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Jones RE, Zaidi HA, Hammersley DJ, Hatipoglu S, Owen R, Balaban G, de Marvao A, Simard F, Lota AS, Mahon C, Almogheer B, Mach L, Musella F, Chen X, Gregson J, Lazzari L, Ravendren A, Leyva F, Zhao S, Vazir A, Lamata P, Halliday BP, Pennell DJ, Bishop MJ, Prasad SK. Comprehensive Phenotypic Characterization of Late Gadolinium Enhancement Predicts Sudden Cardiac Death in Coronary Artery Disease. JACC Cardiovasc Imaging 2023; 16:628-638. [PMID: 36752426 PMCID: PMC10151254 DOI: 10.1016/j.jcmg.2022.10.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 10/24/2022] [Accepted: 10/27/2022] [Indexed: 01/12/2023]
Abstract
BACKGROUND Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) offers the potential to noninvasively characterize the phenotypic substrate for sudden cardiac death (SCD). OBJECTIVES The authors assessed the utility of infarct characterization by CMR, including scar microstructure analysis, to predict SCD in patients with coronary artery disease (CAD). METHODS Patients with stable CAD were prospectively recruited into a CMR registry. LGE quantification of core infarction and the peri-infarct zone (PIZ) was performed alongside computational image analysis to extract morphologic and texture scar microstructure features. The primary outcome was SCD or aborted SCD. RESULTS Of 437 patients (mean age: 64 years; mean left ventricular ejection fraction [LVEF]: 47%) followed for a median of 6.3 years, 49 patients (11.2%) experienced the primary outcome. On multivariable analysis, PIZ mass and core infarct mass were independently associated with the primary outcome (per gram: HR: 1.07 [95% CI: 1.02-1.12]; P = 0.002 and HR: 1.03 [95% CI: 1.01-1.05]; P = 0.01, respectively), and the addition of both parameters improved discrimination of the model (Harrell's C-statistic: 0.64-0.79). PIZ mass, however, did not provide incremental prognostic value over core infarct mass based on Harrell's C-statistic or risk reclassification analysis. Severely reduced LVEF did not predict the primary endpoint after adjustment for scar mass. On scar microstructure analysis, the number of LGE islands in addition to scar transmurality, radiality, interface area, and entropy were all associated with the primary outcome after adjustment for severely reduced LVEF and New York Heart Association functional class of >1. No scar microstructure feature remained associated with the primary endpoint when PIZ mass and core infarct mass were added to the regression models. CONCLUSIONS Comprehensive LGE characterization independently predicted SCD risk beyond conventional predictors used in implantable cardioverter-defibrillator (ICD) insertion guidelines. These results signify the potential for a more personalized approach to determining ICD candidacy in CAD.
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Affiliation(s)
- Richard E Jones
- National Heart and Lung Institute, Imperial College London, United Kingdom; Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom. https://twitter.com/DrREJones
| | - Hassan A Zaidi
- Department of Biomedical Engineering, School of Biomedical & Imaging Sciences, King's College London, United Kingdom
| | - Daniel J Hammersley
- National Heart and Lung Institute, Imperial College London, United Kingdom; Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Suzan Hatipoglu
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Ruth Owen
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Gabriel Balaban
- Department of Biomedical Engineering, School of Biomedical & Imaging Sciences, King's College London, United Kingdom; Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Antonio de Marvao
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom; Department of Women and Children's Health, King's College London, London, United Kingdom; British Heart Foundation Centre of Research Excellence, School of Cardiovascular Medicine and Sciences, King's College London, London, United Kingdom
| | - François Simard
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Amrit S Lota
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Ciara Mahon
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Batool Almogheer
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Lukas Mach
- National Heart and Lung Institute, Imperial College London, United Kingdom; Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Francesca Musella
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Xiuyu Chen
- State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - John Gregson
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Laura Lazzari
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Andrew Ravendren
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Francisco Leyva
- Aston Medical School, Aston University, Birmingham, United Kingdom
| | - Shihua Zhao
- State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ali Vazir
- National Heart and Lung Institute, Imperial College London, United Kingdom
| | - Pablo Lamata
- Department of Biomedical Engineering, School of Biomedical & Imaging Sciences, King's College London, United Kingdom
| | - Brian P Halliday
- National Heart and Lung Institute, Imperial College London, United Kingdom; Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Dudley J Pennell
- National Heart and Lung Institute, Imperial College London, United Kingdom; Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Martin J Bishop
- Department of Biomedical Engineering, School of Biomedical & Imaging Sciences, King's College London, United Kingdom
| | - Sanjay K Prasad
- National Heart and Lung Institute, Imperial College London, United Kingdom; Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom.
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Zaidi HA, Jones RE, Hammersley DJ, Hatipoglu S, Balaban G, Mach L, Halliday BP, Lamata P, Prasad SK, Bishop MJ. Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events. Front Cardiovasc Med 2023; 10:1082778. [PMID: 36824460 PMCID: PMC9941157 DOI: 10.3389/fcvm.2023.1082778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/13/2023] [Indexed: 02/10/2023] Open
Abstract
Background Machine learning analysis of complex myocardial scar patterns affords the potential to enhance risk prediction of life-threatening arrhythmia in stable coronary artery disease (CAD). Objective To assess the utility of computational image analysis, alongside a machine learning (ML) approach, to identify scar microstructure features on late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) that predict major arrhythmic events in patients with CAD. Methods Patients with stable CAD were prospectively recruited into a CMR registry. Shape-based scar microstructure features characterizing heterogeneous ('peri-infarct') and homogeneous ('core') fibrosis were extracted. An ensemble of machine learning approaches were used for risk stratification, in addition to conventional analysis using Cox modeling. Results Of 397 patients (mean LVEF 45.4 ± 16.0) followed for a median of 6 years, 55 patients (14%) experienced a major arrhythmic event. When applied within an ML model for binary classification, peri-infarct zone (PIZ) entropy, peri-infarct components and core interface area outperformed a model representative of the current standard of care (LVEF<35% and NYHA>Class I): AUROC (95%CI) 0.81 (0.81-0.82) vs. 0.64 (0.63-0.65), p = 0.002. In multivariate cox regression analysis, these features again remained significant after adjusting for LVEF<35% and NYHA>Class I: PIZ entropy hazard ratio (HR) 1.88, 95% confidence interval (CI) 1.38-2.56, p < 0.001; number of PIZ components HR 1.34, 95% CI 1.08-1.67, p = 0.009; core interface area HR 1.6, 95% CI 1.29-1.99, p = <0.001. Conclusion Machine learning models using LGE-CMR scar microstructure improved arrhythmic risk stratification as compared to guideline-based clinical parameters; highlighting a potential novel approach to identifying candidates for implantable cardioverter defibrillators in stable CAD.
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Affiliation(s)
- Hassan A. Zaidi
- Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom
| | - Richard E. Jones
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Daniel J. Hammersley
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Suzan Hatipoglu
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Gabriel Balaban
- Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Lukas Mach
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Brian P. Halliday
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Pablo Lamata
- Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sanjay K. Prasad
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Martin J. Bishop
- Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom
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Wu KC. Myocardial Tissue Characterization to Predict Ventricular Arrhythmic Risk: Road Well-Traveled But So Far to Go. JACC Cardiovasc Imaging 2023; 16:639-641. [PMID: 36707355 PMCID: PMC10159956 DOI: 10.1016/j.jcmg.2022.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 12/13/2022] [Indexed: 01/26/2023]
Affiliation(s)
- Katherine C Wu
- Division of Cardiology, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA.
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Bhagirath P, Campos FO, Costa CM, Wilde AAM, Prassl AJ, Neic A, Plank G, Rinaldi CA, Götte MJW, Bishop MJ. Predicting arrhythmia recurrence following catheter ablation for ventricular tachycardia using late gadolinium enhancement magnetic resonance imaging: Implications of varying scar ranges. Heart Rhythm 2022; 19:1604-1610. [PMID: 35644355 PMCID: PMC7616170 DOI: 10.1016/j.hrthm.2022.05.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/09/2022] [Accepted: 05/16/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND Thresholding-based analysis of late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) can create scar maps and identify corridors that might provide a reentrant substrate for ventricular tachycardia (VT). Current recommendations use a full-width-at-half-maximum approach, effectively classifying areas with a pixel signal intensity (PSI) >40% as border zone (BZ) and >60% as core. OBJECTIVE The purpose of this study was to investigate the impact of 4 different threshold settings on scar and corridor quantification and to correlate this with postablation VT recurrence. METHODS Twenty-seven patients with ischemic cardiomyopathy who had undergone catheter ablation for VT were included for retrospective analysis. LGE-CMR images were analyzed using ADAS3D LV. Scar maps were created for 4 PSI thresholds (40-60, 35-65, 30-70, and 45-55), and the extent of variation in BZ and core, as well as the number and weight of conduction corridors, were quantified. Three-dimensional representations were reconstructed from exported segmentations and used to quantify the surface area between healthy myocardium and scar (BZ + core), and between BZ and core. RESULTS A wider PSI threshold was associated with an increase in BZ mass and decrease in scar (P <.001). No significant differences were observed for the total number of corridors and their mass with increasing PSI threshold. The best correlation in predicting arrhythmia recurrence was observed for PSI 45-55 (area under the curve 0.807; P = .001). CONCLUSION Varying PSI has a significant impact on quantification of LGE-CMR parameters and may have incremental clinical value in predicting arrhythmia recurrence. Further prospective investigation is warranted to clarify the functional implications of these findings for LGE-CMR-guided ventricular ablation.
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Affiliation(s)
- Pranav Bhagirath
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Cardiology, St. Thomas' Hospital, London, United Kingdom.
| | - Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Caroline M Costa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Arthur A M Wilde
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Aurel Neic
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Gernot Plank
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | | | - Marco J W Götte
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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ACR Appropriateness Criteria® Dyspnea-Suspected Cardiac Origin (Ischemia Already Excluded): 2021 Update. J Am Coll Radiol 2022; 19:S37-S52. [PMID: 35550804 DOI: 10.1016/j.jacr.2022.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 02/19/2022] [Indexed: 11/20/2022]
Abstract
Dyspnea is the symptom of perceived breathing discomfort and is commonly encountered in a variety of clinical settings. Cardiac etiologies of dyspnea are an important consideration; among these, valvular heart disease (Variant 1), arrhythmia (Variant 2), and pericardial disease (Variant 3) are reviewed in this document. Imaging plays an important role in the clinical assessment of these suspected abnormalities, with usually appropriate procedures including resting transthoracic echocardiography in all three variants, radiography for Variants 1 and 3, MRI heart function and morphology in Variants 2 and 3, and CT heart function and morphology with intravenous contrast for Variant 3. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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Balaban G, Halliday BP, Porter B, Bai W, Nygåard S, Owen R, Hatipoglu S, Ferreira ND, Izgi C, Tayal U, Corden B, Ware J, Pennell DJ, Rueckert D, Plank G, Rinaldi CA, Prasad SK, Bishop MJ. Late-Gadolinium Enhancement Interface Area and Electrophysiological Simulations Predict Arrhythmic Events in Patients With Nonischemic Dilated Cardiomyopathy. JACC Clin Electrophysiol 2021; 7:238-249. [PMID: 33602406 PMCID: PMC7900608 DOI: 10.1016/j.jacep.2020.08.036] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 08/18/2020] [Accepted: 08/19/2020] [Indexed: 11/16/2022]
Abstract
OBJECTIVES This study sought to investigate whether shape-based late gadolinium enhancement (LGE) metrics and simulations of re-entrant electrical activity are associated with arrhythmic events in patients with nonischemic dilated cardiomyopathy (NIDCM). BACKGROUND The presence of LGE predicts life-threatening ventricular arrhythmias in NIDCM; however, risk stratification remains imprecise. LGE shape and simulations of electrical activity may be able to provide additional prognostic information. METHODS Cardiac magnetic resonance (CMR)-LGE shape metrics were computed for a cohort of 156 patients with NIDCM and visible LGE and tested retrospectively for an association with an arrhythmic composite endpoint of sudden cardiac death and ventricular tachycardia. Computational models were created from images and used in conjunction with simulated stimulation protocols to assess the potential for re-entry induction in each patient's scar morphology. A mechanistic analysis of the simulations was carried out to explain the associations. RESULTS During a median follow-up of 1,611 (interquartile range: 881 to 2,341) days, 16 patients (10.3%) met the primary endpoint. In an inverse probability weighted Cox regression, the LGE-myocardial interface area (hazard ratio [HR]: 1.75; 95% confidence interval [CI]: 1.24 to 2.47; p = 0.001), number of simulated re-entries (HR: 1.40; 95% CI: 1.23 to 1.59; p < 0.01) and LGE volume (HR: 1.44; 95% CI: 1.07 to 1.94; p = 0.02) were associated with arrhythmic events. Computational modeling revealed repolarization heterogeneity and rate-dependent block of electrical wavefronts at the LGE-myocardial interface as putative arrhythmogenic mechanisms directly related to the LGE interface area. CONCLUSIONS The area of interface between scar and surviving myocardium, as well as simulated re-entrant activity, are associated with an elevated risk of major arrhythmic events in patients with NIDCM and LGE and represent novel risk predictors.
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Affiliation(s)
- Gabriel Balaban
- Department of Biomedical Engineering, School of Biomedical & Imaging Sciences, King's College London, United Kingdom; Department of Informatics, University of Oslo, Oslo, Norway
| | - Brian P Halliday
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Bradley Porter
- Department of Biomedical Engineering, School of Biomedical & Imaging Sciences, King's College London, United Kingdom; Department of Cardiology, St Thomas' Hospital, London, United Kingdom
| | - Wenjia Bai
- Department of Computer Science, Imperial College London, United Kingdom
| | - Ståle Nygåard
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Ruth Owen
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Suzan Hatipoglu
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, United Kingdom
| | - Nuno Dias Ferreira
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, United Kingdom
| | - Cemil Izgi
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, United Kingdom
| | - Upasana Tayal
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, United Kingdom
| | - Ben Corden
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - James Ware
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Dudley J Pennell
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Daniel Rueckert
- Department of Computer Science, Imperial College London, United Kingdom
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Christopher A Rinaldi
- Department of Biomedical Engineering, School of Biomedical & Imaging Sciences, King's College London, United Kingdom; Department of Cardiology, St Thomas' Hospital, London, United Kingdom
| | - Sanjay K Prasad
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Martin J Bishop
- Department of Biomedical Engineering, School of Biomedical & Imaging Sciences, King's College London, United Kingdom.
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