Feasibility of using patient-specific models and the “minimum cut” algorithm to predict optimal ablation targets for left atrial flutter

Zahid, Sohail; Whyte, Kaitlyn N.; Schwarz, Erica L.; Blake, Robert C.; Boyle, Patrick; Chrispin, Jonathan; Prakosa, Adityo; İpek, Esra Gücük; Pashakhanloo, Farhad; Halperin, Henry R.; Calkins, Hugh; Berger, Ronald D.; Nazarian, Saman; Trayanova, Natalia A.

doi:10.1016/j.hrthm.2016.04.009

Cited by 83 publications

(105 citation statements)

References 24 publications

(29 reference statements)

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“…In a prediction of optimal ablation targets using a novel approach based on flow network theory, in silico ablation was applied to a ‘minimum cut’ – the smallest amount of tissue separating the flow. Resultant lesions were similar in length and location to clinical ablation lesions for these patients (Zahid et al 2016). These studies demonstrate that a patient-specific modeling approach to non-invasively identify AF ablation targets prior to the clinical procedure may present a powerful tool for optimizing ablation procedures.…”

Section: Non-pharmacological Rhythm-control Therapiesmentioning

confidence: 55%

Anti-arrhythmic strategies for atrial fibrillation

Grandi

Maleckar

2016

Pharmacology & Therapeutics

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Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with increased risk of cerebrovascular stroke, and with several other pathologies, including heart failure. Current therapies for AF are targeted at reducing risk of stroke (anticoagulation) and tachycardia-induced cardiomyopathy (rate or rhythm control). Rate control, typically achieved by atrioventricular nodal blocking drugs, is often insufficient to alleviate symptoms. Rhythm control approaches include antiarrhythmic drugs, electrical cardioversion, and ablation strategies. Here, we offer several examples of how computational modeling can provide a quantitative framework for integrating multi scale data to: (a) gain insight into multi-scale mechanisms of AF; (b) identify and test pharmacological and electrical therapy and interventions; and (c) support clinical decisions. We review how modeling approaches have evolved and contributed to the research pipeline and preclinical development and discuss future directions and challenges in the field.

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Section: Non-pharmacological Rhythm-control Therapiesmentioning

confidence: 55%

Anti-arrhythmic strategies for atrial fibrillation

Grandi

Maleckar

2016

Pharmacology & Therapeutics

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“…Graph-based methods are mainly involved in normalized cuts (Ncut) [20], average association [26], and minimum cut [27]. These methods are used for image segmentation by constructing a weighed graph for describing relationships between pixels.…”

Section: Graph-based Partitionmentioning

confidence: 99%

A Fast Color Image Segmentation Approach Using GDF with Improved Region-Level Ncut

Liu¹,

Wang²,

Li³

et al. 2018

Mathematical Problems in Engineering

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Color image segmentation is fundamental in image processing and computer vision. A novel approach, GDF-Ncut, is proposed to segment color images by integrating generalized data field (GDF) and improved normalized cuts (Ncut). To start with, the hierarchy-grid structure is constructed in the color feature space of an image in an attempt to reduce the time complexity but preserve the quality of image segmentation. Then a fast hierarchy-grid clustering is performed under GDF potential estimation and therefore image pixels are merged into disjoint oversegmented but meaningful initial regions. Finally, these regions are presented as a weighted undirected graph, upon which Ncut algorithm merges homogenous initial regions to achieve final image segmentation. The use of the fast clustering improves the effectiveness of Ncut because regions-based graph is constructed instead of pixel-based graph. Meanwhile, during the processes of Ncut matrix computation, oversegmented regions are grouped into homogeneous parts for greatly ameliorating the intermediate problems from GDF and accordingly decreasing the sensitivity to noise. Experimental results on a variety of color images demonstrate that the proposed method significantly reduces the time complexity while partitioning image into meaningful and physically connected regions. The method is potentially beneficial to serve object extraction and pattern recognition.

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“…Recently, a major step towards this object was taken with the publication of a study 51 that compared model-predicted optimal ablations with clinical lesions that rendered arrhythmia noninducible. Specifically, this study was carried out in a cohort of patients who were successfully treated for AF via catheter ablation but experienced recurrent post-procedure left atrial flutter (LAFL).…”

Section: Model-based Prediction Of Optimal Ablation Strategiesmentioning

confidence: 99%

Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia

2016

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Atrial arrhythmias involving a fibrotic substrate are an important cause of morbidity and mortality. In many cases, effective treatment of such rhythm disorders is severely hindered by a lack of mechanistic understanding relating features of fibrotic remodelling to dynamics of re-entrant arrhythmia. With the advent of clinical imaging modalities capable of resolving the unique fibrosis spatial pattern present in the atria of each individual patient, a promising new research trajectory has emerged in which personalized computational models are used to analyse mechanistic underpinnings of arrhythmia dynamics based on the distribution of fibrotic tissue. In this review, we first present findings that have yielded a robust and detailed biophysical representation of fibrotic substrate electrophysiological properties. Then, we summarize the results of several recent investigations seeking to use organ-scale models of the fibrotic human atria to derive new insights on mechanisms of arrhythmia perpetuation and to develop novel strategies for model-assisted individualized planning of catheter ablation procedures for atrial arrhythmias.

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Feasibility of using patient-specific models and the “minimum cut” algorithm to predict optimal ablation targets for left atrial flutter

Cited by 83 publications

References 24 publications

Anti-arrhythmic strategies for atrial fibrillation

Anti-arrhythmic strategies for atrial fibrillation

A Fast Color Image Segmentation Approach Using GDF with Improved Region-Level Ncut

Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia

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