Ventricle Surface Reconstruction from Cardiac MR Slices Using Deep Learning

Reconstructing 3D ventricular surfaces from 2D cardiac MR data is challenging due to the sparsity of the input data and the presence of interslice misalignment. It is usually formulated as a 3D mesh fitting problem often incorporating shape priors and smoothness regularization, which might affect accuracy when handling pathological cases. We propose to formulate the 3D reconstruction as a volumetric mapping problem followed by isosurfacing from dense volumetric data. Taking advantage of deep learning algorithms, which learn to predict each voxel label without explicitly defining the shapes, our method is capable of generating anatomically meaningful surfaces with great flexibility. The sparse 3D volumetric input can process contours with any orientations and thus can utilize information from multiple short- and long-axis views. In addition, our method can provide correction of motion artifacts. We have validated our method using a statistical shape model on reconstructing 3D shapes from both spatially consistent and misaligned input data

Electrocardiogram phenotypes in hypertrophic cardiomyopathy caused by distinct mechanisms: apico-basal repolarization gradients vs. Purkinje-myocardial coupling abnormalities

Aims To identify key structural and electrophysiological features explaining distinct electrocardiogram (ECG) phenotypes in hypertrophic cardiomyopathy (HCM). Methods and results Human heart–torso anatomical models were constructed from cardiac magnetic resonance (CMR) images of HCM patients, representative of ECG phenotypes identified previously. High performance computing simulations using bidomain models were conducted to dissect key features explaining the ECG phenotypes with increased HCM Risk-SCD scores, namely Group 1A, characterized by normal QRS but inverted T waves laterally and coexistence of apical and septal hypertrophy; and Group 3 with marked QRS abnormalities (deep and wide S waves laterally) and septal hypertrophy. Hypertrophic cardiomyopathy abnormalities characterized from CMR, such as hypertrophy, tissue microstructure alterations, abnormal conduction system, and ionic remodelling, were selectively included to assess their influence on ECG morphology. Electrocardiogram abnormalities could not be explained by increased wall thickness nor by local conduction abnormalities associated with fibre disarray or fibrosis. Inverted T wave with normal QRS (Group 1A) was obtained with increased apico-basal repolarization gradient caused by ionic remodelling in septum and apex. Lateral QRS abnormalities (Group 3) were only recovered with abnormal Purkinje-myocardium coupling. Conclusion Two ECG-based HCM phenotypes are explained by distinct mechanisms: ionic remodelling and action potential prolongation in hypertrophied apical and septal areas lead to T wave inversion with normal QRS complexes, whereas abnormal Purkinje-myocardial coupling causes abnormal QRS morphology in V4–V6. These findings have potential implications for patients’ management as they point towards different arrhythmia mechanisms in different phenotypes

SMOD - data augmentation based on statistical models of deformation to enhance segmentation in 2D cine cardiac MRI

Deep learning has revolutionized medical image analysis in recent years. Nevertheless, technical, ethical and financial constraints along with confidentiality issues still limit data availability, and therefore the performance of these approaches. To overcome such limitations, data augmentation has proven crucial. Here we propose SMOD, a novel augmentation methodology based on Statistical Models of Deformations, to segment 2D cine scans in cardiac MRI. In brief, the shape variability of the training set space is modelled so new images with the appearance of the original ones but unseen shapes within the space of plausible realistic shapes are generated. SMOD is compared to standard augmentation providing quantitative improvement, especially when the training data available is very limited or the structures to segment are complex and highly variable. We finally propose a state-of-art, deep learning 2D cardiac MRI segmenter for normal and hypertrophic cardiomyopathy hearts with an epicardium and endocardium mean Dice score of 0.968 in short and long axis

Left ventricle quantification with cardiac MRI: deep learning meets statistical models of deformation

Deep learning has been widely applied for left ventricle (LV) analysis, obtaining state of the art results in quantification through image segmentation. When the training datasets are limited, data augmentation becomes critical, but standard augmentation methods do not usually incorporate the natural variation of anatomy. In this paper we propose a pipeline for LV quantification applying our data augmentation methodology based on statistical models of deformations (SMOD) to quantify LV based on segmentation of cardiac MR (CMR) images, and present an in-depth analysis of the effects of deformation parameters in SMOD performance. We trained and evaluated our pipeline on the MICCAI 2019 Left Ventricle Full Quantification Challenge dataset, and achieved average mean absolute error (MAE) for areas, dimensions, regional wall thickness and phase of 106 mm2, 1.52 mm, 1.01 mm and 8.0% respectively in a 3-fold cross-validation experiment

Electrocardiogram phenotypes in hypertrophic cardiomyopathy caused by distinct mechanisms: apico-basal repolarization gradients vs. Purkinje-myocardial coupling abnormalities

Aims To identify key structural and electrophysiological features explaining distinct electrocardiogram (ECG) phenotypes in hypertrophic cardiomyopathy (HCM). Methods and results Human heart–torso anatomical models were constructed from cardiac magnetic resonance (CMR) images of HCM patients, representative of ECG phenotypes identified previously. High performance computing simulations using bidomain models were conducted to dissect key features explaining the ECG phenotypes with increased HCM Risk-SCD scores, namely Group 1A, characterized by normal QRS but inverted T waves laterally and coexistence of apical and septal hypertrophy; and Group 3 with marked QRS abnormalities (deep and wide S waves laterally) and septal hypertrophy. Hypertrophic cardiomyopathy abnormalities characterized from CMR, such as hypertrophy, tissue microstructure alterations, abnormal conduction system, and ionic remodelling, were selectively included to assess their influence on ECG morphology. Electrocardiogram abnormalities could not be explained by increased wall thickness nor by local conduction abnormalities associated with fibre disarray or fibrosis. Inverted T wave with normal QRS (Group 1A) was obtained with increased apico-basal repolarization gradient caused by ionic remodelling in septum and apex. Lateral QRS abnormalities (Group 3) were only recovered with abnormal Purkinje-myocardium coupling. Conclusion Two ECG-based HCM phenotypes are explained by distinct mechanisms: ionic remodelling and action potential prolongation in hypertrophied apical and septal areas lead to T wave inversion with normal QRS complexes, whereas abnormal Purkinje-myocardial coupling causes abnormal QRS morphology in V4–V6. These findings have potential implications for patients’ management as they point towards different arrhythmia mechanisms in different phenotypes.</p

Understanding and improving risk assessment after myocardial infarction using automated left ventricular shape analysis

Objectives This study aimed to identify novel 3-dimensional (3D) imaging end-systolic (ES) shape and contraction descriptors toward risk-related features and superior prognosis in acute myocardial infarction (AMI). Background Left ventricular ejection fraction (LVEF) and end-systolic volume (ESV) remain the main imaging biomarkers for post-AMI risk stratification. However, they are limited to global systolic function and fail to capture functional and anatomical regional abnormalities, hindering their performance in risk stratification. Methods A multicenter cohort of AMI survivors (n = 1,021; median age 63 years; 74.5% male) who underwent cardiac magnetic resonance (CMR) at a median of 3 days after infarction were considered for this study. The clinical end point was the 12-month rate of major adverse cardiac events (MACE; n = 73), consisting of all-cause death, reinfarction, and new congestive heart failure. A fully automated pipeline was developed to segment CMR images, build 3D statistical models of shape and contraction in AMI, and find the 3D patterns related to MACE occurrence. Results The novel ES shape markers proved to be superior to ESV (median cross-validated area under the receiver-operating characteristic curve 0.681 [IQR 0.679-0.684] vs 0.600 [0.598-0.602]; P Conclusions The quantification of 3D differences in ES shape and contraction, enabled by a fully automated pipeline, improves post-AMI risk prediction and identifies shape and contraction patterns related to MACE occurrence.</p