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

Trayanova, Natalia A.; Pashakhanloo, Farhad; Wu, Kathérine C.; Halperin, Henry R.

doi:10.1161/circep.117.004743

Cited by 50 publications

(37 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, pathological tissue remodeling (e.g., locations and extension of infarct scars, diffuse fibrosis, etc.) can be accounted for via the imaging as well (Vadakkumpadan et al, 2009;Mewton et al, 2011;Dass et al, 2012;Ashikaga et al, 2013;Arevalo et al, 2016;Trayanova et al, 2017). However, there are two important types of microscopic structural information that cannot be completely personalized yet: the myocardial fiber orientation and the CCS.…”

Section: Electrophysiology Modulementioning

confidence: 99%

“…(Trayanova, 2011); as well as for prediction of arrhythmia risks in specific patients. Furthermore, these models are also used to examine the mechanisms of defibrillation shock in the heart for terminating arrhythmia, as well as for increasing the understanding of ablation targets in the arrhythmia treatments (Trayanova et al, 2017).…”

Section: Module Outputs and Applicationsmentioning

confidence: 99%

“…Furthermore, it was shown that the prediction of the thrombotic risk using the hemodynamic variables was validated with a higher sensitivity and specificity in comparison with the standard clinical metrics. In conclusion, this type of personalized computational modeling can be used to provide a non-invasive thrombotic risk stratification that is FIGURE 14 | Summary of cardiovascular IBM's modules and their respective inputs and outputs with corresponding references: 1 Krishnamurthy et al, 2013;Lopez-Perez et al, 2015;2 Krishnamurthy et al, 2013;Cardenes et al, 2014;Kayvanpour et al, 2015;Lopez-Perez et al, 2015;3 Krishnamurthy et al, 2013;Grande Gutierrez et al, 2017;Finsberg et al, 2018;4 Meoli et al, 2015;Bonfanti et al, 2019;5 Arevalo et al, 2016;Trayanova et al, 2017;Lopez-Perez et al, 2019;6 Krishnamurthy et al, 2013;Finsberg et al, 2018;Palit et al, 2018;7 Kung et al, 2014;Grande Gutierrez et al, 2017;Bonfanti et al, 2019;8 Choi et al, 2015;Seo et al, 2016;Harfi et al, 2017;9 Arevalo et al, 2016;Deng et al, 2016;Trayanova and Chang, 2016;Prakosa et al, 2018;10 Arevalo et al, 2016;Trayanova et al, 2017;Deng et al, 2019;11 Voorhees and Han, 2015;Walmsley et al, 2017;Lee et al, 2018;12 Koo et al, 2011;Min et al, 2012Min et al, , 2015…”

Section: Module Personalization Examplementioning

confidence: 99%

See 2 more Smart Citations

An Introductory Overview of Image-Based Computational Modeling in Personalized Cardiovascular Medicine

Nguyen

Kadri

Voronov

2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Section: Electrophysiology Modulementioning

confidence: 99%

Section: Module Outputs and Applicationsmentioning

confidence: 99%

Section: Module Personalization Examplementioning

confidence: 99%

See 1 more Smart Citation

An Introductory Overview of Image-Based Computational Modeling in Personalized Cardiovascular Medicine

Nguyen

Kadri

Voronov

2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

“…Indeed, computational studies from our group (12)(13)(14) and others (15)(16)(17)(18)(19)(20)(21)(22) have implemented repolarizing potassium channel remodeling to enforce prolonged APD in the BZ as a reliable means of facilitating unidirectional block around the scar. Such an in silico approach has been particularly valuable in helping to understand cardiac arrhythmic mechanisms and, more recently, move toward patient-specific simulation and therapy planning (23).…”

Section: Introductionmentioning

confidence: 99%

Factors Promoting Conduction Slowing as Substrates for Block and Reentry in Infarcted Hearts

Campos

Whitaker

Neji

et al. 2019

Biophysical Journal

View full text Add to dashboard Cite

The development of effective and safe therapies for scar-related ventricular tachycardias requires a detailed understanding of the mechanisms underlying the conduction block that initiates electrical re-entries associated with these arrhythmias. Conduction block has been often associated with electrophysiological changes that prolong action potential duration (APD) within the border zone (BZ) of chronically infarcted hearts. However, experimental evidence suggests that remodeling processes promoting conduction slowing as opposed to APD prolongation mark the chronic phase. In this context, the substrate for the initial block at the mouth of an isthmus/diastolic channel leading to ventricular tachycardia is unclear. The goal of this study was to determine whether electrophysiological parameters associated with conduction slowing can cause block and reentry in the BZ. In silico experiments were conducted on two-dimensional idealized infarct tissue as well as on a cohort of postinfarction porcine left ventricular models constructed from ex vivo magnetic resonance imaging scans. Functional conduction slowing in the BZ was modeled by reducing sodium current density, whereas structural conduction slowing was represented by decreasing tissue conductivity and including fibrosis. The arrhythmogenic potential of APD prolongation was also tested as a basis for comparison. Within all models, the combination of reduced sodium current with structural remodeling more often degenerated into re-entry and, if so, was more likely to be sustained for more cycles. Although re-entries were also detected in experiments with prolonged APD, they were often not sustained because of the subsequent block caused by long-lasting repolarization. Functional and structural conditions associated with slow conduction rather than APD prolongation form a potent substrate for arrhythmogenesis at the isthmus/BZ of chronically infarcted hearts. Reduced excitability led to block while slow conduction shortened the wavelength of propagation, facilitating the sustenance of re-entries. These findings provide important insights for models of patient-specific risk stratification and therapy planning.

show abstract

“…Patient selection and delivery of such therapies may be improved through individualized, noninvasive modeling of this arrhythmia substrate . In particular, computational modeling of hearts with ischemic cardiomyopathy has emerged as a promising tool to guide patient‐specific diagnosis and the treatment of associated rhythm disorders . To accurately represent patient‐specific structural remodeling, computational models must incorporate accurate reconstructions of left ventricular (LV) scar geometry …”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network‐based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images

2019

View full text Add to dashboard Cite

Purpose: Accurate three-dimensional (3D) segmentation of myocardial replacement fibrosis (i.e., scar) is emerging as a potentially valuable tool for risk stratification and procedural planning in patients with ischemic cardiomyopathy. The main purpose of this study was to develop a semiautomated method using a 3D convolutional neural network (CNN)-based for the segmentation of left ventricle (LV) myocardial scar from 3D late gadolinium enhancement magnetic resonance (LGE-MR) images. Methods: Our proposed CNN is built upon several convolutional and pooling layers aimed at choosing appropriate features from LGE-MR images to distinguish between myocardial scar and healthy tissues of the left ventricle. In contrast to previous methods that consider image intensity as the sole feature, CNN-based algorithms have the potential to improve the accuracy of scar segmentation through the creation of unconventional features that separate scar from normal myocardium in the feature space. The first step of our pipeline was to manually delineate the left ventricular myocardium, which was used as the region of interest for scar segmentation. Our developed algorithm was trained using 265,220 volume patches extracted from ten 3D LGE-MR images, then was validated on 450,454 patches from a testing dataset of 24 3D LGE-MR images, all obtained from patients with chronic myocardial infarction. We evaluated our method in the context of several alternative methods by comparing algorithm-generated segmentations to manual delineations performed by experts. Results: Our CNN-based method reported an average Dice similarity coefficient (DSC) and Jaccard Index (JI) of 93.63% AE 2.6% and 88.13% AE 4.70%. In comparison to several previous methods, including K-nearest neighbor (KNN), hierarchical max flow (HMF), full width at half maximum (FWHM), and signal threshold to reference mean (STRM), the developed algorithm reported significantly higher accuracy for DSC with a P-value less than 0.0001. Conclusions: Our experimental results demonstrated that our CNN-based proposed method yielded the highest accuracy of all contemporary LV myocardial scar segmentation methodologies, inclusive of the most widely used signal intensity-based methods, such as FWHM and STRM. To our knowledge, this is the first description of LV myocardial scar tissue segmentation from 3D LGE-MR images using a CNN-based method.

show abstract

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

Cited by 50 publications

References 63 publications

An Introductory Overview of Image-Based Computational Modeling in Personalized Cardiovascular Medicine

An Introductory Overview of Image-Based Computational Modeling in Personalized Cardiovascular Medicine

Factors Promoting Conduction Slowing as Substrates for Block and Reentry in Infarcted Hearts

Convolutional neural network‐based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images

Contact Info

Product

Resources

About