High-Resolution Whole-Heart Imaging and Modeling for Studying Cardiac Arrhythmia

Cardiac arrhythmia is a life-threatening heart rhythm disorder affecting millions of people worldwide. The underlying structure of the heart plays an important role in cardiac activity and could promote rhythm disorders. Accurate knowledge of whole-heart cardiac geometry and microstructure in normal and disease hearts is essential for a complete understanding of the mechanisms of arrhythmias. This dissertation presents novel structural data at the whole-heart level aimed at advancing knowledge of cardiac structure in normal and infarcted hearts, and at constructing whole-heart computational models. A 3D diffusion tensor MRI (DTMRI) technique was implemented on a clinical scanner to image intact large animal and human hearts with high image quality and spatial resolution ex vivo. This method was first applied to reconstruct the 3D myofiber organization in 8 human atria nondestructively and at submillimeter resolution. The findings showed that the main features of atrial anatomy are mostly preserved across subjects despite variability in the exact location and orientation of the bundles. Further, we were able to cluster, visualize, and characterize the distinct major bundles in the human atria. Quantitative analysis of the fiber angles across the atrial wall revealed that the transmural fiber angle distribution is heterogeneous throughout the atria. We next studied microstructural remodeling in infarcted porcine and human hearts by combining DTMRI with high-resolution Late Gadolinium Enhancement imaging. This enabled us to provide reconstructions of both fiber architecture and scar distribution in infarcted hearts with an unprecedented level of detail, and to systematically quantify the transmural pattern of diffusion eigenvector orientation. Our results demonstrated that the fiber orientation is generally preserved inside the scar but at a higher transmural gradient of inclination angle. Lastly, we employed the obtained data to generate whole-heart computational models of infarcted hearts with detailed scar geometry and subject-specific fiber orientation. We used these models in simulations to investigate the contribution of the infarct microarchitecture to ventricular tachycardia. The simulation results showed that the reentry circuits traverse thin viable tissues with complex geometries located inside of the infarct. The high resolution of the images enabled 3D reconstruction and characterization of such structures

Accuracy of prediction of infarct-related arrhythmic circuits from image-based models reconstructed from low and high resolution MRI.

Identification of optimal ablation sites in hearts with infarct-related ventricular tachycardia (VT) remains difficult to achieve with the current catheter-based mapping techniques. Limitations arise from the ambiguities in determining the reentrant pathways location(s). The goal of this study was to develop experimentally validated, individualized computer models of infarcted swine hearts, reconstructed from high-resolution ex-vivo MRI and to examine the accuracy of the reentrant circuit location prediction when models of the same hearts are instead reconstructed from low clinical-resolution MRI scans. To achieve this goal, we utilized retrospective data obtained from four pigs ~10 weeks post infarction that underwent VT induction via programmed stimulation and epicardial activation mapping via a multielectrode epicardial sock. After the experiment, high-resolution ex-vivo MRI with late gadolinium enhancement was acquired. The Hi-res images were downsampled into two lower resolutions (Med-res and Low-res) in order to replicate image quality obtainable in the clinic. The images were segmented and models were reconstructed from the three image stacks for each pig heart. VT induction similar to what was performed in the experiment was simulated. Results of the reconstructions showed that the geometry of the ventricles including the infarct could be accurately obtained from Med-res and Low-res images. Simulation results demonstrated that induced VTs in the Med-res and Low-res models were located close to those in Hi-res models. Importantly, all models, regardless of image resolution, accurately predicted the VT morphology and circuit location induced in the experiment. These results demonstrate that MRI-based computer models of hearts with ischemic cardiomyopathy could provide a unique opportunity to predict and analyze VT resulting for from specific infarct architecture, and thus may assist in clinical decisions to identify and ablate the reentrant circuit(s)

Submillimeter diffusion tensor imaging and late gadolinium enhancement cardiovascular magnetic resonance of chronic myocardial infarction.

BackgroundKnowledge of the three-dimensional (3D) infarct structure and fiber orientation remodeling is essential for complete understanding of infarct pathophysiology and post-infarction electromechanical functioning of the heart. Accurate imaging of infarct microstructure necessitates imaging techniques that produce high image spatial resolution and high signal-to-noise ratio (SNR). The aim of this study is to provide detailed reconstruction of 3D chronic infarcts in order to characterize the infarct microstructural remodeling in porcine and human hearts.MethodsWe employed a customized diffusion tensor imaging (DTI) technique in conjunction with late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) on a 3T clinical scanner to image, at submillimeter resolution, myofiber orientation and scar structure in eight chronically infarcted porcine hearts ex vivo. Systematic quantification of local microstructure was performed and the chronic infarct remodeling was characterized at different levels of wall thickness and scar transmurality. Further, a human heart with myocardial infarction was imaged using the same DTI sequence.ResultsThe SNR of non-diffusion-weighted images was >100 in the infarcted and control hearts. Mean diffusivity and fractional anisotropy (FA) demonstrated a 43% increase, and a 35% decrease respectively, inside the scar tissue. Despite this, the majority of the scar showed anisotropic structure with FA higher than an isotropic liquid. The analysis revealed that the primary eigenvector orientation at the infarcted wall on average followed the pattern of original fiber orientation (imbrication angle mean: 1.96 ± 11.03° vs. 0.84 ± 1.47°, p = 0.61, and inclination angle range: 111.0 ± 10.7° vs. 112.5 ± 6.8°, p = 0.61, infarcted/control wall), but at a higher transmural gradient of inclination angle that increased with scar transmurality (r = 0.36) and the inverse of wall thickness (r = 0.59). Further, the infarcted wall exhibited a significant increase in both the proportion of left-handed epicardial eigenvectors, and in the angle incoherency. The infarcted human heart demonstrated preservation of primary eigenvector orientation at the thinned region of infarct, consistent with the findings in the porcine hearts.ConclusionsThe application of high-resolution DTI and LGE-CMR revealed the detailed organization of anisotropic infarct structure at a chronic state. This information enhances our understanding of chronic post-infarction remodeling in large animal and human hearts

Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI

Purpose Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath‐holding difficulty or non‐sinus rhythms. To reduce scan time, we propose a multi‐domain convolutional neural network (MD‐CNN) for fast reconstruction of highly undersampled radial cine images. Methods MD‐CNN is a complex‐valued network that processes MR data in k‐space and image domains via k‐space interpolation and image‐domain subnetworks for residual artifact suppression. MD‐CNN exploits spatio‐temporal correlations across timeframes and multi‐coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective‐gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD‐CNN and k‐t Radial Sparse‐Sense(kt‐RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD‐CNN images were evaluated quantitatively using mean‐squared‐error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5‐point Likert‐scale (1‐non‐diagnostic, 2‐poor, 3‐fair, 4‐good, and 5‐excellent). Results MD‐CNN showed improved MSE and SSIM compared to kt‐RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD‐CCN significantly outperformed kt‐RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end‐diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end‐systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01). Conclusion MD‐CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt‐RASPS

Accuracy of prediction of infarct-related arrhythmic circuits from image-based models reconstructed from low and high resolution MRI.

Identification of optimal ablation sites in hearts with infarct-related ventricular tachycardia (VT) remains difficult to achieve with the current catheter-based mapping techniques. Limitations arise from the ambiguities in determining the reentrant pathways location(s). The goal of this study was to develop experimentally validated, individualized computer models of infarcted swine hearts, reconstructed from high-resolution ex-vivo MRI and to examine the accuracy of the reentrant circuit location prediction when models of the same hearts are instead reconstructed from low clinical-resolution MRI scans. To achieve this goal, we utilized retrospective data obtained from four pigs ~10 weeks post infarction that underwent VT induction via programmed stimulation and epicardial activation mapping via a multielectrode epicardial sock. After the experiment, high-resolution ex-vivo MRI with late gadolinium enhancement was acquired. The Hi-res images were downsampled into two lower resolutions (Med-res and Low-res) in order to replicate image quality obtainable in the clinic. The images were segmented and models were reconstructed from the three image stacks for each pig heart. VT induction similar to what was performed in the experiment was simulated. Results of the reconstructions showed that the geometry of the ventricles including the infarct could be accurately obtained from Med-res and Low-res images. Simulation results demonstrated that induced VTs in the Med-res and Low-res models were located close to those in Hi-res models. Importantly, all models, regardless of image resolution, accurately predicted the VT morphology and circuit location induced in the experiment. These results demonstrate that MRI-based computer models of hearts with ischemic cardiomyopathy could provide a unique opportunity to predict and analyze VT resulting for from specific infarct architecture, and thus may assist in clinical decisions to identify and ablate the reentrant circuit(s)

Submillimeter diffusion tensor imaging and late gadolinium enhancement cardiovascular magnetic resonance of chronic myocardial infarction.

BackgroundKnowledge of the three-dimensional (3D) infarct structure and fiber orientation remodeling is essential for complete understanding of infarct pathophysiology and post-infarction electromechanical functioning of the heart. Accurate imaging of infarct microstructure necessitates imaging techniques that produce high image spatial resolution and high signal-to-noise ratio (SNR). The aim of this study is to provide detailed reconstruction of 3D chronic infarcts in order to characterize the infarct microstructural remodeling in porcine and human hearts.MethodsWe employed a customized diffusion&nbsp;tensor imaging (DTI) technique in conjunction with late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) on a 3T clinical scanner to image, at submillimeter resolution, myofiber orientation and scar structure in eight chronically infarcted porcine hearts ex vivo. Systematic quantification of local microstructure was performed and the chronic infarct remodeling was characterized at different levels of wall thickness and scar transmurality. Further, a human heart with myocardial infarction was imaged using the same DTI sequence.ResultsThe SNR of non-diffusion-weighted images was &gt;100 in the infarcted and control hearts. Mean diffusivity and fractional anisotropy (FA) demonstrated a 43% increase, and a 35% decrease respectively, inside the scar tissue. Despite this, the majority of the scar showed anisotropic structure with FA higher than an isotropic liquid. The analysis revealed that the primary eigenvector orientation at the infarcted wall on average followed the pattern of original fiber orientation (imbrication angle mean: 1.96 ± 11.03° vs. 0.84 ± 1.47°, p = 0.61, and inclination angle range: 111.0 ± 10.7° vs. 112.5 ± 6.8°, p = 0.61, infarcted/control wall), but at a higher transmural gradient of inclination angle that increased with scar transmurality (r = 0.36) and the inverse of wall thickness (r = 0.59). Further, the infarcted wall exhibited a significant increase in both the proportion of left-handed epicardial eigenvectors, and in the angle incoherency. The infarcted human heart demonstrated preservation of primary eigenvector orientation at the thinned region of infarct, consistent with the findings in the porcine hearts.ConclusionsThe application of high-resolution DTI and LGE-CMR revealed the detailed organization of anisotropic infarct structure at a chronic state. This information enhances our understanding of chronic post-infarction remodeling in large animal and human hearts

Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI

Purpose Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath‐holding difficulty or non‐sinus rhythms. To reduce scan time, we propose a multi‐domain convolutional neural network (MD‐CNN) for fast reconstruction of highly undersampled radial cine images. Methods MD‐CNN is a complex‐valued network that processes MR data in k‐space and image domains via k‐space interpolation and image‐domain subnetworks for residual artifact suppression. MD‐CNN exploits spatio‐temporal correlations across timeframes and multi‐coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective‐gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD‐CNN and k‐t Radial Sparse‐Sense(kt‐RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD‐CNN images were evaluated quantitatively using mean‐squared‐error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5‐point Likert‐scale (1‐non‐diagnostic, 2‐poor, 3‐fair, 4‐good, and 5‐excellent). Results MD‐CNN showed improved MSE and SSIM compared to kt‐RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD‐CCN significantly outperformed kt‐RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end‐diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end‐systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01). Conclusion MD‐CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt‐RASPS