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Duchateau N, Viallon M, Petrusca L, Clarysse P, Mewton N, Belle L, Croisille P. Pixel-wise statistical analysis of myocardial injury in STEMI patients with delayed enhancement MRI. Front Cardiovasc Med 2023; 10:1136760. [PMID: 37396590 PMCID: PMC10313104 DOI: 10.3389/fcvm.2023.1136760] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 06/06/2023] [Indexed: 07/04/2023] Open
Abstract
Objectives Myocardial injury assessment from delayed enhancement magnetic resonance images is routinely limited to global descriptors such as size and transmurality. Statistical tools from computational anatomy can drastically improve this characterization, and refine the assessment of therapeutic procedures aiming at infarct size reduction. Based on these techniques, we propose a new characterization of myocardial injury up to the pixel resolution. We demonstrate it on the imaging data from the Minimalist Immediate Mechanical Intervention randomized clinical trial (MIMI: NCT01360242), which aimed at comparing immediate and delayed stenting in acute ST-Elevation Myocardial Infarction (STEMI) patients. Methods We analyzed 123 patients from the MIMI trial (62 ± 12 years, 98 male, 65 immediate 58 delayed stenting). Early and late enhancement images were transported onto a common geometry using techniques inspired by statistical atlases, allowing pixel-wise comparisons across population subgroups. A practical visualization of lesion patterns against specific clinical and therapeutic characteristics was also proposed using state-of-the-art dimensionality reduction. Results Infarct patterns were roughly comparable between the two treatments across the whole myocardium. Subtle but significant local differences were observed for the LCX and RCA territories with higher transmurality for delayed stenting at lateral and inferior/inferoseptal locations, respectively (15% and 23% of myocardial locations with a p-value <0.05, mainly in these regions). In contrast, global measurements were comparable for all territories (no statistically significant differences for all-except-one measurements before standardization / for all after standardization), although immediate stenting resulted in more subjects without reperfusion injury. Conclusion Our approach substantially empowers the analysis of lesion patterns with standardized comparisons up to the pixel resolution, and may reveal subtle differences not accessible with global observations. On the MIMI trial data as illustrative case, it confirmed its general conclusions regarding the lack of benefit of delayed stenting, but revealed subgroups differences thanks to the standardized and finer analysis scale.
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Affiliation(s)
- Nicolas Duchateau
- Univ Lyon, CREATIS, INSA, CNRS UMR 5220, INSERM U1294, Université Lyon 1, UJM Saint-Etienne, Lyon, France
- Institut Universitaire de France (IUF), Paris, France
| | - Magalie Viallon
- Univ Lyon, CREATIS, INSA, CNRS UMR 5220, INSERM U1294, Université Lyon 1, UJM Saint-Etienne, Lyon, France
- Department of Radiology, Hôpital Nord, University Hospital of Saint-Étienne, Saint-Étienne, France
| | - Lorena Petrusca
- Univ Lyon, CREATIS, INSA, CNRS UMR 5220, INSERM U1294, Université Lyon 1, UJM Saint-Etienne, Lyon, France
| | - Patrick Clarysse
- Univ Lyon, CREATIS, INSA, CNRS UMR 5220, INSERM U1294, Université Lyon 1, UJM Saint-Etienne, Lyon, France
| | - Nathan Mewton
- Department of Cardiology, Clinical Investigation Center, INSERM 1407, Hôpital Cardiovasculaire Louis Pradel, Lyon, France
| | - Loic Belle
- Department of Cardiology, Centre Hospitalier Annecy-Genevois, Annecy, France
| | - Pierre Croisille
- Univ Lyon, CREATIS, INSA, CNRS UMR 5220, INSERM U1294, Université Lyon 1, UJM Saint-Etienne, Lyon, France
- Department of Radiology, Hôpital Nord, University Hospital of Saint-Étienne, Saint-Étienne, France
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Saitta S, Maga L, Armour C, Votta E, O'Regan DP, Salmasi MY, Athanasiou T, Weinsaft JW, Xu XY, Pirola S, Redaelli A. Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107468. [PMID: 36921465 DOI: 10.1016/j.cmpb.2023.107468] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/15/2023] [Accepted: 03/05/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA. METHODS Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. RESULTS Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. CONCLUSIONS We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.
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Affiliation(s)
- Simone Saitta
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Ludovica Maga
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Chemical Engineering, Imperial College London, London, UK
| | - Chloe Armour
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Emiliano Votta
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - M Yousuf Salmasi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Jonathan W Weinsaft
- Department of Medicine (Cardiology), Weill Cornell College, New York, NY, USA
| | - Xiao Yun Xu
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Selene Pirola
- Department of Chemical Engineering, Imperial College London, London, UK; Department of BioMechanical Engineering, 3mE Faculty, Delft University of Technology, Delft, Netherlands.
| | - Alberto Redaelli
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy
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Legland D, Le TDQ, Alvarado C, Girousse C, Chateigner-Boutin AL. New Growth-Related Features of Wheat Grain Pericarp Revealed by Synchrotron-Based X-ray Micro-Tomography and 3D Reconstruction. PLANTS (BASEL, SWITZERLAND) 2023; 12:1038. [PMID: 36903900 PMCID: PMC10005608 DOI: 10.3390/plants12051038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/16/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Wheat (Triticum aestivum L.) is one of the most important crops as it provides 20% of calories and proteins to the human population. To overcome the increasing demand in wheat grain production, there is a need for a higher grain yield, and this can be achieved in particular through an increase in the grain weight. Moreover, grain shape is an important trait regarding the milling performance. Both the final grain weight and shape would benefit from a comprehensive knowledge of the morphological and anatomical determinism of wheat grain growth. Synchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages. Coupled with 3D reconstruction, this method revealed changes in the grain shape and new cellular features. The study focused on a particular tissue, the pericarp, which has been hypothesized to be involved in the control of grain development. We showed considerable spatio-temporal diversity in cell shape and orientations, and in tissue porosity associated with stomata detection. These results highlight the growth-related features rarely studied in cereal grains, which may contribute significantly to the final grain weight and shape.
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Affiliation(s)
- David Legland
- INRAE, UR BIA, 44316 Nantes, France
- INRAE, PROBE Research Infrastructure, BIBS Facility, 44316 Nantes, France
| | - Thang Duong Quoc Le
- INRAE, UR BIA, 44316 Nantes, France
- INRAE, PROBE Research Infrastructure, BIBS Facility, 44316 Nantes, France
| | | | - Christine Girousse
- INRAE, Université Clermont-Auvergne, UMR GDEC, 63000 Clermont-Ferrand, France
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A preconception lifestyle intervention in women with obesity and cardiovascular health in their children. Pediatr Res 2023:10.1038/s41390-022-02443-8. [PMID: 36624285 DOI: 10.1038/s41390-022-02443-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/05/2022] [Accepted: 12/12/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Maternal obesity during pregnancy is associated with poorer cardiovascular health (CVH) in children. A strategy to improve CVH in children could be to address preconception maternal obesity by means of a lifestyle intervention. We determined if a preconception lifestyle intervention in women with obesity improved offspring's CVH, assessed by magnetic resonance imaging (MRI). METHODS We invited children born to women who participated in a randomised controlled trial assessing the effect of a preconception lifestyle intervention in women with obesity. We assessed cardiac structure, function and geometric shape, pulse wave velocity and abdominal fat tissue by MRI. RESULTS We included 49 of 243 (20.2%) eligible children, 24 girls (49%) girls, mean age 7.1 (0.8) years. Left ventricular ejection fraction was higher in children in the intervention group as compared to children in the control group (63.0% SD 6.18 vs. 58.8% SD 5.77, p = 0.02). Shape analysis showed that intervention was associated with less regional thickening of the interventricular septum and less sphericity. There were no differences in the other outcomes of interest. CONCLUSION A preconception lifestyle intervention in women with obesity led to a higher ejection fraction and an altered cardiac shape in their offspring, which might suggest a better CVH. IMPACT A preconception lifestyle intervention in women with obesity results in a higher ejection fraction and an altered cardiac shape that may signify better cardiovascular health (CVH) in their children. This is the first experimental human evidence suggesting an effect of a preconception lifestyle intervention in women with obesity on MRI-derived indicators of CVH in their children. Improving maternal preconception health might prevent some of the detrimental consequences of maternal obesity on CVH in their children.
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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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Marx L, Niestrawska JA, Gsell MA, Caforio F, Plank G, Augustin CM. Robust and efficient fixed-point algorithm for the inverse elastostatic problem to identify myocardial passive material parameters and the unloaded reference configuration. JOURNAL OF COMPUTATIONAL PHYSICS 2022; 463:111266. [PMID: 35662800 PMCID: PMC7612790 DOI: 10.1016/j.jcp.2022.111266] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Image-based computational models of the heart represent a powerful tool to shed new light on the mechanisms underlying physiological and pathological conditions in cardiac function and to improve diagnosis and therapy planning. However, in order to enable the clinical translation of such models, it is crucial to develop personalized models that are able to reproduce the physiological reality of a given patient. There have been numerous contributions in experimental and computational biomechanics to characterize the passive behavior of the myocardium. However, most of these studies suffer from severe limitations and are not applicable to high-resolution geometries. In this work, we present a novel methodology to perform an automated identification of in vivo properties of passive cardiac biomechanics. The highly-efficient algorithm fits material parameters against the shape of a patient-specific approximation of the end-diastolic pressure-volume relation (EDPVR). Simultaneously, an unloaded reference configuration is generated, where a novel line search strategy to improve convergence and robustness is implemented. Only clinical image data or previously generated meshes at one time point during diastole and one measured data point of the EDPVR are required as an input. The proposed method can be straightforwardly coupled to existing finite element (FE) software packages and is applicable to different constitutive laws and FE formulations. Sensitivity analysis demonstrates that the algorithm is robust with respect to initial input parameters.
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Affiliation(s)
- Laura Marx
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Justyna A. Niestrawska
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Matthias A.F. Gsell
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Federica Caforio
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University of Graz, Graz, Austria
- Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - Gernot Plank
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Christoph M. Augustin
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- Corresponding author at: Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Neue Stiftingtalstrasse 6/D04, 8010 Graz, Austria. (C.M.Augustin)
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The Use of Digital Coronary Phantoms for the Validation of Arterial Geometry Reconstruction and Computation of Virtual FFR. FLUIDS 2022. [DOI: 10.3390/fluids7060201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
We present computational fluid dynamics (CFD) results of virtual fractional flow reserve (vFFR) calculations, performed on reconstructed arterial geometries derived from a digital phantom (DP). The latter provides a convenient and parsimonious description of the main vessels of the left and right coronary arterial trees, which, crucially, is CFD-compatible. Using our DP, we investigate the reconstruction error in what we deem to be the most relevant way—by evaluating the change in the computed value of vFFR, which results from varying (within representative clinical bounds) the selection of the virtual angiogram pair (defined by their viewing angles) used to segment the artery, the eccentricity and severity of the stenosis, and thereby, the CFD simulation’s luminal boundary. The DP is used to quantify reconstruction and computed haemodynamic error within the VIRTUheartTM software suite. However, our method and the associated digital phantom tool are readily transferable to equivalent, clinically oriented workflows. While we are able to conclude that error within the VIRTUheartTM workflow is suitably controlled, the principal outcomes of the work reported here are the demonstration and provision of a practical tool along with an exemplar methodology for evaluating error in a coronary segmentation process.
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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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9
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Atlas-Based Evaluation of Hemodynamic in Ascending Thoracic Aortic Aneurysms. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app12010394] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Atlas-based analyses of patients with cardiovascular diseases have recently been explored to understand the mechanistic link between shape and pathophysiology. The construction of probabilistic atlases is based on statistical shape modeling (SSM) to assess key anatomic features for a given patient population. Such an approach is relevant to study the complex nature of the ascending thoracic aortic aneurysm (ATAA) as characterized by different patterns of aortic shapes and valve phenotypes. This study was carried out to develop an SSM of the dilated aorta with both bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV), and then assess the computational hemodynamic of virtual models obtained by the deformation of the mean template for specific shape boundaries (i.e., ±1.5 standard deviation, σ). Simulations demonstrated remarkable changes in the velocity streamlines, blood pressure, and fluid shear stress with the principal shape modes such as the aortic size (Mode 1), vessel tortuosity (Mode 2), and aortic valve morphologies (Mode 3). The atlas-based disease assessment can represent a powerful tool to reveal important insights on ATAA-derived hemodynamic, especially for aneurysms which are considered to have borderline anatomies, and thus challenging decision-making. The utilization of SSMs for creating probabilistic patient cohorts can facilitate the understanding of the heterogenous nature of the dilated ascending aorta.
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Romero P, Lozano M, Martínez-Gil F, Serra D, Sebastián R, Lamata P, García-Fernández I. Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta. Front Physiol 2021; 12:713118. [PMID: 34539438 PMCID: PMC8440937 DOI: 10.3389/fphys.2021.713118] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 08/03/2021] [Indexed: 12/20/2022] Open
Abstract
The combination of machine learning methods together with computational modeling and simulation of the cardiovascular system brings the possibility of obtaining very valuable information about new therapies or clinical devices through in-silico experiments. However, the application of machine learning methods demands access to large cohorts of patients. As an alternative to medical data acquisition and processing, which often requires some degree of manual intervention, the generation of virtual cohorts made of synthetic patients can be automated. However, the generation of a synthetic sample can still be computationally demanding to guarantee that it is clinically meaningful and that it reflects enough inter-patient variability. This paper addresses the problem of generating virtual patient cohorts of thoracic aorta geometries that can be used for in-silico trials. In particular, we focus on the problem of generating a cohort of patients that meet a particular clinical criterion, regardless the access to a reference sample of that phenotype. We formalize the problem of clinically-driven sampling and assess several sampling strategies with two goals, sampling efficiency, i.e., that the generated individuals actually belong to the target population, and that the statistical properties of the cohort can be controlled. Our results show that generative adversarial networks can produce reliable, clinically-driven cohorts of thoracic aortas with good efficiency. Moreover, non-linear predictors can serve as an efficient alternative to the sometimes expensive evaluation of anatomical or functional parameters of the organ of interest.
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Affiliation(s)
- Pau Romero
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Miguel Lozano
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Francisco Martínez-Gil
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Dolors Serra
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Rafael Sebastián
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Pablo Lamata
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom
| | - Ignacio García-Fernández
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
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Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks. Med Image Anal 2021; 71:102066. [PMID: 33951597 DOI: 10.1016/j.media.2021.102066] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 03/30/2021] [Accepted: 04/01/2021] [Indexed: 11/21/2022]
Abstract
We present a parametric physics-informed neural network for the simulation of personalised left-ventricular biomechanics. The neural network is constrained to the biophysical problem in two ways: (i) the network output is restricted to a subspace built from radial basis functions capturing characteristic deformations of left ventricles and (ii) the cost function used for training is the energy potential functional specifically tailored for hyperelastic, anisotropic, nearly-incompressible active materials. The radial bases are generated from the results of a nonlinear Finite Element model coupled with an anatomical shape model derived from high-resolution cardiac images. We show that, by coupling the neural network with a simplified circulation model, we can efficiently generate computationally inexpensive estimations of cardiac mechanics. Our model is 30 times faster than the reference Finite Element model used, including training time, while yielding satisfactory average errors in the predictions of ejection fraction (-3%), peak systolic pressure (7%), stroke work (4%) and myocardial strains (14%). This physics-informed neural network is well suited to efficiently augment cardiac images with functional data and to generate large sets of synthetic cases for training deep network classifiers while it provides efficient personalization to the specific patient of interest with a high level of detail.
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12
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Rodero C, Strocchi M, Marciniak M, Longobardi S, Whitaker J, O’Neill MD, Gillette K, Augustin C, Plank G, Vigmond EJ, Lamata P, Niederer SA. Linking statistical shape models and simulated function in the healthy adult human heart. PLoS Comput Biol 2021; 17:e1008851. [PMID: 33857152 PMCID: PMC8049237 DOI: 10.1371/journal.pcbi.1008851] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 03/03/2021] [Indexed: 01/09/2023] Open
Abstract
Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes (n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average (R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.
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Affiliation(s)
- Cristobal Rodero
- Cardiac Electromechanics Research Group, Biomedical Engineering Department, King´s College London, London, United Kingdom
- Cardiac Modelling and Imaging Biomarkers, Biomedical Engineering Department, King´s College London, London, United Kingdom
- * E-mail:
| | - Marina Strocchi
- Cardiac Electromechanics Research Group, Biomedical Engineering Department, King´s College London, London, United Kingdom
| | - Maciej Marciniak
- Cardiac Modelling and Imaging Biomarkers, Biomedical Engineering Department, King´s College London, London, United Kingdom
| | - Stefano Longobardi
- Cardiac Electromechanics Research Group, Biomedical Engineering Department, King´s College London, London, United Kingdom
| | - John Whitaker
- Cardiovascular Imaging Department, King’s College London, London, United Kingdom
| | - Mark D. O’Neill
- Department of Cardiology, St Thomas’ Hospital, London, United Kingdom
| | - Karli Gillette
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | | | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Edward J. Vigmond
- Institute of Electrophysiology and Heart Modeling, Foundation Bordeaux University, Bordeaux, France
- Bordeaux Institute of Mathematics, University of Bordeaux, Bordeaux, France
| | - Pablo Lamata
- Cardiac Modelling and Imaging Biomarkers, Biomedical Engineering Department, King´s College London, London, United Kingdom
| | - Steven A. Niederer
- Cardiac Electromechanics Research Group, Biomedical Engineering Department, King´s College London, London, United Kingdom
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13
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Raisi-Estabragh Z, Harvey NC, Neubauer S, Petersen SE. Cardiovascular magnetic resonance imaging in the UK Biobank: a major international health research resource. Eur Heart J Cardiovasc Imaging 2021; 22:251-258. [PMID: 33164079 PMCID: PMC7899275 DOI: 10.1093/ehjci/jeaa297] [Citation(s) in RCA: 21] [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] [Accepted: 10/12/2020] [Indexed: 12/12/2022] Open
Abstract
The UK Biobank (UKB) is a health research resource of major international importance, incorporating comprehensive characterization of >500 000 men and women recruited between 2006 and 2010 from across the UK. There is prospective tracking of health outcomes for all participants through linkages with national cohorts (death registers, cancer registers, electronic hospital records, and primary care records). The dataset has been enhanced with the UKB imaging study, which aims to scan a subset of 100 000 participants. The imaging protocol includes magnetic resonance imaging of the brain, heart, and abdomen, carotid ultrasound, and whole-body dual X-ray absorptiometry. Since its launch in 2015, over 48 000 participants have completed the imaging study with scheduled completion in 2023. Repeat imaging of 10 000 participants has been approved and commenced in 2019. The cardiovascular magnetic resonance (CMR) scan provides detailed assessment of cardiac structure and function comprising bright blood anatomic assessment (sagittal, coronal, and axial), left and right ventricular cine images (long and short axes), myocardial tagging, native T1 mapping, aortic flow, and imaging of the thoracic aorta. The UKB is an open access resource available to health researchers across all scientific disciplines from both academia and industry with no preferential access or exclusivity. In this paper, we consider how we may best utilize the UKB CMR data to advance cardiovascular research and review notable achievements to date.
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Affiliation(s)
- Zahra Raisi-Estabragh
- William Harvey Research Institute, Centre for Advanced Cardiovascular Imaging, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, Department of Cardiac Imaging, St. Bartholomew's Hospital, Barts Health NHS Trust, London EC1A 7BE, UK
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, SO16 6YD, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, OX3 9DU, UK
| | - Steffen E Petersen
- William Harvey Research Institute, Centre for Advanced Cardiovascular Imaging, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, Department of Cardiac Imaging, St. Bartholomew's Hospital, Barts Health NHS Trust, London EC1A 7BE, UK
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14
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Computational Modeling of Blood Flow Hemodynamics for Biomechanical Investigation of Cardiac Development and Disease. J Cardiovasc Dev Dis 2021; 8:jcdd8020014. [PMID: 33572675 PMCID: PMC7912127 DOI: 10.3390/jcdd8020014] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 01/16/2021] [Accepted: 01/21/2021] [Indexed: 12/11/2022] Open
Abstract
The heart is the first functional organ in a developing embryo. Cardiac development continues throughout developmental stages while the heart goes through a serious of drastic morphological changes. Previous animal experiments as well as clinical observations showed that disturbed hemodynamics interfere with the development of the heart and leads to the formation of a variety of defects in heart valves, heart chambers, and blood vessels, suggesting that hemodynamics is a governing factor for cardiogenesis, and disturbed hemodynamics is an important source of congenital heart defects. Therefore, there is an interest to image and quantify the flowing blood through a developing heart. Flow measurement in embryonic fetal heart can be performed using advanced techniques such as magnetic resonance imaging (MRI) or echocardiography. Computational fluid dynamics (CFD) modeling is another approach especially useful when the other imaging modalities are not available and in-depth flow assessment is needed. The approach is based on numerically solving relevant physical equations to approximate the flow hemodynamics and tissue behavior. This approach is becoming widely adapted to simulate cardiac flows during the embryonic development. While there are few studies for human fetal cardiac flows, many groups used zebrafish and chicken embryos as useful models for elucidating normal and diseased cardiogenesis. In this paper, we explain the major steps to generate CFD models for simulating cardiac hemodynamics in vivo and summarize the latest findings on chicken and zebrafish embryos as well as human fetal hearts.
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15
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Lara Hernandez KA, Rienmüller T, Baumgartner D, Baumgartner C. Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability. Comput Biol Med 2020; 130:104200. [PMID: 33421825 DOI: 10.1016/j.compbiomed.2020.104200] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 12/16/2020] [Accepted: 12/21/2020] [Indexed: 12/24/2022]
Abstract
The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the visualization and interpretation of altered morphological structures and function of the heart. In recent years, there has been an increasing interest in AI and deep learning that take into account spatial and temporal information in medical image analysis. In particular, deep learning tools using temporal information in image processing have not yet found their way into daily clinical practice, despite its presumed high diagnostic and prognostic value. This review aims to synthesize the most relevant deep learning methods and discuss their clinical usability in dynamic cardiac imaging using for example the complete spatiotemporal image information of the heart cycle. Selected articles were categorized according to the following indicators: clinical applications, quality of datasets, preprocessing and annotation, learning methods and training strategy, and test performance. Clinical usability was evaluated based on these criteria by classifying the selected papers into (i) clinical level, (ii) robust candidate and (iii) proof of concept applications. Interestingly, not a single one of the reviewed papers was classified as a "clinical level" study. Almost 39% of the articles achieved a "robust candidate" and as many as 61% a "proof of concept" status. In summary, deep learning in spatiotemporal cardiac imaging is still strongly research-oriented and its implementation in clinical application still requires considerable efforts. Challenges that need to be addressed are the quality of datasets together with clinical verification and validation of the performance achieved by the used method.
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Affiliation(s)
- Karen Andrea Lara Hernandez
- Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, Austria; Department of Biomedical Engineering, Galileo University, Guatemala City, Guatemala
| | - Theresa Rienmüller
- Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, Austria
| | | | - Christian Baumgartner
- Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, Austria.
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16
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Cetin I, Raisi-Estabragh Z, Petersen SE, Napel S, Piechnik SK, Neubauer S, Gonzalez Ballester MA, Camara O, Lekadir K. Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank. Front Cardiovasc Med 2020; 7:591368. [PMID: 33240940 PMCID: PMC7667130 DOI: 10.3389/fcvm.2020.591368] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 10/06/2020] [Indexed: 12/25/2022] Open
Abstract
Cardiovascular magnetic resonance (CMR) radiomics is a novel technique for advanced cardiac image phenotyping by analyzing multiple quantifiers of shape and tissue texture. In this paper, we assess, in the largest sample published to date, the performance of CMR radiomics models for identifying changes in cardiac structure and tissue texture due to cardiovascular risk factors. We evaluated five risk factor groups from the first 5,065 UK Biobank participants: hypertension (n = 1,394), diabetes (n = 243), high cholesterol (n = 779), current smoker (n = 320), and previous smoker (n = 1,394). Each group was randomly matched with an equal number of healthy comparators (without known cardiovascular disease or risk factors). Radiomics analysis was applied to short axis images of the left and right ventricles at end-diastole and end-systole, yielding a total of 684 features per study. Sequential forward feature selection in combination with machine learning (ML) algorithms (support vector machine, random forest, and logistic regression) were used to build radiomics signatures for each specific risk group. We evaluated the degree of separation achieved by the identified radiomics signatures using area under curve (AUC), receiver operating characteristic (ROC), and statistical testing. Logistic regression with L1-regularization was the optimal ML model. Compared to conventional imaging indices, radiomics signatures improved the discrimination of risk factor vs. healthy subgroups as assessed by AUC [diabetes: 0.80 vs. 0.70, hypertension: 0.72 vs. 0.69, high cholesterol: 0.71 vs. 0.65, current smoker: 0.68 vs. 0.65, previous smoker: 0.63 vs. 0.60]. Furthermore, we considered clinical interpretation of risk-specific radiomics signatures. For hypertensive individuals and previous smokers, the surface area to volume ratio was smaller in the risk factor vs. healthy subjects; perhaps reflecting a pattern of global concentric hypertrophy in these conditions. In the diabetes subgroup, the most discriminatory radiomics feature was the median intensity of the myocardium at end-systole, which suggests a global alteration at the myocardial tissue level. This study confirms the feasibility and potential of CMR radiomics for deeper image phenotyping of cardiovascular health and disease. We demonstrate such analysis may have utility beyond conventional CMR metrics for improved detection and understanding of the early effects of cardiovascular risk factors on cardiac structure and tissue.
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Affiliation(s)
- Irem Cetin
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Sandy Napel
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Stefan K. Piechnik
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Miguel A. Gonzalez Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Oscar Camara
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Karim Lekadir
- Departament de Matematiques i Informatica, Universitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain
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17
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Statistical Shape Analysis of Ascending Thoracic Aortic Aneurysm: Correlation between Shape and Biomechanical Descriptors. J Pers Med 2020; 10:jpm10020028. [PMID: 32331429 PMCID: PMC7354467 DOI: 10.3390/jpm10020028] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/14/2020] [Accepted: 04/17/2020] [Indexed: 12/21/2022] Open
Abstract
An ascending thoracic aortic aneurysm (ATAA) is a heterogeneous disease showing different patterns of aortic dilatation and valve morphologies, each with distinct clinical course. This study aimed to explore the aortic morphology and the associations between shape and function in a population of ATAA, while further assessing novel risk models of aortic surgery not based on aortic size. Shape variability of n = 106 patients with ATAA and different valve morphologies (i.e., bicuspid versus tricuspid aortic valve) was estimated by statistical shape analysis (SSA) to compute a mean aortic shape and its deformation. Once the computational atlas was built, principal component analysis (PCA) allowed to reduce the complex ATAA anatomy to a few shape modes, which were correlated to shear stress and aortic strain, as determined by computational analysis. Findings demonstrated that shape modes are associated to specific morphological features of aneurysmal aorta as the vessel tortuosity and local bulging of the ATAA. A predictive model, built with principal shape modes of the ATAA wall, achieved better performance in stratifying surgically operated ATAAs versus monitored ATAAs, with respect to a baseline model using the maximum aortic diameter. Using current imaging resources, this study demonstrated the potential of SSA to investigate the association between shape and function in ATAAs, with the goal of developing a personalized approach for the treatment of the severity of aneurysmal aorta.
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18
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Rood JE, Stuart T, Ghazanfar S, Biancalani T, Fisher E, Butler A, Hupalowska A, Gaffney L, Mauck W, Eraslan G, Marioni JC, Regev A, Satija R. Toward a Common Coordinate Framework for the Human Body. Cell 2019; 179:1455-1467. [PMID: 31835027 PMCID: PMC6934046 DOI: 10.1016/j.cell.2019.11.019] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 10/30/2019] [Accepted: 11/13/2019] [Indexed: 01/21/2023]
Abstract
Understanding the genetic and molecular drivers of phenotypic heterogeneity across individuals is central to biology. As new technologies enable fine-grained and spatially resolved molecular profiling, we need new computational approaches to integrate data from the same organ across different individuals into a consistent reference and to construct maps of molecular and cellular organization at histological and anatomical scales. Here, we review previous efforts and discuss challenges involved in establishing such a common coordinate framework, the underlying map of tissues and organs. We focus on strategies to handle anatomical variation across individuals and highlight the need for new technologies and analytical methods spanning multiple hierarchical scales of spatial resolution.
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Affiliation(s)
- Jennifer E Rood
- Klarman Cell Observatory, Broad Institute, Cambridge, MA 02142, USA
| | - Tim Stuart
- New York Genome Center, New York, NY 10013, USA
| | - Shila Ghazanfar
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | | | - Eyal Fisher
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Andrew Butler
- New York Genome Center, New York, NY 10013, USA; New York University, Center for Genomics and Systems Biology, New York, NY 10012, USA
| | - Anna Hupalowska
- Klarman Cell Observatory, Broad Institute, Cambridge, MA 02142, USA
| | - Leslie Gaffney
- Klarman Cell Observatory, Broad Institute, Cambridge, MA 02142, USA
| | - William Mauck
- New York Genome Center, New York, NY 10013, USA; New York University, Center for Genomics and Systems Biology, New York, NY 10012, USA
| | - Gökçen Eraslan
- Klarman Cell Observatory, Broad Institute, Cambridge, MA 02142, USA
| | - John C Marioni
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK.
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute, Cambridge, MA 02142, USA; Howard Hughes Medical Institute and Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA 02142, USA.
| | - Rahul Satija
- New York Genome Center, New York, NY 10013, USA; New York University, Center for Genomics and Systems Biology, New York, NY 10012, USA.
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19
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Rusli WMR, Kedgley AE. Statistical shape modelling of the first carpometacarpal joint reveals high variation in morphology. Biomech Model Mechanobiol 2019; 19:1203-1210. [PMID: 31754950 PMCID: PMC7423863 DOI: 10.1007/s10237-019-01257-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 11/08/2019] [Indexed: 11/26/2022]
Abstract
The first carpometacarpal (CMC) joint, located at the base of the thumb and formed by the junction between the first metacarpal and trapezium, is a common site for osteoarthritis of the hand. The shape of both the first metacarpal and trapezium contributes to the intrinsic bony stability of the joint, and variability in the morphology of both these bones can affect the joint’s function. The objectives of this study were to quantify the morphological variation in the complete metacarpal and trapezium and determine any correlation between anatomical features of these two components of the first CMC joint. A multi-object statistical shape modelling pipeline, consisting of scaling, hierarchical rigid registration, non-rigid registration and projection pursuit principal component analysis, was implemented. Four anatomical measures were quantified from the shape model, namely the first metacarpal articular tilt and torsion angles and the trapezium length and width. Variations in the first metacarpal articular tilt angle (− 6.3° < θ < 12.3°) and trapezium width (10.28 mm < \documentclass[12pt]{minimal}
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\begin{document}$${\fancyscript{w}}$$\end{document}w < 11.13 mm) were identified in the first principal component. In the second principal component, variations in the first metacarpal torsion angle (0.2° < α < 14.2°), first metacarpal articular tilt angle (1.0° < θ < 6.4°) and trapezium length (12.25 mm < \documentclass[12pt]{minimal}
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\begin{document}$$\text{ }\ell$$\end{document}ℓ < 17.33 mm) were determined. Due to their implications for joint stability, the first metacarpal articular tilt angle and trapezium width may be important anatomical features which could be used to advance early detection and treatment of first CMC joint osteoarthritis.
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Affiliation(s)
- Wan M R Rusli
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | - Angela E Kedgley
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK.
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20
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Aortic root sizing for transcatheter aortic valve implantation using a shape model parameterisation. Med Biol Eng Comput 2019; 57:2081-2092. [PMID: 31353427 DOI: 10.1007/s11517-019-01996-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
During a transcatheter aortic valve implantation, an axisymmetric implant is placed in an irregularly shaped aortic root. Implanting an incorrect size can cause complications such as leakage of blood alongside or through the implant. The aim of this study was to construct a method that determines the optimal size of the implant based on the three-dimensional shape of the aortic root. Based on the pre-interventional computed tomography scan of 89 patients, a statistical shape model of their aortic root was constructed. The weights associated with the principal components and the volume of calcification in the aortic valve were used as parameters in a classification algorithm. The classification algorithm was trained using the patients with no or mild leakage after their intervention. Subsequently, the algorithms were applied to the patients with moderate to severe leakage. Cross validation showed that a random forest classifier assigned the same size in 65 ± 7% of the training cases, while 57 ± 8% of the patients with moderate to severe leakage were assigned a different size. This initial study showed that this semi-automatic method has the potential to correctly assign an implant size. Further research is required to assess whether the different size implants would improve the outcome of those patients.
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21
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O'Regan DP. Putting machine learning into motion: applications in cardiovascular imaging. Clin Radiol 2019; 75:33-37. [PMID: 31079952 DOI: 10.1016/j.crad.2019.04.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 04/04/2019] [Indexed: 12/24/2022]
Abstract
Heart and circulatory diseases cause a quarter of all deaths in the UK and cardiac imaging offers an effective tool for early diagnosis and risk-stratification to improve premature death and disability. This domain of radiology is unique in that assessing flow and motion is essential for understanding and quantifying normal physiology and disease processes. Conventional image interpretation relies on manual analysis but this often fails to capture important prognostic features in the complex disturbances of cardiovascular physiology. Machine learning (ML) in cardiovascular imaging promises to be a transformative tool and addresses an unmet need for patient-specific management, accurate prediction of future events, and the discovery of tractable molecular mechanisms of disease. This review discusses the potential of ML across every aspect of image analysis including efficient acquisition, segmentation and motion tracking, disease classification, prediction tasks and modelling of genotype-phenotype interactions; however, significant challenges remain in access to high-quality data at scale, robust validation, and clinical interpretability.
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Affiliation(s)
- D P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK.
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22
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Rodríguez‐Cantano R, Sundnes J, Rognes ME. Uncertainty in cardiac myofiber orientation and stiffnesses dominate the variability of left ventricle deformation response. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2019; 35:e3178. [PMID: 30632711 PMCID: PMC6618163 DOI: 10.1002/cnm.3178] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 12/15/2018] [Indexed: 05/26/2023]
Abstract
Computational cardiac modelling is a mature area of biomedical computing and is currently evolving from a pure research tool to aiding in clinical decision making. Assessing the reliability of computational model predictions is a key factor for clinical use, and uncertainty quantification (UQ) and sensitivity analysis are important parts of such an assessment. In this study, we apply UQ in computational heart mechanics to study uncertainty both in material parameters characterizing global myocardial stiffness and in the local muscle fiber orientation that governs tissue anisotropy. The uncertainty analysis is performed using the polynomial chaos expansion (PCE) method, which is a nonintrusive meta-modeling technique that surrogates the original computational model with a series of orthonormal polynomials over the random input parameter space. In addition, in order to study variability in the muscle fiber architecture, we model the uncertainty in orientation of the fiber field as an approximated random field using a truncated Karhunen-Loéve expansion. The results from the UQ and sensitivity analysis identify clear differences in the impact of various material parameters on global output quantities. Furthermore, our analysis of random field variations in the fiber architecture demonstrate a substantial impact of fiber angle variations on the selected outputs, highlighting the need for accurate assignment of fiber orientation in computational heart mechanics models.
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Affiliation(s)
- Rocío Rodríguez‐Cantano
- Department of Numerical Analysis and Scientific ComputingSimula Research Laboratory ASBærumNorway
| | - Joakim Sundnes
- Center for Cardiological InnovationSimula Research LaboratoryBærumNorway
| | - Marie E. Rognes
- Department of Numerical Analysis and Scientific ComputingSimula Research Laboratory ASBærumNorway
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23
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Abstract
Persistent spinal (traumatic and nontraumatic) pain is common and contributes to high societal and personal costs globally. There is an acknowledged urgency for new and interdisciplinary approaches to the condition, and soft tissues, including skeletal muscles, the spinal cord, and the brain, are rightly receiving increased attention as important biological contributors. In reaction to the recent suspicion and questioned value of imaging-based findings, this paper serves to recognize the promise that the technological evolution of imaging techniques, and particularly magnetic resonance imaging, is allowing in characterizing previously less visible morphology. We emphasize the value of quantification and data analysis of several contributors in the biopsychosocial model for understanding spinal pain. Further, we highlight emerging evidence regarding the pathobiology of changes to muscle composition (eg, atrophy, fatty infiltration), as well as advancements in neuroimaging and musculoskeletal imaging techniques (eg, fat-water imaging, functional magnetic resonance imaging, diffusion imaging, magnetization transfer imaging) for these important soft tissues. These noninvasive and objective data sources may complement known prognostic factors of poor recovery, patient self-report, diagnostic tests, and the "-omics" fields. When combined, advanced "big-data" analyses may assist in identifying associations previously not considered. Our clinical commentary is supported by empirical findings that may orient future efforts toward collaborative conversation, hypothesis generation, interdisciplinary research, and translation across a number of health fields. Our emphasis is that magnetic resonance imaging technologies and research are crucial to the advancement of our understanding of the complexities of spinal conditions. J Orthop Sports Phys Ther 2019;49(5):320-329. Epub 26 Mar 2019. doi:10.2519/jospt.2019.8793.
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24
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Gilbert K, Bai W, Mauger C, Medrano-Gracia P, Suinesiaputra A, Lee AM, Sanghvi MM, Aung N, Piechnik SK, Neubauer S, Petersen SE, Rueckert D, Young AA. Independent Left Ventricular Morphometric Atlases Show Consistent Relationships with Cardiovascular Risk Factors: A UK Biobank Study. Sci Rep 2019; 9:1130. [PMID: 30718635 PMCID: PMC6362245 DOI: 10.1038/s41598-018-37916-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 12/17/2018] [Indexed: 01/08/2023] Open
Abstract
Left ventricular (LV) mass and volume are important indicators of clinical and pre-clinical disease processes. However, much of the shape information present in modern imaging examinations is currently ignored. Morphometric atlases enable precise quantification of shape and function, but there has been no objective comparison of different atlases in the same cohort. We compared two independent LV atlases using MRI scans of 4547 UK Biobank participants: (i) a volume atlas derived by automatic non-rigid registration of image volumes to a common template, and (ii) a surface atlas derived from manually drawn epicardial and endocardial surface contours. The strength of associations between atlas principal components and cardiovascular risk factors (smoking, diabetes, high blood pressure, high cholesterol and angina) were quantified with logistic regression models and five-fold cross validation, using area under the ROC curve (AUC) and Akaike Information Criterion (AIC) metrics. Both atlases exhibited similar principal components, showed similar relationships with risk factors, and had stronger associations (higher AUC and lower AIC) than a reference model based on LV mass and volume, for all risk factors (DeLong p < 0.05). Morphometric variations associated with each risk factor could be quantified and visualized and were similar between atlases. UK Biobank LV shape atlases are robust to construction method and show stronger relationships with cardiovascular risk factors than mass and volume.
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Affiliation(s)
- Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Wenjia Bai
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Charlene Mauger
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Pau Medrano-Gracia
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Aaron M Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Mihir M Sanghvi
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Stefan K Piechnik
- Oxford NIHR Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Stefan Neubauer
- Oxford NIHR Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.
- Department of Biomedical Engineering, King's College London, London, UK.
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25
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Dawes TJW, de Marvao A, Shi W, Rueckert D, Cook SA, O'Regan DP. Identifying the optimal regional predictor of right ventricular global function: a high-resolution three-dimensional cardiac magnetic resonance study. Anaesthesia 2018; 74:312-320. [PMID: 30427059 PMCID: PMC6767156 DOI: 10.1111/anae.14494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2018] [Indexed: 12/17/2022]
Abstract
Right ventricular (RV) function has prognostic value in acute, chronic and peri‐operative disease, although the complex RV contractile pattern makes rapid assessment difficult. Several two‐dimensional (2D) regional measures estimate RV function, however the optimal measure is not known. High‐resolution three‐dimensional (3D) cardiac magnetic resonance cine imaging was acquired in 300 healthy volunteers and a computational model of RV motion created. Points where regional function was significantly associated with global function were identified and a 2D, optimised single‐point marker (SPM‐O) of global function developed. This marker was prospectively compared with tricuspid annular plane systolic excursion (TAPSE), septum‐freewall displacement (SFD) and their fractional change (TAPSE‐F, SFD‐F) in a test cohort of 300 patients in the prediction of RV ejection fraction. RV ejection fraction was significantly associated with systolic function in a contiguous 7.3 cm2 patch of the basal RV freewall combining transverse (38%), longitudinal (35%) and circumferential (27%) contraction and coinciding with the four‐chamber view. In the test cohort, all single‐point surrogates correlated with RV ejection fraction (p < 0.010), but correlation (R) was higher for SPM‐O (R = 0.44, p < 0.001) than TAPSE (R = 0.24, p < 0.001) and SFD (R = 0.22, p < 0.001), and non‐significantly higher than TAPSE‐F (R = 0.40, p < 0.001) and SFD‐F (R = 0.43, p < 0.001). SPM‐O explained more of the observed variance in RV ejection fraction (19%) and predicted it more accurately than any other 2D marker (median error 2.8 ml vs 3.6 ml, p < 0.001). We conclude that systolic motion of the basal RV freewall predicts global function more accurately than other 2D estimators. However, no markers summarise 3D contractile patterns, limiting their predictive accuracy.
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Affiliation(s)
- T J W Dawes
- National Heart and Lung Institute, Imperial College London, London, UK
| | - A de Marvao
- Medical Research Council London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - W Shi
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
| | - D Rueckert
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
| | - S A Cook
- Department of Clinical and Molecular Cardiology, Medical Research Council London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, London, UK.,Department of Cardiology, National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore
| | - D P O'Regan
- Medical Research Council London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, London, UK
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26
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Xiao Y, Fortin M, Battié MC, Rivaz H. Population-averaged MRI atlases for automated image processing and assessments of lumbar paraspinal muscles. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2018; 27:2442-2448. [PMID: 30051147 DOI: 10.1007/s00586-018-5704-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 07/16/2018] [Indexed: 12/19/2022]
Abstract
PURPOSE Growing evidence suggests an association between lumbar paraspinal muscle degeneration and low back pain (LBP). Currently, time-consuming and laborious manual segmentations of paraspinal muscles are commonly performed on magnetic resonance imaging (MRI) axial scans. Automated image analysis algorithms can mitigate these drawbacks, but they often require individual MRIs to be aligned to a standard "reference" atlas. Such atlases are well established in automated neuroimaging analysis. Our aim was to create atlases of similar nature for automated paraspinal muscle measurements. METHODS Lumbosacral T2-weighted MRIs were acquired from 117 patients who experienced LBP, stratified by gender and age group (30-39, 40-49, and 50-59 years old). Axial MRI slices of the L4-L5 and L5-S1 levels at mid-disc were obtained and aligned using group-wise linear and nonlinear image registration to produce a set of unbiased population-averaged atlases for lumbar paraspinal muscles. RESULTS The resulting atlases represent the averaged morphology and MRI intensity features of the corresponding cohorts. Differences in paraspinal muscle shapes and fat infiltration levels with respect to gender and age can be visually identified from the population-averaged data from both linear and nonlinear registrations. CONCLUSION We constructed a set of population-averaged atlases for developing automated algorithms to help analyze paraspinal muscle morphometry from axial MRI scans. Such an advancement could greatly benefit the fields of paraspinal muscle and LBP research. These slides can be retrieved under Electronic Supplementary Material.
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Affiliation(s)
- Yiming Xiao
- Robarts Research Institute, Western University, 1151 Richmond Street North, London, ON, N6A 5B7, Canada.
| | - Maryse Fortin
- PERFORM Centre, Concordia University, Montreal, Canada
| | - Michele C Battié
- School of Physical Therapy, Western University, London, Canada.,Bone and Joint Institute, Western University, London, Canada
| | - Hassan Rivaz
- PERFORM Centre, Concordia University, Montreal, Canada.,Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada
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27
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Bieging ET, Morris A, Wilson BD, McGann CJ, Marrouche NF, Cates J. Left atrial shape predicts recurrence after atrial fibrillation catheter ablation. J Cardiovasc Electrophysiol 2018; 29:966-972. [PMID: 29846999 DOI: 10.1111/jce.13641] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 03/25/2018] [Accepted: 03/28/2018] [Indexed: 01/13/2023]
Abstract
INTRODUCTION Multiple markers left atrium (LA) remodeling, including LA shape, correlate with outcomes in atrial fibrillation (AF). Catheter ablation is an important treatment of AF, but better tools are needed to determine which patients will benefit. In this study, we use particle-based modeling to quantitatively assess LA shape, and determine to what degree it predicts AF recurrence after catheter ablation. METHODS AND RESULTS There were 254 patients enrolled in the DECAAF study who underwent cardiac magnetic resonance imaging of the LA prior to AF ablation and were followed for recurrence for up to 475 days. We performed particle-based shape modeling on each patient's LA shape. We selected shape parameters using the LASSO method and factor analysis, and then added them to a Cox regression model, which included multiple clinical parameters and LA fibrosis. We computed Harrell's C-statistic with and without shape in the model. We used the model to stratify patients into recurrence risk classes by both shape and shape and fibrosis combined. Three shape parameters were selected for inclusion. The C-statistic increased from 0.68 to 0.72 when shape was added to the model (P < 0.05). Visualized shapes showed that a more round LA shape with a shorter, more laterally rotated appendage was predictive of recurrence. CONCLUSION LA shape is an independent predictor of recurrence after AF ablation. When combined with LA fibrosis, shape analysis using PBM may improve patient selection for ablation.
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Affiliation(s)
- Erik T Bieging
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, UT, USA
| | - Alan Morris
- Comprehensive Arrhythmia Research and Management Center, University of Utah, Salt Lake City, UT, USA
| | - Brent D Wilson
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, UT, USA
| | | | - Nassir F Marrouche
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, UT, USA.,Comprehensive Arrhythmia Research and Management Center, University of Utah, Salt Lake City, UT, USA
| | - Joshua Cates
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
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28
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Sack KL, Davies NH, Guccione JM, Franz T. Personalised computational cardiology: Patient-specific modelling in cardiac mechanics and biomaterial injection therapies for myocardial infarction. Heart Fail Rev 2018; 21:815-826. [PMID: 26833320 PMCID: PMC4969231 DOI: 10.1007/s10741-016-9528-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Predictive computational modelling in biomedical research offers the potential to integrate diverse data, uncover biological mechanisms that are not easily accessible through experimental methods and expose gaps in knowledge requiring further research. Recent developments in computing and diagnostic technologies have initiated the advancement of computational models in terms of complexity and specificity. Consequently, computational modelling can increasingly be utilised as enabling and complementing modality in the clinic—with medical decisions and interventions being personalised. Myocardial infarction and heart failure are amongst the leading causes of death globally despite optimal modern treatment. The development of novel MI therapies is challenging and may be greatly facilitated through predictive modelling. Here, we review the advances in patient-specific modelling of cardiac mechanics, distinguishing specificity in cardiac geometry, myofibre architecture and mechanical tissue properties. Thereafter, the focus narrows to the mechanics of the infarcted heart and treatment of myocardial infarction with particular attention on intramyocardial biomaterial delivery.
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Affiliation(s)
- Kevin L Sack
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Private Bag X3, 7935, Observatory, South Africa
| | - Neil H Davies
- Cardiovascular Research Unit, MRC IUCHRU, Chris Barnard Division of Cardiothoracic Surgery, University of Cape Town, Observatory, South Africa
| | - Julius M Guccione
- Department of Surgery, University of California at San Francisco, San Francisco, CA, USA
| | - Thomas Franz
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Private Bag X3, 7935, Observatory, South Africa.
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29
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Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease. J Cardiovasc Transl Res 2018; 11:123-132. [PMID: 29294215 DOI: 10.1007/s12265-017-9778-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 12/18/2017] [Indexed: 12/18/2022]
Abstract
Approximately 1% of all babies are born with some form of congenital heart defect. Many serious forms of CHD can now be surgically corrected after birth, which has led to improved survival into adulthood. However, many patients require serial monitoring to evaluate progression of heart failure and determine timing of interventions. Accurate multidimensional quantification of regional heart shape and function is required for characterizing these patients. A computational atlas of single ventricle and biventricular heart shape and function enables quantification of remodeling in terms of z scores in relation to specific reference populations. Progression of disease can then be monitored effectively by longitudinal evaluation of z scores. A biomechanical analysis of cardiac function in relation to population variation enables investigation of the underlying mechanisms for developing pathology. Here, we summarize recent progress in this field, with examples in single ventricle and biventricular congenital pathologies.
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30
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Biffi C, de Marvao A, Attard MI, Dawes TJW, Whiffin N, Bai W, Shi W, Francis C, Meyer H, Buchan R, Cook SA, Rueckert D, O’Regan DP. Three-dimensional cardiovascular imaging-genetics: a mass univariate framework. Bioinformatics 2018; 34:97-103. [PMID: 28968671 PMCID: PMC5870605 DOI: 10.1093/bioinformatics/btx552] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 08/10/2017] [Accepted: 09/01/2017] [Indexed: 01/19/2023] Open
Abstract
Motivation Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for high-throughput mapping of genotype-phenotype associations in three dimensions (3D). Results High-resolution cardiac magnetic resonance images were automatically segmented in 1124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts. Availability and implementation The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work. Contact declan.oregan@imperial.ac.uk. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Carlo Biffi
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
- Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK
| | - Antonio de Marvao
- Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK
| | - Mark I Attard
- Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK
| | - Timothy J W Dawes
- Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK
- Quantitative Physiology and Genetics, National Heart and Lung Institute, Imperial College London, London, UK
| | - Nicola Whiffin
- Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK
- Quantitative Physiology and Genetics, National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Cardiovascular Biomedical Research Unit, Royal Brompton and Harefield NHS Trust, London, UK
| | - Wenjia Bai
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - Wenzhe Shi
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - Catherine Francis
- Quantitative Physiology and Genetics, National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Cardiovascular Biomedical Research Unit, Royal Brompton and Harefield NHS Trust, London, UK
| | - Hannah Meyer
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Rachel Buchan
- Quantitative Physiology and Genetics, National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Cardiovascular Biomedical Research Unit, Royal Brompton and Harefield NHS Trust, London, UK
| | - Stuart A Cook
- Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK
- Quantitative Physiology and Genetics, National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Cardiovascular Biomedical Research Unit, Royal Brompton and Harefield NHS Trust, London, UK
- Department of Cardiology, National Heart Centre Singapore, Singapore
- Programme in Cardiovascular and Metabolic Disorders, Duke National University Singapore, Singapore
| | - Daniel Rueckert
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - Declan P O’Regan
- Cardiovascular Magnetic Resonance Imaging and Genetics, MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK
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31
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Dall'Asta A, Schievano S, Bruse JL, Paramasivam G, Kaihura CT, Dunaway D, Lees CC. Quantitative analysis of fetal facial morphology using 3D ultrasound and statistical shape modeling: a feasibility study. Am J Obstet Gynecol 2017; 217:76.e1-76.e8. [PMID: 28209493 DOI: 10.1016/j.ajog.2017.02.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 01/26/2017] [Accepted: 02/06/2017] [Indexed: 11/19/2022]
Abstract
BACKGROUND The antenatal detection of facial dysmorphism using 3-dimensional ultrasound may raise the suspicion of an underlying genetic condition but infrequently leads to a definitive antenatal diagnosis. Despite advances in array and noninvasive prenatal testing, not all genetic conditions can be ascertained from such testing. OBJECTIVES The aim of this study was to investigate the feasibility of quantitative assessment of fetal face features using prenatal 3-dimensional ultrasound volumes and statistical shape modeling. STUDY DESIGN: Thirteen normal and 7 abnormal stored 3-dimensional ultrasound fetal face volumes were analyzed, at a median gestation of 29+4 weeks (25+0 to 36+1). The 20 3-dimensional surface meshes generated were aligned and served as input for a statistical shape model, which computed the mean 3-dimensional face shape and 3-dimensional shape variations using principal component analysis. RESULTS Ten shape modes explained more than 90% of the total shape variability in the population. While the first mode accounted for overall size differences, the second highlighted shape feature changes from an overall proportionate toward a more asymmetric face shape with a wide prominent forehead and an undersized, posteriorly positioned chin. Analysis of the Mahalanobis distance in principal component analysis shape space suggested differences between normal and abnormal fetuses (median and interquartile range distance values, 7.31 ± 5.54 for the normal group vs 13.27 ± 9.82 for the abnormal group) (P = .056). CONCLUSION This feasibility study demonstrates that objective characterization and quantification of fetal facial morphology is possible from 3-dimensional ultrasound. This technique has the potential to assist in utero diagnosis, particularly of rare conditions in which facial dysmorphology is a feature.
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Affiliation(s)
- Andrea Dall'Asta
- Centre for Fetal Care, Queen Charlotte's and Chelsea Hospital, Imperial College Healthcare National Health Service Trust, London, United Kingdom; Obstetrics and Gynaecology Unit, University of Parma, Parma, Italy
| | - Silvia Schievano
- University College London Institute of Child Health and Great Ormond Street Hospital for Children, London, United Kingdom
| | - Jan L Bruse
- University College London Institute of Child Health and Great Ormond Street Hospital for Children, London, United Kingdom
| | - Gowrishankar Paramasivam
- Centre for Fetal Care, Queen Charlotte's and Chelsea Hospital, Imperial College Healthcare National Health Service Trust, London, United Kingdom
| | | | - David Dunaway
- Craniofacial Unit, Great Ormond Street Hospital for Children National Health Service Foundation Trust and University College London Hospital, London, United Kingdom
| | - Christoph C Lees
- Centre for Fetal Care, Queen Charlotte's and Chelsea Hospital, Imperial College Healthcare National Health Service Trust, London, United Kingdom; Institute of Reproductive and Developmental Biology, Department of Surgery and Cancer, Imperial College London, London, United Kingdom; Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
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32
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Chitiboi T, Axel L. Magnetic resonance imaging of myocardial strain: A review of current approaches. J Magn Reson Imaging 2017; 46:1263-1280. [PMID: 28471530 DOI: 10.1002/jmri.25718] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Accepted: 03/14/2017] [Indexed: 11/07/2022] Open
Abstract
Contraction of the heart is central to its purpose of pumping blood around the body. While simple global function measures (such as the ejection fraction) are most commonly used in the clinical assessment of cardiac function, MRI also provides a range of approaches for quantitatively characterizing regional cardiac function, including the local deformation (or strain) within the heart wall. While they have been around for some years, these methods are still undergoing further technical development, and they have had relatively little clinical evaluation. However, they can provide potentially useful new ways to assess cardiac function, which may be able to contribute to better classification and treatment of heart disease. This article provides some basic background on the physical and physiological factors that determine the motion of the heart, in health and disease and then reviews some of the ways that MRI methods are being developed to image and quantify strain within the myocardium. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1263-1280.
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Affiliation(s)
- Teodora Chitiboi
- NYU School of Medicine, Department of Radiology, New York, New York, USA
| | - Leon Axel
- NYU School of Medicine, Department of Radiology, New York, New York, USA
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33
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Bruse JL, Giusti G, Baker C, Cervi E, Hsia TY, Taylor AM, Schievano S. Statistical Shape Modeling for Cavopulmonary Assist Device Development: Variability of Vascular Graft Geometry and Implications for Hemodynamics. J Med Device 2017; 11. [PMID: 28479938 DOI: 10.1115/1.4035865] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Patients born with a single functional ventricle typically undergo three-staged surgical palliation in the first years of life, with the last stage realizing a cross-like total cavopulmonary connection (TCPC) of superior and inferior vena cavas (SVC and IVC) with both left and right pulmonary arteries, allowing all deoxygenated blood to flow passively back to the lungs (Fontan circulation). Even though within the past decades more patients survive into adulthood, the connection comes at the prize of deficiencies such as chronic systemic venous hypertension and low cardiac output, which ultimately may lead to Fontan failure. Many studies have suggested that the TCPC's inherent insufficiencies might be addressed by adding a cavopulmonary assist device (CPAD) to provide the necessary pressure boost. While many device concepts are being explored, few take into account the complex cardiac anatomy typically associated with TCPCs. In this study, we focus on the extra cardiac conduit vascular graft connecting IVC and pulmonary arteries as one possible landing zone for a CPAD and describe its geometric variability in a cohort of 18 patients that had their TCPC realized with a 20mm vascular graft. We report traditional morphometric parameters and apply statistical shape modeling to determine the main contributors of graft shape variability. Such information may prove useful when designing CPADs that are adapted to the challenging anatomical boundaries in Fontan patients. We further compute the anatomical mean 3D graft shape (template graft) as a representative of key shape features of our cohort and prove this template graft to be a significantly better approximation of population and individual patient's hemodynamics than a commonly used simplified tube geometry. We therefore conclude that statistical shape modeling results can provide better models of geometric and hemodynamic boundary conditions associated with complex cardiac anatomy, which in turn may impact on improved cardiac device development.
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Affiliation(s)
- Jan L Bruse
- Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children
| | - Giuliano Giusti
- Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children
| | - Catriona Baker
- Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children
| | - Elena Cervi
- Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children
| | - Tain-Yen Hsia
- Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children
| | - Andrew M Taylor
- Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children
| | - Silvia Schievano
- Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children
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Lu X, Yang R, Xie Q, Ou S, Zha Y, Wang D. Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images. Biomed Eng Online 2017; 16:39. [PMID: 28351368 PMCID: PMC5370472 DOI: 10.1186/s12938-017-0323-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 02/10/2017] [Indexed: 12/01/2022] Open
Abstract
Background Dual-source computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperative to develop an automatic segmentation technique for 4D cardiac images. Methods In this paper, we implement the heart segmentation-propagation framework based on nonrigid registration. The corresponding points of anatomical substructures are extracted by using the extension of n-dimensional scale invariant feature transform method. They are considered as a constraint term of nonrigid registration using the free-form deformation, in order to restrain the large variations and boundary ambiguity between subjects. Results We validate our method on 15 patients at ten time phases. Atlases are constructed by the training dataset from ten patients. On the remaining data the median overlap is shown to improve significantly compared to original mutual information, in particular from 0.4703 to 0.5015 (\documentclass[12pt]{minimal}
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\begin{document}$$ p = 5.0 \times 10^{ - 4} $$\end{document}p=5.0×10-4) for left ventricle myocardium and from 0.6307 to 0.6519 (\documentclass[12pt]{minimal}
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\begin{document}$$ p = 6.0 \times 10^{ - 4} $$\end{document}p=6.0×10-4) for right atrium. Conclusions The proposed method outperforms standard mutual information of intensity only. The segmentation errors had been significantly reduced at the left ventricle myocardium and the right atrium. The mean surface distance of using our framework is around 1.73 mm for the whole heart.
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Affiliation(s)
- Xuesong Lu
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, People's Republic of China
| | - Rongqian Yang
- School of Materials Science and Engineering, South China University of Technology, Guangzhou, 510006, People's Republic of China.
| | - Qinlan Xie
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, People's Republic of China
| | - Shanxing Ou
- Radiology Department, Guangzhou General Hospital of Guangzhou Military Area Command, Guangzhou, 510010, People's Republic of China
| | - Yunfei Zha
- Department of Radiology, Remin Hospital of Wuhan University, Wuhan, 430060, People's Republic of China
| | - Defeng Wang
- Research Center for Medical Image Computing, Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. .,Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.
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35
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How successful is successful? Aortic arch shape after successful aortic coarctation repair correlates with left ventricular function. J Thorac Cardiovasc Surg 2017; 153:418-427. [DOI: 10.1016/j.jtcvs.2016.09.018] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Revised: 07/14/2016] [Accepted: 09/07/2016] [Indexed: 11/17/2022]
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36
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Biffi B, Bruse JL, Zuluaga MA, Ntsinjana HN, Taylor AM, Schievano S. Investigating Cardiac Motion Patterns Using Synthetic High-Resolution 3D Cardiovascular Magnetic Resonance Images and Statistical Shape Analysis. Front Pediatr 2017; 5:34. [PMID: 28337429 PMCID: PMC5340748 DOI: 10.3389/fped.2017.00034] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 02/06/2017] [Indexed: 01/25/2023] Open
Abstract
Diagnosis of ventricular dysfunction in congenital heart disease is more and more based on medical imaging, which allows investigation of abnormal cardiac morphology and correlated abnormal function. Although analysis of 2D images represents the clinical standard, novel tools performing automatic processing of 3D images are becoming available, providing more detailed and comprehensive information than simple 2D morphometry. Among these, statistical shape analysis (SSA) allows a consistent and quantitative description of a population of complex shapes, as a way to detect novel biomarkers, ultimately improving diagnosis and pathology understanding. The aim of this study is to describe the implementation of a SSA method for the investigation of 3D left ventricular shape and motion patterns and to test it on a small sample of 4 congenital repaired aortic stenosis patients and 4 age-matched healthy volunteers to demonstrate its potential. The advantage of this method is the capability of analyzing subject-specific motion patterns separately from the individual morphology, visually and quantitatively, as a way to identify functional abnormalities related to both dynamics and shape. Specifically, we combined 3D, high-resolution whole heart data with 2D, temporal information provided by cine cardiovascular magnetic resonance images, and we used an SSA approach to analyze 3D motion per se. Preliminary results of this pilot study showed that using this method, some differences in end-diastolic and end-systolic ventricular shapes could be captured, but it was not possible to clearly separate the two cohorts based on shape information alone. However, further analyses on ventricular motion allowed to qualitatively identify differences between the two populations. Moreover, by describing shape and motion with a small number of principal components, this method offers a fully automated process to obtain visually intuitive and numerical information on cardiac shape and motion, which could be, once validated on a larger sample size, easily integrated into the clinical workflow. To conclude, in this preliminary work, we have implemented state-of-the-art automatic segmentation and SSA methods, and we have shown how they could improve our understanding of ventricular kinetics by visually and potentially quantitatively highlighting aspects that are usually not picked up by traditional approaches.
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Affiliation(s)
- Benedetta Biffi
- Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children, London, UK; Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Jan L Bruse
- Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children , London , UK
| | - Maria A Zuluaga
- Translational Imaging Group, Centre for Medical Image Computing, University College London , London , UK
| | - Hopewell N Ntsinjana
- Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children , London , UK
| | - Andrew M Taylor
- Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children , London , UK
| | - Silvia Schievano
- Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children , London , UK
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37
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A Mathematical Spline-Based Model of Cardiac Left Ventricle Anatomy and Morphology. COMPUTATION 2016. [DOI: 10.3390/computation4040042] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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38
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Biglino G, Capelli C, Bruse J, Bosi GM, Taylor AM, Schievano S. Computational modelling for congenital heart disease: how far are we from clinical translation? Heart 2016; 103:98-103. [PMID: 27798056 PMCID: PMC5284484 DOI: 10.1136/heartjnl-2016-310423] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 09/26/2016] [Accepted: 09/29/2016] [Indexed: 12/17/2022] Open
Abstract
Computational models of congenital heart disease (CHD) have become increasingly sophisticated over the last 20 years. They can provide an insight into complex flow phenomena, allow for testing devices into patient-specific anatomies (pre-CHD or post-CHD repair) and generate predictive data. This has been applied to different CHD scenarios, including patients with single ventricle, tetralogy of Fallot, aortic coarctation and transposition of the great arteries. Patient-specific simulations have been shown to be informative for preprocedural planning in complex cases, allowing for virtual stent deployment. Novel techniques such as statistical shape modelling can further aid in the morphological assessment of CHD, risk stratification of patients and possible identification of new ‘shape biomarkers’. Cardiovascular statistical shape models can provide valuable insights into phenomena such as ventricular growth in tetralogy of Fallot, or morphological aortic arch differences in repaired coarctation. In a constant move towards more realistic simulations, models can also account for multiscale phenomena (eg, thrombus formation) and importantly include measures of uncertainty (ie, CIs around simulation results). While their potential to aid understanding of CHD, surgical/procedural decision-making and personalisation of treatments is undeniable, important elements are still lacking prior to clinical translation of computational models in the field of CHD, that is, large validation studies, cost-effectiveness evaluation and establishing possible improvements in patient outcomes.
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Affiliation(s)
- Giovanni Biglino
- Bristol Heart Institute, School of Clinical Sciences, University of Bristol, Bristol, UK.,Cardiorespiratory Unit, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK
| | - Claudio Capelli
- Cardiorespiratory Unit, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK.,Institute of Cardiovascular Science, University College London, London, UK
| | - Jan Bruse
- Cardiorespiratory Unit, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK.,Institute of Cardiovascular Science, University College London, London, UK
| | - Giorgia M Bosi
- Cardiorespiratory Unit, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK.,Institute of Cardiovascular Science, University College London, London, UK
| | - Andrew M Taylor
- Cardiorespiratory Unit, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK.,Institute of Cardiovascular Science, University College London, London, UK
| | - Silvia Schievano
- Cardiorespiratory Unit, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK.,Institute of Cardiovascular Science, University College London, London, UK
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39
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Ambale-Venkatesh B, Yoneyama K, Sharma RK, Ohyama Y, Wu CO, Burke GL, Shea S, Gomes AS, Young AA, Bluemke DA, Lima JA. Left ventricular shape predicts different types of cardiovascular events in the general population. Heart 2016; 103:499-507. [PMID: 27694110 DOI: 10.1136/heartjnl-2016-310052] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Revised: 08/31/2016] [Accepted: 09/05/2016] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE To investigate whether sphericity volume index (SVI), an indicator of left ventricular (LV) remodelling, predicts incident cardiovascular events (coronary heart disease, CHD; all cardiovascular disease, CVD; heart failure, HF; atrial fibrillation, AF) over 10 years of follow-up in a multiethnic population (Multi-Ethnic Study of Atherosclerosis). METHODS 5004 participants free of known CVD had magnetic resonance imaging (MRI) in 2000-2002. Cine images were analysed to compute, [Formula: see text] equivalent to LV volume/volume of sphere with length of LV as the diameter. The highest (greatest sphericity) and lowest (lowest sphericity) quintiles of SVI were compared against the reference group (2-4 quintiles combined). Risk-factor adjusted hazard's ratio (HR) from Cox regression assessed the predictive performance of SVI at end-diastole (ED) and end-systole (ES) to predict incident outcomes over 10 years in retrospective interpretation of prospective data. RESULTS At baseline, participants were aged 61±10 years; 52% men and 39%/13%/26%/22% Cauc/Chinese/Afr-Amer/Hispanic. Low sphericity was associated with higher Framingham CVD risk, greater coronary calcium score and higher N-terminal pro-brain natriuretic peptide (NT-proBNP); while increased sphericity was associated with higher NT-proBNP and lower ejection fraction. Low sphericity predicted incident CHD (HR: 1.48, 1.55-2.59 at ED) and CVD (HR: 1.82, 1.47-2.27 at ED). However, both low (HR: 1.81, 1.20-2.73 at ES) and high (HR: 2.21, 1.41-3.46 at ES) sphericity predicted incident HF. High sphericity also predicted AF. CONCLUSIONS In a multiethnic population free of CVD at baseline, lowest sphericity was a predictor of incident CHD, CVD and HF over a 10-year follow-up period. Extreme sphericity was a strong predictor of incident HF and AF. SVI improved risk prediction models beyond established risk factors only for HF, but not for all CVD or CHD.
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Affiliation(s)
| | | | | | | | - Colin O Wu
- National Institutes of Health, Bethesda, Maryland, USA
| | - Gregory L Burke
- Wake Forest University Health Sciences, Winston-Salem, North Carolina, USA
| | - Steven Shea
- Columbia University, New York, New York, USA
| | | | | | | | - João Ac Lima
- Johns Hopkins University, Baltimore, Maryland, USA
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40
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Frangi AF, Taylor ZA, Gooya A. Precision Imaging: more descriptive, predictive and integrative imaging. Med Image Anal 2016; 33:27-32. [PMID: 27373145 DOI: 10.1016/j.media.2016.06.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Revised: 06/15/2016] [Accepted: 06/15/2016] [Indexed: 12/22/2022]
Abstract
Medical image analysis has grown into a matured field challenged by progress made across all medical imaging technologies and more recent breakthroughs in biological imaging. The cross-fertilisation between medical image analysis, biomedical imaging physics and technology, and domain knowledge from medicine and biology has spurred a truly interdisciplinary effort that stretched outside the original boundaries of the disciplines that gave birth to this field and created stimulating and enriching synergies. Consideration on how the field has evolved and the experience of the work carried out over the last 15 years in our centre, has led us to envision a future emphasis of medical imaging in Precision Imaging. Precision Imaging is not a new discipline but rather a distinct emphasis in medical imaging borne at the cross-roads between, and unifying the efforts behind mechanistic and phenomenological model-based imaging. It captures three main directions in the effort to deal with the information deluge in imaging sciences, and thus achieve wisdom from data, information, and knowledge. Precision Imaging is finally characterised by being descriptive, predictive and integrative about the imaged object. This paper provides a brief and personal perspective on how the field has evolved, summarises and formalises our vision of Precision Imaging for Precision Medicine, and highlights some connections with past research and current trends in the field.
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Affiliation(s)
- Alejandro F Frangi
- CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine, Electronic and Electrical Engineering Department, University of Sheffield, Sheffield, UK.
| | - Zeike A Taylor
- CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine, Mechanical Engineering Department, University of Sheffield, Sheffield, UK.
| | - Ali Gooya
- CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine, Electronic and Electrical Engineering Department, University of Sheffield, Sheffield, UK.
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41
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Bruse JL, McLeod K, Biglino G, Ntsinjana HN, Capelli C, Hsia TY, Sermesant M, Pennec X, Taylor AM, Schievano S. A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta. BMC Med Imaging 2016; 16:40. [PMID: 27245048 PMCID: PMC4894556 DOI: 10.1186/s12880-016-0142-z] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 05/19/2016] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements. METHODS Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient's anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters. RESULTS The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors. CONCLUSIONS The suggested method has the potential to discover previously unknown 3D shape biomarkers from medical imaging data. Thus, it could contribute to improving diagnosis and risk stratification in complex cardiac disease.
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Affiliation(s)
- Jan L Bruse
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK.
| | - Kristin McLeod
- Cardiac Modelling Department, Simula Research Laboratory, Oslo, Norway
- Inria Sophia Antipolis-Méditeranée, ASCLEPIOS Project, Sophia Antipolis, France
| | - Giovanni Biglino
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK
- Bristol Heart Institute, School of Clinical Sciences, University of Bristol, Bristol, UK
| | - Hopewell N Ntsinjana
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK
| | - Claudio Capelli
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK
| | - Tain-Yen Hsia
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK
| | - Maxime Sermesant
- Inria Sophia Antipolis-Méditeranée, ASCLEPIOS Project, Sophia Antipolis, France
| | - Xavier Pennec
- Inria Sophia Antipolis-Méditeranée, ASCLEPIOS Project, Sophia Antipolis, France
| | - Andrew M Taylor
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK
| | - Silvia Schievano
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK
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42
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Bruse JL, McLeod K, Biglino G, Ntsinjana HN, Capelli C, Hsia TY, Sermesant M, Pennec X, Taylor AM, Schievano S. A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal? STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. IMAGING AND MODELLING CHALLENGES 2016. [DOI: 10.1007/978-3-319-28712-6_3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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43
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Affiliation(s)
- V.Y. Wang
- Auckland Bioengineering Institute and
| | - P.M.F. Nielsen
- Auckland Bioengineering Institute and
- Department of Engineering Science, Faculty of Engineering, University of Auckland, Auckland 1010, New Zealand; , ,
| | - M.P. Nash
- Auckland Bioengineering Institute and
- Department of Engineering Science, Faculty of Engineering, University of Auckland, Auckland 1010, New Zealand; , ,
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44
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Miller MI, Trouvé A, Younes L. Hamiltonian Systems and Optimal Control in Computational Anatomy: 100 Years Since D'Arcy Thompson. Annu Rev Biomed Eng 2015; 17:447-509. [PMID: 26643025 DOI: 10.1146/annurev-bioeng-071114-040601] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The Computational Anatomy project is the morphome-scale study of shape and form, which we model as an orbit under diffeomorphic group action. Metric comparison calculates the geodesic length of the diffeomorphic flow connecting one form to another. Geodesic connection provides a positioning system for coordinatizing the forms and positioning their associated functional information. This article reviews progress since the Euler-Lagrange characterization of the geodesics a decade ago. Geodesic positioning is posed as a series of problems in Hamiltonian control, which emphasize the key reduction from the Eulerian momentum with dimension of the flow of the group, to the parametric coordinates appropriate to the dimension of the submanifolds being positioned. The Hamiltonian viewpoint provides important extensions of the core setting to new, object-informed positioning systems. Several submanifold mapping problems are discussed as they apply to metamorphosis, multiple shape spaces, and longitudinal time series studies of growth and atrophy via shape splines.
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Affiliation(s)
- Michael I Miller
- Center of Imaging Science.,Department of Biomedical Engineering.,Kavli Neuroscience Discovery Institute, and
| | - Alain Trouvé
- CMLA, ENS Cachan, CNRS, Université Paris-Saclay, 94235 Cachan, France;
| | - Laurent Younes
- Center of Imaging Science.,Department of Applied Mathematics, The John Hopkins University, Baltimore, Maryland 21218; ,
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45
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Zhang X, Ambale-Venkatesh B, Bluemke DA, Cowan BR, Finn JP, Kadish AH, Lee DC, Lima JAC, Hundley WG, Suinesiaputra A, Young AA, Medrano-Gracia P. Information maximizing component analysis of left ventricular remodeling due to myocardial infarction. J Transl Med 2015; 13:343. [PMID: 26531126 PMCID: PMC4632345 DOI: 10.1186/s12967-015-0709-4] [Citation(s) in RCA: 18] [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: 06/30/2015] [Accepted: 10/23/2015] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Although adverse left ventricular shape changes (remodeling) after myocardial infarction (MI) are predictive of morbidity and mortality, current clinical assessment is limited to simple mass and volume measures, or dimension ratios such as length to width ratio. We hypothesized that information maximizing component analysis (IMCA), a supervised feature extraction method, can provide more efficient and sensitive indices of overall remodeling. METHODS IMCA was compared to linear discriminant analysis (LDA), both supervised methods, to extract the most discriminatory global shape changes associated with remodeling after MI. Finite element shape models from 300 patients with myocardial infarction from the DETERMINE study (age 31-86, mean age 63, 20 % women) were compared with 1991 asymptomatic cases from the MESA study (age 44-84, mean age 62, 52 % women) available from the Cardiac Atlas Project. IMCA and LDA were each used to identify a single mode of global remodeling best discriminating the two groups. Logistic regression was employed to determine the association between the remodeling index and MI. Goodness-of-fit results were compared against a baseline logistic model comprising standard clinical indices. RESULTS A single IMCA mode simultaneously describing end-diastolic and end-systolic shapes achieved best results (lowest Deviance, Akaike information criterion and Bayesian information criterion, and the largest area under the receiver-operating-characteristic curve). This mode provided a continuous scale where remodeling can be quantified and visualized, showing that MI patients tend to present larger size and more spherical shape, more bulging of the apex, and thinner wall thickness. CONCLUSIONS IMCA enables better characterization of global remodeling than LDA, and can be used to quantify progression of disease and the effect of treatment. These data and results are available from the Cardiac Atlas Project ( http://www.cardiacatlas.org ).
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Affiliation(s)
- Xingyu Zhang
- Department of Anatomy with Radiology, Grafton Campus, University of Auckland, 85 Park Road, Grafton, Auckland, 1148, New Zealand.
| | - Bharath Ambale-Venkatesh
- The Donald W. Reynolds Cardiovascular Clinical Research Center, The Johns Hopkins University, Baltimore, USA.
| | - David A Bluemke
- National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, USA.
| | - Brett R Cowan
- Department of Anatomy with Radiology, Grafton Campus, University of Auckland, 85 Park Road, Grafton, Auckland, 1148, New Zealand.
| | - J Paul Finn
- Department of Radiology, UCLA, Los Angeles, USA.
| | - Alan H Kadish
- Feinberg Cardiovascular Research Institute, Northwestern University Feinberg School of Medicine, Chicago, USA.
| | - Daniel C Lee
- Feinberg Cardiovascular Research Institute, Northwestern University Feinberg School of Medicine, Chicago, USA.
| | - Joao A C Lima
- The Donald W. Reynolds Cardiovascular Clinical Research Center, The Johns Hopkins University, Baltimore, USA.
| | | | - Avan Suinesiaputra
- Department of Anatomy with Radiology, Grafton Campus, University of Auckland, 85 Park Road, Grafton, Auckland, 1148, New Zealand.
| | - Alistair A Young
- Department of Anatomy with Radiology, Grafton Campus, University of Auckland, 85 Park Road, Grafton, Auckland, 1148, New Zealand.
| | - Pau Medrano-Gracia
- Department of Anatomy with Radiology, Grafton Campus, University of Auckland, 85 Park Road, Grafton, Auckland, 1148, New Zealand.
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de Marvao A, Dawes TJ, Shi W, Durighel G, Rueckert D, Cook SA, O’Regan DP. Precursors of Hypertensive Heart Phenotype Develop in Healthy Adults: A High-Resolution 3D MRI Study. JACC Cardiovasc Imaging 2015; 8:1260-9. [PMID: 26476505 PMCID: PMC4639392 DOI: 10.1016/j.jcmg.2015.08.007] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Revised: 07/28/2015] [Accepted: 08/13/2015] [Indexed: 12/24/2022]
Abstract
OBJECTIVES This study used high-resolution 3-dimensional cardiac magnetic resonance to define the anatomical and functional left ventricular (LV) properties associated with increasing systolic blood pressure (SBP) in a drug-naïve cohort. BACKGROUND LV hypertrophy and remodeling occur in response to hemodynamic stress but little is known about how these phenotypic changes are initiated in the general population. METHODS In this study, 1,258 volunteers (54% women, mean age 40.6 ± 12.8 years) without self-reported cardiovascular disease underwent 3-dimensional cardiac magnetic resonance combined with computational modeling. The relationship between SBP and wall thickness (WT), relative WT, end-systolic wall stress (WS), and fractional wall thickening were analyzed using 3-dimensional regression models adjusted for body surface area, sex, race, age, and multiple testing. Significantly associated points in the LV model (p < 0.05) were identified and the relationship with SBP reported as mean β coefficients. RESULTS There was a continuous relationship between SBP and asymmetric concentric hypertrophic adaptation of the septum and anterior wall that was associated with normalization of wall stress. In the lateral wall an increase in wall stress with rising SBP was not balanced by a commensurate hypertrophic relationship. In normotensives, SBP was positively associated with WT (β = 0.09) and relative WT (β = 0.07) in the septal and anterior walls, and this regional hypertrophic relationship was progressively stronger among pre-hypertensives (β = 0.10) and hypertensives (β = 0.30). CONCLUSIONS These findings show that the precursors of the hypertensive heart phenotype can be traced to healthy normotensive adults and that an independent and continuous relationship exists between adverse LV remodeling and SBP in a low-risk population. These adaptations show distinct regional variations with concentric hypertrophy of the septum and eccentric hypertrophy of the lateral wall, which challenge conventional classifications of LV remodeling.
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MESH Headings
- Adaptation, Physiological
- Adolescent
- Adult
- Aged
- Aged, 80 and over
- Blood Pressure
- Computer Simulation
- Cross-Sectional Studies
- Disease Progression
- Female
- Healthy Volunteers
- Humans
- Hypertrophy, Left Ventricular/etiology
- Hypertrophy, Left Ventricular/pathology
- Hypertrophy, Left Ventricular/physiopathology
- Image Interpretation, Computer-Assisted/methods
- Imaging, Three-Dimensional/methods
- Magnetic Resonance Imaging, Cine/methods
- Male
- Middle Aged
- Models, Cardiovascular
- Myocardium/pathology
- Phenotype
- Predictive Value of Tests
- Prehypertension/complications
- Prehypertension/pathology
- Prehypertension/physiopathology
- Prospective Studies
- Regression Analysis
- Ventricular Function, Left
- Ventricular Remodeling
- Young Adult
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Affiliation(s)
- Antonio de Marvao
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Timothy J.W. Dawes
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Wenzhe Shi
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
- Department of Computing, Imperial College London, South Kensington Campus, London, United Kingdom
| | - Giuliana Durighel
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Daniel Rueckert
- Department of Computing, Imperial College London, South Kensington Campus, London, United Kingdom
| | - Stuart A. Cook
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
- National Heart Centre Singapore, Singapore
- Duke–National University of Singapore Graduate Medical School, Singapore
- Reprint requests and correspondence: Dr. Declan O’Regan and Prof. Stuart Cook, Medical Research Council Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0HS, United Kingdom.
| | - Declan P. O’Regan
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
- Reprint requests and correspondence: Dr. Declan O’Regan and Prof. Stuart Cook, Medical Research Council Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0HS, United Kingdom.
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Bai W, Shi W, de Marvao A, Dawes TJW, O'Regan DP, Cook SA, Rueckert D. A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion. Med Image Anal 2015; 26:133-45. [PMID: 26387054 DOI: 10.1016/j.media.2015.08.009] [Citation(s) in RCA: 75] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Revised: 07/24/2015] [Accepted: 08/20/2015] [Indexed: 11/30/2022]
Abstract
Atlases encode valuable anatomical and functional information from a population. In this work, a bi-ventricular cardiac atlas was built from a unique data set, which consists of high resolution cardiac MR images of 1000+ normal subjects. Based on the atlas, statistical methods were used to study the variation of cardiac shapes and the distribution of cardiac motion across the spatio-temporal domain. We have shown how statistical parametric mapping (SPM) can be combined with a general linear model to study the impact of gender and age on regional myocardial wall thickness. Finally, we have also investigated the influence of the population size on atlas construction and atlas-based analysis. The high resolution atlas, the statistical models and the SPM method will benefit more studies on cardiac anatomy and function analysis in the future.
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Affiliation(s)
- Wenjia Bai
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK.
| | - Wenzhe Shi
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
| | - Antonio de Marvao
- MRC Clinical Sciences Centre, Hammersmith Hospital, Imperial College London, UK
| | - Timothy J W Dawes
- MRC Clinical Sciences Centre, Hammersmith Hospital, Imperial College London, UK
| | - Declan P O'Regan
- MRC Clinical Sciences Centre, Hammersmith Hospital, Imperial College London, UK
| | - Stuart A Cook
- MRC Clinical Sciences Centre, Hammersmith Hospital, Imperial College London, UK; National Heart Centre Singapore, Singapore, Duke-NUS Graduate Medical School, Singapore
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
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Lopez-Perez A, Sebastian R, Ferrero JM. Three-dimensional cardiac computational modelling: methods, features and applications. Biomed Eng Online 2015; 14:35. [PMID: 25928297 PMCID: PMC4424572 DOI: 10.1186/s12938-015-0033-5] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Accepted: 04/02/2015] [Indexed: 01/19/2023] Open
Abstract
The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.
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Affiliation(s)
- Alejandro Lopez-Perez
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, València, Spain.
| | - Rafael Sebastian
- Computational Multiscale Physiology Lab (CoMMLab), Universitat de València, València, Spain.
| | - Jose M Ferrero
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, València, Spain.
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Rusu M, Bloch BN, Jaffe CC, Genega EM, Lenkinski RE, Rofsky NM, Feleppa E, Madabhushi A. Prostatome: a combined anatomical and disease based MRI atlas of the prostate. Med Phys 2015; 41:072301. [PMID: 24989400 DOI: 10.1118/1.4881515] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE In this work, the authors introduce a novel framework, the anatomically constrained registration (AnCoR) scheme and apply it to create a fused anatomic-disease atlas of the prostate which the authors refer to as the prostatome. The prostatome combines a MRI based anatomic and a histology based disease atlas. Statistical imaging atlases allow for the integration of information across multiple scales and imaging modalities into a single canonical representation, in turn enabling a fused anatomical-disease representation which may facilitate the characterization of disease appearance relative to anatomic structures. While statistical atlases have been extensively developed and studied for the brain, approaches that have attempted to combine pathology and imaging data for study of prostate pathology are not extant. This works seeks to address this gap. METHODS The AnCoR framework optimizes a scoring function composed of two surface (prostate and central gland) misalignment measures and one intensity-based similarity term. This ensures the correct mapping of anatomic regions into the atlas, even when regional MRI intensities are inconsistent or highly variable between subjects. The framework allows for creation of an anatomic imaging and a disease atlas, while enabling their fusion into the anatomic imaging-disease atlas. The atlas presented here was constructed using 83 subjects with biopsy confirmed cancer who had pre-operative MRI (collected at two institutions) followed by radical prostatectomy. The imaging atlas results from mapping thein vivo MRI into the canonical space, while the anatomic regions serve as domain constraints. Elastic co-registration MRI and corresponding ex vivo histology provides "ground truth" mapping of cancer extent on in vivo imaging for 23 subjects. RESULTS AnCoR was evaluated relative to alternative construction strategies that use either MRI intensities or the prostate surface alone for registration. The AnCoR framework yielded a central gland Dice similarity coefficient (DSC) of 90%, and prostate DSC of 88%, while the misalignment of the urethra and verumontanum was found to be 3.45 mm, and 4.73 mm, respectively, which were measured to be significantly smaller compared to the alternative strategies. As might have been anticipated from our limited cohort of biopsy confirmed cancers, the disease atlas showed that most of the tumor extent was limited to the peripheral zone. Moreover, central gland tumors were typically larger in size, possibly because they are only discernible at a much later stage. CONCLUSIONS The authors presented the AnCoR framework to explicitly model anatomic constraints for the construction of a fused anatomic imaging-disease atlas. The framework was applied to constructing a preliminary version of an anatomic-disease atlas of the prostate, the prostatome. The prostatome could facilitate the quantitative characterization of gland morphology and imaging features of prostate cancer. These techniques, may be applied on a large sample size data set to create a fully developed prostatome that could serve as a spatial prior for targeted biopsies by urologists. Additionally, the AnCoR framework could allow for incorporation of complementary imaging and molecular data, thereby enabling their careful correlation for population based radio-omics studies.
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Affiliation(s)
- Mirabela Rusu
- Case Western Reserve University, Cleveland, Ohio 44106
| | - B Nicolas Bloch
- Boston University School of Medicine, Boston, Massachusetts 02118
| | - Carl C Jaffe
- Boston University School of Medicine, Boston, Massachusetts 02118
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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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