Farhad Pashakhanloo

Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology.

Patient-specific modeling of ventricular electrophysiology requires an interpolated reconstruction of the 3-dimensional (3D) geometry of the patient ventricles from the low-resolution (Lo-res) clinical images. The goal of this study was to implement a processing pipeline for obtaining the interpolated reconstruction, and thoroughly evaluate the efficacy of this pipeline in comparison with alternative methods. The pipeline implemented here involves contouring the epi- and endocardial boundaries in Lo-res images, interpolating the contours using the variational implicit functions method, and merging the interpolation results to obtain the ventricular reconstruction. Five alternative interpolation methods, namely linear, cubic spline, spherical harmonics, cylindrical harmonics, and shape-based interpolation were implemented for comparison. In the thorough evaluation of the processing pipeline, Hi-res magnetic resonance (MR), computed tomography (CT), and diffusion tensor (DT) MR images from numerous hearts were used. Reconstructions obtained from the Hi-res images were compared with the reconstructions computed by each of the interpolation methods from a sparse sample of the Hi-res contours, which mimicked Lo-res clinical images. Qualitative and quantitative comparison of these ventricular geometry reconstructions showed that the variational implicit functions approach performed better than others. Additionally, the outcomes of electrophysiological simulations (sinus rhythm activation maps and pseudo-ECGs) conducted using models based on the various reconstructions were compared. These electrophysiological simulations demonstrated that our implementation of the variational implicit functions-based method had the best accuracy.

Myofiber Architecture of the Human Atria as Revealed by Submillimeter Diffusion Tensor Imaging

Background—Accurate knowledge of the human atrial fibrous structure is paramount in understanding the mechanisms of atrial electric function in health and disease. Thus far, such knowledge has been acquired from destructive sectioning, and there is a paucity of data about atrial fiber architecture variability in the human population. Methods and Results—In this study, we have developed a customized 3-dimensional diffusion tensor magnetic resonance imaging sequence on a clinical scanner that makes it possible to image an entire intact human heart specimen ex vivo at submillimeter resolution. The data from 8 human atrial specimens obtained with this technique present complete maps of the fibrous organization of the human atria. The findings demonstrate that the main features of atrial anatomy are mostly preserved across subjects although the exact location and orientation of atrial bundles vary. Using the full tractography data, we were able to cluster, visualize, and characterize the distinct major bundles in the human atria. Furthermore, quantitative characterization of the fiber angles across the atrial wall revealed that the transmural fiber angle distribution is heterogeneous throughout different regions of the atria. Conclusions—The application of submillimeter diffusion tensor magnetic resonance imaging provides an unprecedented level of information on both human atrial structure, as well as its intersubject variability. The high resolution and fidelity of this data could enhance our understanding of structural contributions to atrial rhythm and pump disorders and lead to improvements in their targeted treatment.

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).

Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology.

PURPOSE Accurate three-dimensional (3D) reconstruction of myocardial infarct geometry is crucial to patient-specific modeling of the heart aimed at providing therapeutic guidance in ischemic cardiomyopathy. However, myocardial infarct imaging is clinically performed using two-dimensional (2D) late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) techniques, and a method to build accurate 3D infarct reconstructions from the 2D LGE-CMR images has been lacking. The purpose of this study was to address this need. METHODS The authors developed a novel methodology to reconstruct 3D infarct geometry from segmented low-resolution (Lo-res) clinical LGE-CMR images. Their methodology employed the so-called logarithm of odds (LogOdds) function to implicitly represent the shape of the infarct in segmented image slices as LogOdds maps. These 2D maps were then interpolated into a 3D image, and the result transformed via the inverse of LogOdds to a binary image representing the 3D infarct geometry. To assess the efficacy of this method, the authors utilized 39 high-resolution (Hi-res) LGE-CMR images, including 36 in vivo acquisitions of human subjects with prior myocardial infarction and 3 ex vivo scans of canine hearts following coronary ligation to induce infarction. The infarct was manually segmented by trained experts in each slice of the Hi-res images, and the segmented data were downsampled to typical clinical resolution. The proposed method was then used to reconstruct 3D infarct geometry from the downsampled images, and the resulting reconstructions were compared with the manually segmented data. The method was extensively evaluated using metrics based on geometry as well as results of electrophysiological simulations of cardiac sinus rhythm and ventricular tachycardia in individual hearts. Several alternative reconstruction techniques were also implemented and compared with the proposed method. RESULTS The accuracy of the LogOdds method in reconstructing 3D infarct geometry, as measured by the Dice similarity coefficient, was 82.10% ± 6.58%, a significantly higher value than those of the alternative reconstruction methods. Among outcomes of electrophysiological simulations with infarct reconstructions generated by various methods, the simulation results corresponding to the LogOdds method showed the smallest deviation from those corresponding to the manual reconstructions, as measured by metrics based on both activation maps and pseudo-ECGs. CONCLUSIONS The authors have developed a novel method for reconstructing 3D infarct geometry from segmented slices of Lo-res clinical 2D LGE-CMR images. This method outperformed alternative approaches in reproducing expert manual 3D reconstructions and in electrophysiological simulations.

Sensitivity of reentrant driver localization to electrophysiological parameter variability in image-based computational models of persistent atrial fibrillation sustained by a fibrotic substrate

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, causing morbidity and mortality in millions worldwide. The atria of patients with persistent AF (PsAF) are characterized by the presence of extensive and distributed atrial fibrosis, which facilitates the formation of persistent reentrant drivers (RDs, i.e., spiral waves), which promote fibrillatory activity. Targeted catheter ablation of RD-harboring tissues has shown promise as a clinical treatment for PsAF, but the outcomes remain sub-par. Personalized computational modeling has been proposed as a means of non-invasively predicting optimal ablation targets in individual PsAF patients, but it remains unclear how RD localization dynamics are influenced by inter-patient variability in the spatial distribution of atrial fibrosis, action potential duration (APD), and conduction velocity (CV). Here, we conduct simulations in computational models of fibrotic atria derived from the clinical imaging of PsAF patients to characterize the sensitivity of RD locations to these three factors. We show that RDs consistently anchor to boundaries between fibrotic and non-fibrotic tissues, as delineated by late gadolinium-enhanced magnetic resonance imaging, but those changes in APD/CV can enhance or attenuate the likelihood that an RD will anchor to a specific site. These findings show that the level of uncertainty present in patient-specific atrial models reconstructed without any invasive measurements (i.e., incorporating each individuals unique distribution of fibrotic tissue from medical imaging alongside an average representation of AF-remodeled electrophysiology) is sufficiently high that a personalized ablation strategy based on targeting simulation-predicted RD trajectories alone may not produce the desired result.

The association of pre-existing left atrial fibrosis with clinical variables in patients referred for catheter ablation of atrial fibrillation.

Introduction Atrial fibrillation (AF) recurrence after ablation is associated with left atrial (LA) fibrosis on late gadolinium enhanced (LGE) magnetic resonance imaging (MRI). We sought to determine pre-ablation, clinical characteristics that associate with the extent of LA fibrosis in patients undergoing catheter ablation for AF. Methods and Results Consecutive patients presenting for catheter ablation of AF were enrolled and underwent LGE-MRI prior to initial AF ablation. The extent of fibrosis as a percentage of total LA myocardium was calculated in all patients prior to ablation. The cohort was divided into quartiles based on the percentage of fibrosis. Of 60 patients enrolled in the cohort, 13 had <5% fibrosis (Group 1), 15 had 5-7% fibrosis (Group 2), 17 had 8-13% fibrosis (Group 3), and 15 had 14-36% fibrosis (Group 4). The extent of LA fibrosis was positively associated with time in continuous AF, and the presence of persistent or longstanding persistent AF. However, no statistically significant difference was observed in the presence of comorbid conditions, age, BMI, LA volume, or family history of AF among the four groups. After adjusting for diabetes and hypertension in a multivariable linear regression model, paroxysmal AF remained independently and negatively associated with the extent of fibrosis (-4.0 ± 1.8, P = 0.034). Conclusion The extent of LA fibrosis in patients undergoing AF ablation is associated with AF type and time in continuous AF. Our results suggest that the presence and duration of AF are primary determinants of increased atrial LGE.

The association of left atrial low-voltage regions on electroanatomic mapping with low attenuation regions on cardiac computed tomography perfusion imaging in patients with atrial fibrillation

BACKGROUND Previous studies have shown that contrast-enhanced multidetector computed tomography (CE-MDCT) could identify ventricular fibrosis after myocardial infarction. However, whether CE-MDCT can characterize atrial low-voltage regions remains unknown. OBJECTIVE The purpose of this study was to examine the association of CE-MDCT image attenuation with left atrial (LA) low bipolar voltage regions in patients undergoing repeat ablation for atrial fibrillation recurrence. METHODS We enrolled 20 patients undergoing repeat ablation for atrial fibrillation recurrence. All patients underwent preprocedural 3-dimensional CE-MDCT of the LA, followed by voltage mapping (>100 points) of the LA during the ablation procedure. Epicardial and endocardial contours were manually drawn around LA myocardium on multiplanar CE-MDCT axial images. Segmented 3-dimensional images of the LA myocardium were reconstructed. Electroanatomic map points were retrospectively registered to the corresponding CE-MDCT images. RESULTS A total of 2028 electroanatomic map points obtained in sinus rhythm from the LA endocardium were registered to the segmented LA wall CE-MDCT images. In a linear mixed model, each unit increase in the local image attenuation ratio was associated with 25.2% increase in log bipolar voltage (P = .046) after adjusting for age, sex, body mass index, and LA volume, as well as clustering of data by patient and LA regions. CONCLUSION We demonstrate that the image attenuation ratio derived from CE-MDCT is associated with LA bipolar voltage. The potential ability to image fibrosis via CE-MDCT may provide a useful alternative in patients with contraindications to magnetic resonance imaging.

Imaging-Based Simulations for Predicting Sudden Death and Guiding Ventricular Tachycardia Ablation

Simulation-driven engineering has put rockets in space, airplanes in the sky, and self-driving cars on the road. Computational approaches have also contributed to advancements in clinical medicine and human health.1–3 In the arena of cardiac care, the recent emphasis on personalized medicine has provided a significant impetus for the development of predictive approaches combining imaging and computational modeling that can be applied to the diagnosis and treatment of heart rhythm disorders. A major advance in this direction is the creation and translation into clinical practice of novel imaging- and simulation-based strategies for predicting an individual’s risk of sudden cardiac death (SCD) and for the noninvasive planning of optimal personalized antiarrhythmia therapies. Clinical decisions about the stratification of patients for SCD risk resulting from arrhythmia and for determining the optimal targets for antiarrhythmia ablation therapies could greatly benefit from such targeted developments because current approaches, although life saving, remain suboptimal, often increase the burden on the healthcare system, and could lead to increased patient morbidity. SCD resulting from ventricular arrhythmias is a leading cause of death in the industrialized world, particularly among patients with prior myocardial infarction (MI).4 For patients at high risk of SCD, mortality is reduced by the prophylactic insertion of implantable cardioverter defibrillators (ICDs).5 To determine the level of SCD risk, clinical cardiology practice still relies on the one-size- fits-all metric of left ventricular ejection fraction (LVEF) <35% to identify high-risk patients. Mechanistically, in hearts with structural disease, arrhythmia results from the heterogeneously distributed remodeled tissue, which can promote the initiation and maintenance of electric instability. Global LVEF poorly reflects these mechanistic factors and, hence, its use to determine the level of SCD risk and stratify patients for ICD implantation results in a low rate of appropriate ICD device therapy, only 5% per …

Estimation of ventricular fiber orientations in infarcted hearts for patient-specific simulations

Patient-specific modeling of the heart is limited by lack of technology to acquire myocardial fiber orientations in the clinic. To overcome this limitation, we recently developed an image-based methodology to estimate the fiber orientations. In this study, we test the efficacy of that methodology in infarcted hearts. To this end, we implemented a processing pipeline to compare estimated fiber orientations of infarcted hearts with measured ones, and quantify the effect of the estimation error on outcomes of electrophysiological simulations. The pipeline was applied to images that we acquired from three infarcted canine hearts. The new insights obtained from the project will pave the way for the development of patient-specific models of infarcted hearts that can aid physicians in personalized diagnosis and decisions regarding electrophysiological interventions.

Image-based Reconstruction of 3D Myocardial Infarct Geometry for Patient Specific Applications

Accurate reconstruction of the three-dimensional (3D) geometry of a myocardial infarct from two-dimensional (2D) multi-slice image sequences has important applications in the clinical evaluation and treatment of patients with ischemic cardiomyopathy. However, this reconstruction is challenging because the resolution of common clinical scans used to acquire infarct structure, such as short-axis, late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, is low, especially in the out-of-plane direction. In this study, we propose a novel technique to reconstruct the 3D infarct geometry from low resolution clinical images. Our methodology is based on a function called logarithm of odds (LogOdds), which allows the broader class of linear combinations in the LogOdds vector space as opposed to being limited to only a convex combination in the binary label space. To assess the efficacy of the method, we used high-resolution LGE-CMR images of 36 human hearts in vivo, and 3 canine hearts ex vivo. The infarct was manually segmented in each slice of the acquired images, and the manually segmented data were downsampled to clinical resolution. The developed method was then applied to the downsampled image slices, and the resulting reconstructions were compared with the manually segmented data. Several existing reconstruction techniques were also implemented, and compared with the proposed method. The results show that the LogOdds method significantly outperforms all the other tested methods in terms of region overlap.