberan Pushparajah
- Department of Biomedical Engineering, King's College London, 1 Lambeth Palace Road, London, SE1 7EU, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - Georgina Abraham
- Department of Biomedical Engineering, King's College London, 1 Lambeth Palace Road, London, SE1 7EU, UK
| | - Charlène A Mauger
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Andrew D McCulloch
- Department of Bioengineering, University of California San Diego, San Diego, CA, USA
- Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - Jeffrey H Omens
- Department of Bioengineering, University of California San Diego, San Diego, CA, USA
- Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - Malenka M Bissell
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, England
| | - Zach Blair
- Department of Pediatrics, Division of Pediatric Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Tyler Huffaker
- Department of Pediatrics, Division of Pediatric Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Animesh Tandon
- Department of Pediatrics, Division of Pediatric Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Pediatric Cardiology, Cleveland Clinic Children's, Cleveland, OH, USA
| | - Sandy Engelhardt
- Department of Internal Medicine III, Group Artificial Intelligence in Cardiovascular Medicine, Heidelberg University Hospital, 69120, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg/Mannheim, Germany
| | - Sven Koehler
- Department of Internal Medicine III, Group Artificial Intelligence in Cardiovascular Medicine, Heidelberg University Hospital, 69120, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg/Mannheim, Germany
| | - Thomas Pickardt
- German Competence Network for Congenital Heart Defects, DZHK (German Centre for Cardiovascular Research), Berlin, Germany
| | - Philipp Beerbaum
- German Competence Network for Congenital Heart Defects, DZHK (German Centre for Cardiovascular Research), Berlin, Germany
- Department for Paediatric Cardiology and Paediatric Intensive Care Medicine, University Children's Hospital, Hannover Medical School, Hannover, Germany
| | - Samir Sarikouch
- German Competence Network for Congenital Heart Defects, DZHK (German Centre for Cardiovascular Research), Berlin, Germany
- Department of Cardiothoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - Heiner Latus
- Department of Paediatric Cardiology and Congenital Heart Defects, German Heart Centre Munich, Munich, Germany
| | - Gerald Greil
- Department of Pediatrics, Division of Pediatric Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Alistair A Young
- Department of Biomedical Engineering, King's College London, 1 Lambeth Palace Road, London, SE1 7EU, UK.
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.
| | - Tarique Hussain
- Department of Pediatrics, Division of Pediatric Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Pontiki AA, De Angelis S, Dibblin C, Trujillo-Cortes I, Lamata P, Housden R, Benedetti G, Bille A, Rhode K. Development and Evaluation of a Rib Statistical Shape Model for Thoracic Surgery. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3758-3763. [PMID: 36085707 DOI: 10.1109/embc48229.2022.9870985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Patients with advanced cancer undergoing chest wall resection may require reconstruction. Currently, rib prostheses are created by segmenting computed tomography images, which is time-consuming and labour intensive. The aim was to optimise the production of digital rib models based on a patient's age, weight, height and gender. A statistical shape model of human ribs was created and used to synthetise rib models, which were compared to the ones produced by segmentation and mirroring. The segmentation took 11.56±1.60 min compared to 0.027 ±0.009 min using the new technique. The average mesh error between the mirroring technique and segmentation was 0.58±0.25 mm (right ribs), and 0.87±0.18 mm (left ribs), compared to 1.37±0.66 mm ( ) and 1.68 ±0.77 mm ( ), respectively, for the new technique. The new technique is promising for the efficiency and ease-of-use in the clinical environment. Clinical Relevance- This is an optimised 3D modelling method providing clinicians with a time-efficient technique to create patient-specific rib prostheses, without any expertise or software knowledge required.
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12
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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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13
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Augustin CM, Gsell MA, Karabelas E, Willemen E, Prinzen FW, Lumens J, Vigmond EJ, Plank G. A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021; 386:114092. [PMID: 34630765 PMCID: PMC7611781 DOI: 10.1016/j.cma.2021.114092] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Computer models of cardiac electro-mechanics (EM) show promise as an effective means for the quantitative analysis of clinical data and, potentially, for predicting therapeutic responses. To realize such advanced applications methodological key challenges must be addressed. Enhanced computational efficiency and robustness is crucial to facilitate, within tractable time frames, model personalization, the simulation of prolonged observation periods under a broad range of conditions, and physiological completeness encompassing therapy-relevant mechanisms is needed to endow models with predictive capabilities beyond the mere replication of observations. Here, we introduce a universal feature-complete cardiac EM modeling framework that builds on a flexible method for coupling a 3D model of bi-ventricular EM to the physiologically comprehensive 0D CircAdapt model representing atrial mechanics and closed-loop circulation. A detailed mathematical description is given and efficiency, robustness, and accuracy of numerical scheme and solver implementation are evaluated. After parameterization and stabilization of the coupled 3D-0D model to a limit cycle under baseline conditions, the model's ability to replicate physiological behaviors is demonstrated, by simulating the transient response to alterations in loading conditions and contractility, as induced by experimental protocols used for assessing systolic and diastolic ventricular properties. Mechanistic completeness and computational efficiency of this novel model render advanced applications geared towards predicting acute outcomes of EM therapies feasible.
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Affiliation(s)
- Christoph M. Augustin
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Matthias A.F. Gsell
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Erik Willemen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Frits W. Prinzen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Edward J. Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux, France
| | - Gernot Plank
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- Correspondence to: Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Neue Stiftingtalstrasse 6/IV, Graz 8010, Austria. (G. Plank)
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14
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Augustin CM, Gsell MAF, Karabelas E, Willemen E, Prinzen FW, Lumens J, Vigmond EJ, Plank G. A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021; 386:114092. [PMID: 34630765 DOI: 10.1016/jxma.2021.114092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Computer models of cardiac electro-mechanics (EM) show promise as an effective means for the quantitative analysis of clinical data and, potentially, for predicting therapeutic responses. To realize such advanced applications methodological key challenges must be addressed. Enhanced computational efficiency and robustness is crucial to facilitate, within tractable time frames, model personalization, the simulation of prolonged observation periods under a broad range of conditions, and physiological completeness encompassing therapy-relevant mechanisms is needed to endow models with predictive capabilities beyond the mere replication of observations. Here, we introduce a universal feature-complete cardiac EM modeling framework that builds on a flexible method for coupling a 3D model of bi-ventricular EM to the physiologically comprehensive 0D CircAdapt model representing atrial mechanics and closed-loop circulation. A detailed mathematical description is given and efficiency, robustness, and accuracy of numerical scheme and solver implementation are evaluated. After parameterization and stabilization of the coupled 3D-0D model to a limit cycle under baseline conditions, the model's ability to replicate physiological behaviors is demonstrated, by simulating the transient response to alterations in loading conditions and contractility, as induced by experimental protocols used for assessing systolic and diastolic ventricular properties. Mechanistic completeness and computational efficiency of this novel model render advanced applications geared towards predicting acute outcomes of EM therapies feasible.
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Affiliation(s)
- Christoph M Augustin
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Matthias A F Gsell
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Erik Willemen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Frits W Prinzen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux, France
| | - Gernot Plank
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
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15
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Romaszko L, Borowska A, Lazarus A, Dalton D, Berry C, Luo X, Husmeier D, Gao H. Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics. Artif Intell Med 2021; 119:102140. [PMID: 34531009 DOI: 10.1016/j.artmed.2021.102140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 06/10/2021] [Accepted: 08/03/2021] [Indexed: 12/24/2022]
Abstract
Combining biomechanical modelling of left ventricular (LV) function and dysfunction with cardiac magnetic resonance (CMR) imaging has the potential to improve the prognosis of patient-specific cardiovascular disease risks. Biomechanical studies of LV function in three dimensions usually rely on a computerized representation of the LV geometry based on finite element discretization, which is essential for numerically simulating in vivo cardiac dynamics. Detailed knowledge of the LV geometry is also relevant for various other clinical applications, such as assessing the LV cavity volume and wall thickness. Accurately and automatically reconstructing personalized LV geometries from conventional CMR images with minimal manual intervention is still a challenging task, which is a pre-requisite for any subsequent automated biomechanical analysis. We propose a deep learning-based automatic pipeline for predicting the three-dimensional LV geometry directly from routinely-available CMR cine images, without the need to manually annotate the ventricular wall. Our framework takes advantage of a low-dimensional representation of the high-dimensional LV geometry based on principal component analysis. We analyze how the inference of myocardial passive stiffness is affected by using our automatically generated LV geometries instead of manually generated ones. These insights will inform the development of statistical emulators of LV dynamics to avoid computationally expensive biomechanical simulations. Our proposed framework enables accurate LV geometry reconstruction, outperforming previous approaches by delivering a reconstruction error 50% lower than reported in the literature. We further demonstrate that for a nonlinear cardiac mechanics model, using our reconstructed LV geometries instead of manually extracted ones only moderately affects the inference of passive myocardial stiffness described by an anisotropic hyperelastic constitutive law. The developed methodological framework has the potential to make an important step towards personalized medicine by eliminating the need for time consuming and costly manual operations. In addition, our method automatically maps the CMR scan into a low-dimensional representation of the LV geometry, which constitutes an important stepping stone towards the development of an LV geometry-heterogeneous emulator.
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Affiliation(s)
- Lukasz Romaszko
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Agnieszka Borowska
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Alan Lazarus
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - David Dalton
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Colin Berry
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Xiaoyu Luo
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Dirk Husmeier
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Hao Gao
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK.
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16
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Mohammadi F, Shontz SM, Linte CA. High-Order Cardiomyopathy Human Heart Model and Mesh Generation. COMPUTING IN CARDIOLOGY 2021; 2021:10.23919/cinc53138.2021.9662923. [PMID: 35647206 PMCID: PMC9140116 DOI: 10.23919/cinc53138.2021.9662923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Faithful, accurate, and successful cardiac biomechanics and electrophysiological simulations require patient-specific geometric models of the heart. Since the cardiac geometry consists of highly-curved boundaries, the use of high-order meshes with curved elements would ensure that the various curves and features present in the cardiac geometry are well-captured and preserved in the corresponding mesh. Most other existing mesh generation techniques require computer-aided design files to represent the geometric boundary, which are often not available for biomedical applications. Unlike such methods, our technique takes a high-order surface mesh, generated from patient medical images, as input and generates a high-order volume mesh directly from the curved surface mesh. In this paper, we use our direct high-order curvilinear tetrahedral mesh generation method [1] to generate several second-order cardiac meshes. Our meshes include the left ventricle myocardia of a healthy heart and hearts with dilated and hypertrophic cardiomyopathy. We show that our high-order cardiac meshes do not contain inverted elements and are of sufficiently high quality for use in cardiac finite element simulations.
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Affiliation(s)
- Fariba Mohammadi
- Department of Mechanical Engineering, University of Kansas, Lawrence, KS, USA
- Information and Telecommunication Technology Center, University of Kansas, Lawrence, KS, USA
| | - Suzanne M. Shontz
- Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA
- Bioengineering Program, University of Kansas, Lawrence, KS, USA
- Information and Telecommunication Technology Center, University of Kansas, Lawrence, KS, USA
| | - Cristian A. Linte
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
- Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA
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17
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Lu A, Ahn SS, Ta K, Parajuli N, Stendahl JC, Liu Z, Boutagy NE, Jeng GS, Staib LH, O'Donnell M, Sinusas AJ, Duncan JS. Learning-Based Regularization for Cardiac Strain Analysis via Domain Adaptation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2233-2245. [PMID: 33872145 PMCID: PMC8442959 DOI: 10.1109/tmi.2021.3074033] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Reliable motion estimation and strain analysis using 3D+ time echocardiography (4DE) for localization and characterization of myocardial injury is valuable for early detection and targeted interventions. However, motion estimation is difficult due to the low-SNR that stems from the inherent image properties of 4DE, and intelligent regularization is critical for producing reliable motion estimates. In this work, we incorporated the notion of domain adaptation into a supervised neural network regularization framework. We first propose a semi-supervised Multi-Layered Perceptron (MLP) network with biomechanical constraints for learning a latent representation that is shown to have more physiologically plausible displacements. We extended this framework to include a supervised loss term on synthetic data and showed the effects of biomechanical constraints on the network's ability for domain adaptation. We validated the semi-supervised regularization method on in vivo data with implanted sonomicrometers. Finally, we showed the ability of our semi-supervised learning regularization approach to identify infarct regions using estimated regional strain maps with good agreement to manually traced infarct regions from postmortem excised hearts.
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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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19
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Preterm Birth Is a Novel, Independent Risk Factor for Altered Cardiac Remodeling and Early Heart Failure: Is it Time for a New Cardiomyopathy? CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2019; 21:8. [PMID: 30762137 DOI: 10.1007/s11936-019-0712-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
PURPOSE OF REVIEW Around 10% of the global population is born preterm (< 37 weeks' gestation). Preterm birth is associated with an increased risk of cardiovascular events, with preterm-born individuals demonstrating a distinct cardiac phenotype. This review aims to summarize the main phenotypic features of the preterm heart and directions for future research to develop novel intervention strategies. RECENT FINDINGS Being born between 28 and 31 weeks' gestation results in a 4-fold higher risk of heart failure in childhood and adolescence and 17-fold increased risk when born less than 28 weeks' gestation. In support of this being due to a reduction in myocardial functional reserve, preterm-born young adults have an impaired left ventricular cardiac systolic response to moderate and high intensity physiological stress, despite having a preserved resting left ventricular ejection fraction. Similar impairments under physiological stress were also recently reported regarding the right ventricle in young adults born preterm. Preterm birth relates to a unique cardiac phenotype with an impaired response to stress conditions. These data, combined with the work in animal models, suggest that being born preterm may lead to a novel form of cardiomyopathy. Understanding the driving mechanisms leading to this unique cardiac phenotype is important to reduce risk of future heart failure and cardiovascular events.
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20
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Warriner DR, Jackson T, Zacur E, Sammut E, Sheridan P, Hose DR, Lawford P, Razavi R, Niederer SA, Rinaldi CA, Lamata P. An Asymmetric Wall-Thickening Pattern Predicts Response to Cardiac Resynchronization Therapy. JACC Cardiovasc Imaging 2018; 11:1545-1546. [PMID: 29550311 PMCID: PMC6288240 DOI: 10.1016/j.jcmg.2018.01.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 01/04/2018] [Indexed: 11/29/2022]
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Pablo Lamata
- Department of Biomedical Engineering, St. Thomas Hospital, 3rd Floor Lambeth Wing, Westminster Bridge Road, London SE1 7EH, United Kingdom
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21
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Quaglino A, Pezzuto S, Koutsourelakis PS, Auricchio A, Krause R. Fast uncertainty quantification of activation sequences in patient-specific cardiac electrophysiology meeting clinical time constraints. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34:e2985. [PMID: 29577657 DOI: 10.1002/cnm.2985] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 01/16/2018] [Accepted: 03/15/2018] [Indexed: 06/08/2023]
Abstract
We present a fast, patient-specific methodology for uncertainty quantification in electrophysiology, aimed at meeting the time constraints of clinical practitioners. We focus on computing the statistics of the activation map, given the uncertainties associated with the conductivity tensor modeling the fiber orientation in the heart. We use a fast parallel solution method implemented on a graphics processing unit for the eikonal approximation, in order to compute the activation map and to sample the random fiber field with correlation on the basis of geodesic distances. While this enables to perform uncertainty quantification studies with a manageable computational effort, the required time frame still exceeds clinically suitable time expectations. In order to reduce it further by 2 orders of magnitude, we rely on Bayesian multifidelity methods. In particular, we propose a low-fidelity model that is patient-specific and free from the additional training cost associated with reduced models. This is achieved by a sound physics-based simplification of the full eikonal model. The low-fidelity output is then corrected by the standard multifidelity framework. In practice, the complete procedure only requires approximately 100 new runs of our eikonal graphics processing unit solver for producing the sought estimates and their associated credible intervals, enabling a full online analysis in less than 5 minutes.
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Affiliation(s)
- A Quaglino
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - S Pezzuto
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | | | - A Auricchio
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
- Division of Cardiology, Fondazione Cardiocentro Ticino, Lugano, Switzerland
| | - R Krause
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
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22
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Gray RA, Pathmanathan P. Patient-Specific Cardiovascular Computational Modeling: Diversity of Personalization and Challenges. J Cardiovasc Transl Res 2018; 11:80-88. [PMID: 29512059 PMCID: PMC5908828 DOI: 10.1007/s12265-018-9792-2] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 02/02/2018] [Indexed: 02/07/2023]
Abstract
Patient-specific computer models have been developed representing a variety of aspects of the cardiovascular system spanning the disciplines of electrophysiology, electromechanics, solid mechanics, and fluid dynamics. These physiological mechanistic models predict macroscopic phenomena such as electrical impulse propagation and contraction throughout the entire heart as well as flow and pressure dynamics occurring in the ventricular chambers, aorta, and coronary arteries during each heartbeat. Such models have been used to study a variety of clinical scenarios including aortic aneurysms, coronary stenosis, cardiac valvular disease, left ventricular assist devices, cardiac resynchronization therapy, ablation therapy, and risk stratification. After decades of research, these models are beginning to be incorporated into clinical practice directly via marketed devices and indirectly by improving our understanding of the underlying mechanisms of health and disease within a clinical context.
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Affiliation(s)
- Richard A Gray
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA.
- , Silver Spring, USA.
| | - Pras Pathmanathan
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA
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23
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Land S, Niederer SA. Influence of atrial contraction dynamics on cardiac function. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34:e2931. [PMID: 28990354 DOI: 10.1002/cnm.2931] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 08/11/2017] [Accepted: 09/25/2017] [Indexed: 06/07/2023]
Abstract
In recent years, there has been a move from monoventricular or biventricular models of the heart, to more complex models that incorporate the electromechanical function in all 4 chambers. However, the biophysical foundation is still underdeveloped, with most work in atrial cellular models having focused on electrophysiological properties. Here, we present a biophysical model of human atrial contraction at body temperature and use it to study the effects of atrial contraction on whole organ function and a study of the effects of remodelling due to atrial fibrillation on atrial and ventricular function.
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Affiliation(s)
- Sander Land
- King's College London, Department of Biomedical Engineering, St Thomas' Hospital, SE1 7EH, London, UK
| | - Steven Alexander Niederer
- King's College London, Department of Biomedical Engineering, St Thomas' Hospital, SE1 7EH, London, UK
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24
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Lewalle A, Land S, Carruth E, Frank LR, Lamata P, Omens JH, McCulloch AD, Niederer SA, Smith NP. Decreasing Compensatory Ability of Concentric Ventricular Hypertrophy in Aortic-Banded Rat Hearts. Front Physiol 2018; 9:37. [PMID: 29527171 PMCID: PMC5829063 DOI: 10.3389/fphys.2018.00037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 01/10/2018] [Indexed: 11/28/2022] Open
Abstract
The cardiac system compensates for variations in physiological and pathophysiological conditions through a dynamic remodeling at the organ, tissue, and intracellular levels in order to maintain function. However, on longer time scales following the onset of ventricular pressure overload, such remodeling may begin to inhibit physiological function and ultimately lead to heart failure. This progression from compensatory to decompensatory behavior is poorly understood, in particular owing to the absence of a unified perspective of the concomitantly remodeling subsystems. To address this issue, the present study investigates the evolution of compensatory mechanisms, in response to overload, by integrating diffusion-tensor MRI, echocardiography, and intracellular and hemodynamic measurements within consistent computational simulations of aortic-banded rat hearts. This approach allows a comparison of the relative leverage of different cardiac properties (geometry, passive mechanical stiffness, fiber configuration, diastolic and peak calcium concentrations, calcium-binding affinity, and aortic impedance) to affect cardiac contraction. Measurements indicate that, following aortic banding, an ejection fraction (EF) of 75% was maintained, relative to control rats, despite significant remodeling of the left-ventricular wall thickness (increasing by ~90% over 4 weeks). Applying our framework, we identified the left-ventricular wall thickness (concentric hypertrophy) and the intracellular calcium dynamics as playing the dominant roles in preserving EF acutely, whereas the significance of hypertrophy decreased subsequently. This trend suggests an increasing reliance on intracellular mechanisms (average increase ~50%), rather than on anatomical features (average decrease ~60%), to achieve compensation of pump function in the early phase of heart failure.
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Affiliation(s)
- Alexandre Lewalle
- Department of Biomedical Engineering, King's College London, St. Thomas's Hospital, London, United Kingdom
| | - Sander Land
- Department of Biomedical Engineering, King's College London, St. Thomas's Hospital, London, United Kingdom
| | - Eric Carruth
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States
| | - Lawrence R. Frank
- Radiology Department, University of California, San Diego, San Diego, CA, United States
| | - Pablo Lamata
- Department of Biomedical Engineering, King's College London, St. Thomas's Hospital, London, United Kingdom
| | - Jeffrey H. Omens
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States
| | - Andrew D. McCulloch
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States
| | - Steven A. Niederer
- Department of Biomedical Engineering, King's College London, St. Thomas's Hospital, London, United Kingdom
| | - Nicolas P. Smith
- Department of Biomedical Engineering, King's College London, St. Thomas's Hospital, London, United Kingdom
- Faculty of Engineering, University of Auckland, Auckland, New Zealand
- *Correspondence: Nicolas P. Smith
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25
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Mangion K, Gao H, Husmeier D, Luo X, Berry C. Advances in computational modelling for personalised medicine after myocardial infarction. Heart 2017; 104:550-557. [PMID: 29127185 DOI: 10.1136/heartjnl-2017-311449] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 10/24/2017] [Accepted: 10/25/2017] [Indexed: 11/04/2022] Open
Abstract
Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners.
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Affiliation(s)
- Kenneth Mangion
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK.,West of Scotland Heart and Lung Centre, Golden Jubilee National Hospital, Clydebank, UK
| | - Hao Gao
- Department of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Dirk Husmeier
- Department of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Xiaoyu Luo
- Department of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Colin Berry
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK.,West of Scotland Heart and Lung Centre, Golden Jubilee National Hospital, Clydebank, UK
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26
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Aye CYL, Lewandowski AJ, Lamata P, Upton R, Davis E, Ohuma EO, Kenworthy Y, Boardman H, Wopperer S, Packham A, Adwani S, McCormick K, Papageorghiou AT, Leeson P. Disproportionate cardiac hypertrophy during early postnatal development in infants born preterm. Pediatr Res 2017; 82:36-46. [PMID: 28399117 PMCID: PMC5511508 DOI: 10.1038/pr.2017.96] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 03/20/2017] [Indexed: 12/26/2022]
Abstract
BackgroundAdults born very preterm have increased cardiac mass and reduced function. We investigated whether a hypertrophic phenomenon occurs in later preterm infants and when this occurs during early development.MethodsCardiac ultrasound was performed on 392 infants (33% preterm at mean gestation 34±2 weeks). Scans were performed during fetal development in 137, at birth and 3 months of postnatal age in 200, and during both fetal and postnatal development in 55. Cardiac morphology and function was quantified and computational models created to identify geometric changes.ResultsAt birth, preterm offspring had reduced cardiac mass and volume relative to body size with a more globular heart. By 3 months, ventricular shape had normalized but both left and right ventricular mass relative to body size were significantly higher than expected for postmenstrual age (left 57.8±41.9 vs. 27.3±29.4%, P<0.001; right 39.3±38.1 vs. 16.6±40.8, P=0.002). Greater changes were associated with lower gestational age at birth (left P<0.001; right P=0.001).ConclusionPreterm offspring, including those born in late gestation, have a disproportionate increase in ventricular mass from birth up to 3 months of postnatal age. These differences were not present before birth. Early postnatal development may provide a window for interventions relevant to long-term cardiovascular health.
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Affiliation(s)
- Christina Y L Aye
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, University of Oxford, Oxfordshire, UK
| | - Adam J Lewandowski
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, University of Oxford, Oxfordshire, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, King’s College London, London, UK
| | - Ross Upton
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, University of Oxford, Oxfordshire, UK
| | - Esther Davis
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, University of Oxford, Oxfordshire, UK
| | - Eric O Ohuma
- Centre for Statistics in Medicine, University of Oxford, Oxford, Oxfordshire, UK
| | - Yvonne Kenworthy
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, University of Oxford, Oxfordshire, UK
| | - Henry Boardman
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, University of Oxford, Oxfordshire, UK
| | - Samuel Wopperer
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, University of Oxford, Oxfordshire, UK
| | - Alice Packham
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, University of Oxford, Oxfordshire, UK
| | - Satish Adwani
- Department of Paediatrics and Neonatology, John Radcliffe Hospital, Oxford, Oxfordshire, UK
| | - Kenny McCormick
- Department of Paediatrics and Neonatology, John Radcliffe Hospital, Oxford, Oxfordshire, UK
| | - Aris T Papageorghiou
- Nuffield Department of Obstetrics & Gynaecology, University of Oxford, Oxford, Oxfordshire, UK
| | - Paul Leeson
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, University of Oxford, Oxfordshire, UK,
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27
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Land S, Park-Holohan SJ, Smith NP, Dos Remedios CG, Kentish JC, Niederer SA. A model of cardiac contraction based on novel measurements of tension development in human cardiomyocytes. J Mol Cell Cardiol 2017; 106:68-83. [PMID: 28392437 DOI: 10.1016/j.yjmcc.2017.03.008] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 01/12/2017] [Accepted: 03/31/2017] [Indexed: 11/18/2022]
Abstract
Experimental data from human cardiac myocytes at body temperature is crucial for a quantitative understanding of clinically relevant cardiac function and development of whole-organ computational models. However, such experimental data is currently very limited. Specifically, important measurements to characterize changes in tension development in human cardiomyocytes that occur with perturbations in cell length are not available. To address this deficiency, in this study we present an experimental data set collected from skinned human cardiac myocytes, including the passive and viscoelastic properties of isolated myocytes, the steady-state force calcium relationship at different sarcomere lengths, and changes in tension following a rapid increase or decrease in length, and after constant velocity shortening. This data set is, to our knowledge, the first characterization of length and velocity-dependence of tension generation in human skinned cardiac myocytes at body temperature. We use this data to develop a computational model of contraction and passive viscoelasticity in human myocytes. Our model includes troponin C kinetics, tropomyosin kinetics, a three-state crossbridge model that accounts for the distortion of crossbridges, and the cellular viscoelastic response. Each component is parametrized using our experimental data collected in human cardiomyocytes at body temperature. Furthermore we are able to confirm that properties of length-dependent activation at 37°C are similar to other species, with a shift in calcium sensitivity and increase in maximum tension. We revise our model of tension generation in the skinned isolated myocyte to replicate reported tension traces generated in intact muscle during isometric tension, to provide a model of human tension generation for multi-scale simulations. This process requires changes to calcium sensitivity, cooperativity, and crossbridge transition rates. We apply this model within multi-scale simulations of biventricular cardiac function and further refine the parametrization within the whole organ context, based on obtaining a healthy ejection fraction. This process reveals that crossbridge cycling rates differ between skinned myocytes and intact myocytes.
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Affiliation(s)
- Sander Land
- Department of Biomedical Engineering, King's College London, UK.
| | - So-Jin Park-Holohan
- Cardiovascular Division, King's College London British Heart Foundation Centre of Research Excellence, UK
| | - Nicolas P Smith
- Department of Engineering Science, University of Auckland, New Zealand
| | | | - Jonathan C Kentish
- Cardiovascular Division, King's College London British Heart Foundation Centre of Research Excellence, UK
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28
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Varela M, Bisbal F, Zacur E, Berruezo A, Aslanidi OV, Mont L, Lamata P. Novel Computational Analysis of Left Atrial Anatomy Improves Prediction of Atrial Fibrillation Recurrence after Ablation. Front Physiol 2017; 8:68. [PMID: 28261103 PMCID: PMC5306209 DOI: 10.3389/fphys.2017.00068] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 01/25/2017] [Indexed: 11/16/2022] Open
Abstract
The left atrium (LA) can change in size and shape due to atrial fibrillation (AF)-induced remodeling. These alterations can be linked to poorer outcomes of AF ablation. In this study, we propose a novel comprehensive computational analysis of LA anatomy to identify what features of LA shape can optimally predict post-ablation AF recurrence. To this end, we construct smooth 3D geometrical models from the segmentation of the LA blood pool captured in pre-procedural MR images. We first apply this methodology to characterize the LA anatomy of 144 AF patients and build a statistical shape model that includes the most salient variations in shape across this cohort. We then perform a discriminant analysis to optimally distinguish between recurrent and non-recurrent patients. From this analysis, we propose a new shape metric called vertical asymmetry, which measures the imbalance of size along the anterior to posterior direction between the superior and inferior left atrial hemispheres. Vertical asymmetry was found, in combination with LA sphericity, to be the best predictor of post-ablation recurrence at both 12 and 24 months (area under the ROC curve: 0.71 and 0.68, respectively) outperforming other shape markers and any of their combinations. We also found that model-derived shape metrics, such as the anterior-posterior radius, were better predictors than equivalent metrics taken directly from MRI or echocardiography, suggesting that the proposed approach leads to a reduction of the impact of data artifacts and noise. This novel methodology contributes to an improved characterization of LA organ remodeling and the reported findings have the potential to improve patient selection and risk stratification for catheter ablations in AF.
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Affiliation(s)
- Marta Varela
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London London, UK
| | - Felipe Bisbal
- Arrhythmia Unit-Heart Institute (iCor), Hospital Universitari Germans Trias i Pujol Badalona, Spain
| | - Ernesto Zacur
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College LondonLondon, UK; Department of Engineering Science, University of OxfordOxford, UK
| | - Antonio Berruezo
- Unitat de Fibrillació Auricular, Hospital Clínic, Universitat de Barcelona Barcelona, Spain
| | - Oleg V Aslanidi
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London London, UK
| | - Lluis Mont
- Unitat de Fibrillació Auricular, Hospital Clínic, Universitat de Barcelona Barcelona, Spain
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London London, UK
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29
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Nasopoulou A, Shetty A, Lee J, Nordsletten D, Rinaldi CA, Lamata P, Niederer S. Improved identifiability of myocardial material parameters by an energy-based cost function. Biomech Model Mechanobiol 2017. [PMID: 28188386 DOI: 10.1007/s10237‐016‐0865‐3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Myocardial stiffness is a valuable clinical biomarker for the monitoring and stratification of heart failure (HF). Cardiac finite element models provide a biomechanical framework for the assessment of stiffness through the determination of the myocardial constitutive model parameters. The reported parameter intercorrelations in popular constitutive relations, however, obstruct the unique estimation of material parameters and limit the reliable translation of this stiffness metric to clinical practice. Focusing on the role of the cost function (CF) in parameter identifiability, we investigate the performance of a set of geometric indices (based on displacements, strains, cavity volume, wall thickness and apicobasal dimension of the ventricle) and a novel CF derived from energy conservation. Our results, with a commonly used transversely isotropic material model (proposed by Guccione et al.), demonstrate that a single geometry-based CF is unable to uniquely constrain the parameter space. The energy-based CF, conversely, isolates one of the parameters and in conjunction with one of the geometric metrics provides a unique estimation of the parameter set. This gives rise to a new methodology for estimating myocardial material parameters based on the combination of deformation and energetics analysis. The accuracy of the pipeline is demonstrated in silico, and its robustness in vivo, in a total of 8 clinical data sets (7 HF and one control). The mean identified parameters of the Guccione material law were [Formula: see text] and [Formula: see text] ([Formula: see text], [Formula: see text], [Formula: see text]) for the HF cases and [Formula: see text] and [Formula: see text] ([Formula: see text], [Formula: see text], [Formula: see text]) for the healthy case.
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Affiliation(s)
- Anastasia Nasopoulou
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Anoop Shetty
- Cardiovascular Department, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Jack Lee
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - David Nordsletten
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - C Aldo Rinaldi
- Cardiovascular Department, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
| | - Steven Niederer
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
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30
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Nasopoulou A, Shetty A, Lee J, Nordsletten D, Rinaldi CA, Lamata P, Niederer S. Improved identifiability of myocardial material parameters by an energy-based cost function. Biomech Model Mechanobiol 2017; 16:971-988. [PMID: 28188386 PMCID: PMC5480093 DOI: 10.1007/s10237-016-0865-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Accepted: 12/09/2016] [Indexed: 12/16/2022]
Abstract
Myocardial stiffness is a valuable clinical biomarker for the monitoring and stratification of heart failure (HF). Cardiac finite element models provide a biomechanical framework for the assessment of stiffness through the determination of the myocardial constitutive model parameters. The reported parameter intercorrelations in popular constitutive relations, however, obstruct the unique estimation of material parameters and limit the reliable translation of this stiffness metric to clinical practice. Focusing on the role of the cost function (CF) in parameter identifiability, we investigate the performance of a set of geometric indices (based on displacements, strains, cavity volume, wall thickness and apicobasal dimension of the ventricle) and a novel CF derived from energy conservation. Our results, with a commonly used transversely isotropic material model (proposed by Guccione et al.), demonstrate that a single geometry-based CF is unable to uniquely constrain the parameter space. The energy-based CF, conversely, isolates one of the parameters and in conjunction with one of the geometric metrics provides a unique estimation of the parameter set. This gives rise to a new methodology for estimating myocardial material parameters based on the combination of deformation and energetics analysis. The accuracy of the pipeline is demonstrated in silico, and its robustness in vivo, in a total of 8 clinical data sets (7 HF and one control). The mean identified parameters of the Guccione material law were [Formula: see text] and [Formula: see text] ([Formula: see text], [Formula: see text], [Formula: see text]) for the HF cases and [Formula: see text] and [Formula: see text] ([Formula: see text], [Formula: see text], [Formula: see text]) for the healthy case.
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Affiliation(s)
- Anastasia Nasopoulou
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Anoop Shetty
- Cardiovascular Department, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Jack Lee
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - David Nordsletten
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - C Aldo Rinaldi
- Cardiovascular Department, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
| | - Steven Niederer
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
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31
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Determinants of biventricular cardiac function: a mathematical model study on geometry and myofiber orientation. Biomech Model Mechanobiol 2016; 16:721-729. [PMID: 27581324 PMCID: PMC5350259 DOI: 10.1007/s10237-016-0825-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Accepted: 08/24/2016] [Indexed: 11/19/2022]
Abstract
In patient-specific mathematical models of cardiac electromechanics, usually a patient-specific geometry and a generic myofiber orientation field are used as input, upon which myocardial tissue properties are tuned to clinical data. It remains unclear to what extent deviations in myofiber orientation and geometry between model and patient influence model predictions on cardiac function. Therefore, we evaluated the sensitivity of cardiac function for geometry and myofiber orientation in a biventricular (BiV) finite element model of cardiac mechanics. Starting out from a reference geometry in which myofiber orientation had no transmural component, two new geometries were defined with either a 27 % decrease in LV short- to long-axis ratio, or a 16 % decrease of RV length, but identical LV and RV cavity and wall volumes. These variations in geometry caused differences in both local myofiber and global pump work below 6 %. Variation of fiber orientation was induced through adaptive myofiber reorientation that caused an average change in fiber orientation of \documentclass[12pt]{minimal}
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\begin{document}$${\sim }8^\circ $$\end{document}∼8∘ predominantly through the formation of a component in transmural direction. Reorientation caused a considerable increase in local myofiber work \documentclass[12pt]{minimal}
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\begin{document}$$({\sim }18\,\%)$$\end{document}(∼18%) and in global pump work \documentclass[12pt]{minimal}
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\begin{document}$$({\sim }17\,\%)$$\end{document}(∼17%) in all three geometries, while differences between geometries were below 5 %. The findings suggest that implementing a realistic myofiber orientation is at least as important as defining a patient-specific geometry. The model for remodeling of myofiber orientation seems a useful approach to estimate myofiber orientation in the absence of accurate patient-specific information.
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Wong J, Lamata P, Rathod RH, Bertaud S, Dedieu N, Bellsham-Revell H, Pushparajah K, Razavi R, Hussain T, Schaeffter T, Powell AJ, Geva T, Greil GF. Right ventricular morphology and function following stage I palliation with a modified Blalock-Taussig shunt versus a right ventricle-to-pulmonary artery conduit. Eur J Cardiothorac Surg 2016; 51:50-57. [PMID: 27422888 PMCID: PMC5226069 DOI: 10.1093/ejcts/ezw227] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Revised: 05/17/2016] [Accepted: 05/28/2016] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES The Norwood procedure for hypoplastic left heart syndrome (HLHS) is performed either via a right ventricle-to-pulmonary artery (RVPA) conduit or a modified Blalock–Taussig (MBT) shunt. Cardiac magnetic resonance (CMR) data was used to assess the effects of the RVPA conduit on ventricular shape and function through a computational analysis of anatomy and assessment of indices of strain. METHODS A retrospective analysis of 93 CMR scans of subjects with HLHS was performed (59 with MBT shunt, 34 with RVPA conduit), incorporating data at varying stages of surgery from two congenital centres. Longitudinal and short-axis cine images were used to create a computational cardiac atlas and assess global strain. RESULTS Those receiving an RVPA conduit had significant differences (P< 0.0001) in the shape of the RV corresponding to increased ventricular dilatation (P = 0.001) and increased sphericity (P = 0.006). Differences were evident only following completion of stage II surgery. Despite preserved ejection fraction in both groups, functional strain in the RVPA conduit group compared with that in the MBT shunt group was reduced across multiple ventricular axes, including a reduced systolic longitudinal strain rate (P< 0.0001), reduced diastolic longitudinal strain rate (P = 0.0001) and reduced midventricular systolic circumferential strain (P < 0.0001). CONCLUSIONS Computational modelling analysis reveals differences in ventricular remodelling in patients with HLHS undergoing an RVPA conduit insertion with focal scarring and volume loading leading to decreased functional markers of strain. The need for continued surveillance is warranted, as deleterious effects may not become apparent until later years.
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Affiliation(s)
- James Wong
- Department of Imaging Sciences, Kings College London, St Thomas' Hospital, London, UK
| | - Pablo Lamata
- Department of Imaging Sciences, Kings College London, St Thomas' Hospital, London, UK
| | - Rahul H Rathod
- Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Sophie Bertaud
- Department of Imaging Sciences, Kings College London, St Thomas' Hospital, London, UK
| | - Nathalie Dedieu
- Department of Imaging Sciences, Kings College London, St Thomas' Hospital, London, UK
| | | | - Kuberan Pushparajah
- Department of Imaging Sciences, Kings College London, St Thomas' Hospital, London, UK
| | - Reza Razavi
- Department of Imaging Sciences, Kings College London, St Thomas' Hospital, London, UK
| | - Tarique Hussain
- Department of Imaging Sciences, Kings College London, St Thomas' Hospital, London, UK
| | - Tobias Schaeffter
- Department of Imaging Sciences, Kings College London, St Thomas' Hospital, London, UK
| | - Andrew J Powell
- Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Tal Geva
- Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Gerald F Greil
- Department of Imaging Sciences, Kings College London, St Thomas' Hospital, London, UK
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Lewandowski AJ, Lamata P, Francis JM, Piechnik SK, Ferreira VM, Boardman H, Neubauer S, Singhal A, Leeson P, Lucas A. Breast Milk Consumption in Preterm Neonates and Cardiac Shape in Adulthood. Pediatrics 2016; 138:peds.2016-0050. [PMID: 27302980 PMCID: PMC6198929 DOI: 10.1542/peds.2016-0050] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/12/2016] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Preterm birth relates to long-term alterations in cardiac morphology and function. Understanding whether preterm postnatal life is a tractable period of cardiovascular development that can be positively altered by nutrition is relevant to long-term outcomes. We hypothesized that being fed human breast milk during early postnatal life is beneficial to long-term cardiac structure and function in preterm-born individuals compared with infant formulas. METHODS A total of 926 preterm-born infants originally took part in a randomized controlled trial of postnatal milk-feeding regimens between 1982 and 1985 across 5 different UK centers. Preterm-born individuals were randomly assigned to either breast milk donated by unrelated lactating women or nutrient-enriched formulas. We followed 102 individuals from this cohort: 30 of whom had been randomized to being fed exclusively human milk and 16 to being fed exclusively formula. As a comparison group, we recruited an additional 102 individuals born term to uncomplicated pregnancies. Cardiac morphology and function were assessed by MRI. RESULTS Preterm-born individuals fed exclusively human milk as infants had increased left and right ventricular end-diastolic volume index (+9.73%, P = .04 and +18.2%, P < .001) and stroke volume index (+9.79%, P = .05 and +22.1%, P = .01) compared with preterm-born individuals who were exclusively formula fed as infants. CONCLUSIONS This study provides the first evidence of a beneficial association between breast milk and cardiac morphology and function in adult life in those born preterm and supports promotion of human milk for the care of preterm infants to reduce long-term cardiovascular risk.
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Affiliation(s)
- Adam J. Lewandowski
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom,Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Pablo Lamata
- Department of Biomedical Engineering, King’s College London, London, United Kingdom
| | - Jane M. Francis
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stefan K. Piechnik
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Vanessa M. Ferreira
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Henry Boardman
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom,Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stefan Neubauer
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Atul Singhal
- MRC Childhood Nutrition Research Centre, Institute of Child Health, University College London, London, United Kingdom
| | - Paul Leeson
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom,Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Alan Lucas
- MRC Childhood Nutrition Research Centre, Institute of Child Health, University College London, London, United Kingdom
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Crozier A, Blazevic B, Lamata P, Plank G, Ginks M, Duckett S, Sohal M, Shetty A, Rinaldi CA, Razavi R, Smith NP, Niederer SA. The relative role of patient physiology and device optimisation in cardiac resynchronisation therapy: A computational modelling study. J Mol Cell Cardiol 2015; 96:93-100. [PMID: 26546827 PMCID: PMC4915816 DOI: 10.1016/j.yjmcc.2015.10.026] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 10/12/2015] [Accepted: 10/21/2015] [Indexed: 11/22/2022]
Abstract
Cardiac resynchronisation therapy (CRT) is an established treatment for heart failure, however the effective selection of patients and optimisation of therapy remain controversial. While extensive research is ongoing, it remains unclear whether improvements in patient selection or therapy planning offers a greater opportunity for the improvement of clinical outcomes. This computational study investigates the impact of both physiological conditions that guide patient selection and the optimisation of pacing lead placement on CRT outcomes. A multi-scale biophysical model of cardiac electromechanics was developed and personalised to patient data in three patients. These models were separated into components representing cardiac anatomy, pacing lead location, myocardial conductivity and stiffness, afterload, active contraction and conduction block for each individual, and recombined to generate a cohort of 648 virtual patients. The effect of these components on the change in total activation time of the ventricles (ΔTAT) and acute haemodynamic response (AHR) was analysed. The pacing site location was found to have the largest effect on ΔTAT and AHR. Secondary effects on ΔTAT and AHR were found for functional conduction block and cardiac anatomy. The simulation results highlight a need for a greater emphasis on therapy optimisation in order to achieve the best outcomes for patients. Ventricular conduction block indicates patient response to CRT. Placement of CRT pacing leads strongly affects response to therapy. Improved treatment planning should be prioritised in order to maximise CRT outcomes.
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Affiliation(s)
- Andrew Crozier
- Division of Imaging Sciences and Biomedical Engineering, King's College London, United Kingdom; Institute of Biophysics, Medical University of Graz, Austria
| | - Bojan Blazevic
- Division of Imaging Sciences and Biomedical Engineering, King's College London, United Kingdom
| | - Pablo Lamata
- Division of Imaging Sciences and Biomedical Engineering, King's College London, United Kingdom
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Austria
| | - Matthew Ginks
- Department of Cardiology, Guy's and St. Thomas' Hospital, London, United Kingdom
| | - Simon Duckett
- Department of Cardiology, Guy's and St. Thomas' Hospital, London, United Kingdom
| | - Manav Sohal
- Department of Cardiology, Guy's and St. Thomas' Hospital, London, United Kingdom
| | - Anoop Shetty
- Department of Cardiology, Guy's and St. Thomas' Hospital, London, United Kingdom
| | | | - Reza Razavi
- Division of Imaging Sciences and Biomedical Engineering, King's College London, United Kingdom
| | - Nicolas P Smith
- Division of Imaging Sciences and Biomedical Engineering, King's College London, United Kingdom; Faculty of Engineering, University of Auckland, New Zealand
| | - Steven A Niederer
- Division of Imaging Sciences and Biomedical Engineering, King's College London, United Kingdom.
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Clinical Diagnostic Biomarkers from the Personalization of Computational Models of Cardiac Physiology. Ann Biomed Eng 2015; 44:46-57. [PMID: 26399986 DOI: 10.1007/s10439-015-1439-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Accepted: 08/25/2015] [Indexed: 10/23/2022]
Abstract
Computational modelling of the heart is rapidly advancing to the point of clinical utility. However, the difficulty of parameterizing and validating models from clinical data indicates that the routine application of truly predictive models remains a significant challenge. We argue there is significant value in an intermediate step towards prediction. This step is the use of biophysically based models to extract clinically useful information from existing patient data. Specifically in this paper we review methodologies for applying modelling frameworks for this goal in the areas of quantifying cardiac anatomy, estimating myocardial stiffness and optimizing measurements of coronary perfusion. Using these indicative examples of the general overarching approach, we finally discuss the value, ongoing challenges and future potential for applying biophysically based modelling in the clinical context.
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Myocardial Stiffness Estimation: A Novel Cost Function for Unique Parameter Identification. ACTA ACUST UNITED AC 2015. [DOI: 10.1007/978-3-319-20309-6_41] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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Land S, Niederer SA, Lamata P, Smith NP. Improving the stability of cardiac mechanical simulations. IEEE Trans Biomed Eng 2014; 62:939-947. [PMID: 25474804 DOI: 10.1109/tbme.2014.2373399] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In the field of cardiac modeling, the mechanical action of the heart is often simulated using finite element methods. These simulations are becoming increasingly challenging as the computational domain is customized to a patient's anatomy, within which large heterogeneous tension gradients are generated via biophysical cell models which drive simulations of the cardiac pump cycle. The convergence of nonlinear solvers in simulations of large deformation mechanics depends on many factors. When extreme stress or irregular deformations are modeled, commonly used numerical methods can often fail to find a solution, which can prevent investigation of interesting parameter variations or use of models in a clinical context with high standards for robustness. This paper outlines a novel numerical method that is straightforward to implement and which significantly improves the stability of these simulations. The method involves adding a compressibility penalty to the standard incompressible formulation of large deformation mechanics. We compare the method's performance when used with both a direct discretization of the equations for incompressible solid mechanics, as well as the formulation based on an isochoric/deviatoric split of the deformation gradient. The addition of this penalty decreases the tendency for solutions to deviate from the incompressibility constraint, and significantly improves the ability of the Newton solver to find a solution. Additionally, our method maintains the expected order of convergence under mesh refinement, has nearly identical solutions for the pressure-volume relations, and stabilizes the solver to allow challenging simulations of both diastolic and systolic function on personalized patient geometries.
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Affiliation(s)
| | | | | | - Nicolas P Smith
- Department of Biomedical Engineering, King's College, London
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Suinesiaputra A, Medrano-Gracia P, Cowan BR, Young AA. Big heart data: advancing health informatics through data sharing in cardiovascular imaging. IEEE J Biomed Health Inform 2014; 19:1283-90. [PMID: 25415993 DOI: 10.1109/jbhi.2014.2370952] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The burden of heart disease is rapidly worsening due to the increasing prevalence of obesity and diabetes. Data sharing and open database resources for heart health informatics are important for advancing our understanding of cardiovascular function, disease progression and therapeutics. Data sharing enables valuable information, often obtained at considerable expense and effort, to be reused beyond the specific objectives of the original study. Many government funding agencies and journal publishers are requiring data reuse, and are providing mechanisms for data curation and archival. Tools and infrastructure are available to archive anonymous data from a wide range of studies, from descriptive epidemiological data to gigabytes of imaging data. Meta-analyses can be performed to combine raw data from disparate studies to obtain unique comparisons or to enhance statistical power. Open benchmark datasets are invaluable for validating data analysis algorithms and objectively comparing results. This review provides a rationale for increased data sharing and surveys recent progress in the cardiovascular domain. We also highlight the potential of recent large cardiovascular epidemiological studies enabling collaborative efforts to facilitate data sharing, algorithms benchmarking, disease modeling and statistical atlases.
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Kowallick JT, Lamata P, Hussain ST, Kutty S, Steinmetz M, Sohns JM, Fasshauer M, Staab W, Unterberg-Buchwald C, Bigalke B, Lotz J, Hasenfuß G, Schuster A. Quantification of left ventricular torsion and diastolic recoil using cardiovascular magnetic resonance myocardial feature tracking. PLoS One 2014; 9:e109164. [PMID: 25285656 PMCID: PMC4186780 DOI: 10.1371/journal.pone.0109164] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Accepted: 08/29/2014] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVES Cardiovascular magnetic resonance feature tracking (CMR-FT) offers quantification of myocardial deformation from routine cine images. However, data using CMR-FT to quantify left ventricular (LV) torsion and diastolic recoil are not yet available. We therefore sought to evaluate the feasibility and reproducibility of CMR-FT to quantify LV torsion and peak recoil rate using an optimal anatomical approach. METHODS Short-axis cine stacks were acquired at rest and during dobutamine stimulation (10 and 20 µg · kg(-1) · min(-1)) in 10 healthy volunteers. Rotational displacement was analysed for all slices. A complete 3D-LV rotational model was developed using linear interpolation between adjacent slices. Torsion was defined as the difference between apical and basal rotation, divided by slice distance. Depending on the distance between the most apical (defined as 0% LV distance) and basal (defined as 100% LV distance) slices, four different models for the calculation of torsion were examined: Model-1 (25-75%), Model-2 (0-100%), Model-3 (25-100%) and Model-4 (0-75%). Analysis included subendocardial, subepicardial and global torsion and recoil rate (mean of subendocardial and subepicardial values). RESULTS Quantification of torsion and recoil rate was feasible in all subjects. There was no significant difference between the different models at rest. However, only Model-1 (25-75%) discriminated between rest and stress (Global Torsion: 2.7 ± 1.5° cm(-1), 3.6 ± 2.0° cm(-1), 5.1 ± 2.2° cm(-1), p<0.01; Global Recoil Rate: -30.1 ± 11.1° cm(-1) s(-1),-46.9 ± 15.0° cm(-1) s(-1),-68.9 ± 32.3° cm(-1) s(-1), p<0.01; for rest, 10 and 20 µg · kg(-)1 · min(-1) of dobutamine, respectively). Reproducibility was sufficient for all parameters as determined by Bland-Altman analysis, intraclass correlation coefficients and coefficient of variation. CONCLUSIONS CMR-FT based derivation of myocardial torsion and recoil rate is feasible and reproducible at rest and with dobutamine stress. Using an optimal anatomical approach measuring rotation at 25% and 75% apical and basal LV locations allows effective quantification of torsion and recoil dynamics. Application of these new measures of deformation by CMR-FT should next be explored in disease states.
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Affiliation(s)
- Johannes T. Kowallick
- Institute for Diagnostic and Interventional Radiology, Georg-August-University Göttingen, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
| | - Pablo Lamata
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
- Division of Imaging Sciences and Biomedical Engineering, The Rayne Institute, St. Thomas' Hospital, King's College London, London, United Kingdom
| | - Shazia T. Hussain
- Papworth Hospital NHS Trust, Papworth Everard, Cambridgeshire, United Kingdom
| | - Shelby Kutty
- Children's Hospital and Medical Center, University of Nebraska College of Medicine, Omaha, Nebraska, United States of America
| | - Michael Steinmetz
- Department of Pediatric Cardiology and Intensive Care Medicine, Georg-August-University Göttingen, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
| | - Jan M. Sohns
- Institute for Diagnostic and Interventional Radiology, Georg-August-University Göttingen, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
| | - Martin Fasshauer
- Institute for Diagnostic and Interventional Radiology, Georg-August-University Göttingen, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
| | - Wieland Staab
- Institute for Diagnostic and Interventional Radiology, Georg-August-University Göttingen, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
| | - Christina Unterberg-Buchwald
- Department of Cardiology and Pneumology, Georg-August-University Göttingen, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
| | - Boris Bigalke
- Division of Imaging Sciences and Biomedical Engineering, The Rayne Institute, St. Thomas' Hospital, King's College London, London, United Kingdom
- Medizinische Klinik III, Kardiologie und Kreislauferkrankungen, Eberhard-Karls-Universität Tübingen, Tübingen, Germany
| | - Joachim Lotz
- Institute for Diagnostic and Interventional Radiology, Georg-August-University Göttingen, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
| | - Gerd Hasenfuß
- Department of Cardiology and Pneumology, Georg-August-University Göttingen, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
| | - Andreas Schuster
- Department of Cardiology and Pneumology, Georg-August-University Göttingen, Göttingen, Germany
- Division of Imaging Sciences and Biomedical Engineering, The Rayne Institute, St. Thomas' Hospital, King's College London, London, United Kingdom
- DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
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Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 115:226-34. [PMID: 25148771 DOI: 10.1016/j.pbiomolbio.2014.08.009] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Revised: 07/31/2014] [Accepted: 08/10/2014] [Indexed: 01/29/2023]
Abstract
Patient-specific modeling of ventricular electrophysiology requires an interpolated reconstruction of the 3-dimensional (3D) geometry of the patient ventricles from the low-resolution (Lo-res) clinical images. The goal of this study was to implement a processing pipeline for obtaining the interpolated reconstruction, and thoroughly evaluate the efficacy of this pipeline in comparison with alternative methods. The pipeline implemented here involves contouring the epi- and endocardial boundaries in Lo-res images, interpolating the contours using the variational implicit functions method, and merging the interpolation results to obtain the ventricular reconstruction. Five alternative interpolation methods, namely linear, cubic spline, spherical harmonics, cylindrical harmonics, and shape-based interpolation were implemented for comparison. In the thorough evaluation of the processing pipeline, Hi-res magnetic resonance (MR), computed tomography (CT), and diffusion tensor (DT) MR images from numerous hearts were used. Reconstructions obtained from the Hi-res images were compared with the reconstructions computed by each of the interpolation methods from a sparse sample of the Hi-res contours, which mimicked Lo-res clinical images. Qualitative and quantitative comparison of these ventricular geometry reconstructions showed that the variational implicit functions approach performed better than others. Additionally, the outcomes of electrophysiological simulations (sinus rhythm activation maps and pseudo-ECGs) conducted using models based on the various reconstructions were compared. These electrophysiological simulations demonstrated that our implementation of the variational implicit functions-based method had the best accuracy.
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Images as drivers of progress in cardiac computational modelling. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 115:198-212. [PMID: 25117497 PMCID: PMC4210662 DOI: 10.1016/j.pbiomolbio.2014.08.005] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2014] [Accepted: 08/02/2014] [Indexed: 11/28/2022]
Abstract
Computational models have become a fundamental tool in cardiac research. Models are evolving to cover multiple scales and physical mechanisms. They are moving towards mechanistic descriptions of personalised structure and function, including effects of natural variability. These developments are underpinned to a large extent by advances in imaging technologies. This article reviews how novel imaging technologies, or the innovative use and extension of established ones, integrate with computational models and drive novel insights into cardiac biophysics. In terms of structural characterization, we discuss how imaging is allowing a wide range of scales to be considered, from cellular levels to whole organs. We analyse how the evolution from structural to functional imaging is opening new avenues for computational models, and in this respect we review methods for measurement of electrical activity, mechanics and flow. Finally, we consider ways in which combined imaging and modelling research is likely to continue advancing cardiac research, and identify some of the main challenges that remain to be solved.
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