Toward Clinically-Feasible Noninvasive Electrophysiological Imaging: Investigating the Impact of Local Anatomical Details

Rahimi, Azar; Mao, Hongda; Shi, Peng; Wang, Linwei

doi:10.1007/978-3-642-28326-0_19

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…14) can be seen as an anatomically-plausible model that is personalized to a subject’s heart anatomy without including realistic shape details. The construction details of this model and its use in phantom experiments were previously described [50]. …”

Section: Discussionmentioning

confidence: 99%

Sensitivity of Noninvasive Cardiac Electrophysiological Imaging to Variations in Personalized Anatomical Modeling

Rahimi

Wang

2015

IEEE Trans. Biomed. Eng.

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Objective Noninvasive cardiac electrophysiological (EP) imaging techniques rely on anatomically-detailed heart-torso models derived from high-quality tomographic images of individual subjects. However, anatomical modeling involves variations that lead to unresolved uncertainties in the outcome of EP imaging, bringing questions to the robustness of these methods in clinical practice. In this study, we design a systematic statistical approach to assess the sensitivity of EP imaging methods to the variations in personalized anatomical modeling. Methods We first quantify the variations in personalized anatomical models by a novel application of statistical shape modeling. Given the statistical distribution of the variation in personalized anatomical models, we then employ unscented transform to determine the sensitivity of EP imaging outputs to the variation in input personalized anatomical modeling. Results We test the feasibility of our proposed approach using two of the existing EP imaging methods: epicardial-based electrocardiographic imaging and transmural electrophysiological imaging. Both phantom and real-data experiments show that variations in personalized anatomical models have negligible impact on the outcome of EP imaging. Conclusion This study verifies the robustness of EP imaging methods to the errors in personalized anatomical modeling and suggests the possibility to simplify the process of anatomical modeling in future clinical practice. Significance This study proposes a systematic statistical approach to quantify anatomical modeling variations and assess their impact on EP imaging, which can be extended to find a balance between the quality of personalized anatomical models and the accuracy of EP imaging that may improve the clinical feasibility of EP imaging.

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Section: Discussionmentioning

confidence: 99%

Sensitivity of Noninvasive Cardiac Electrophysiological Imaging to Variations in Personalized Anatomical Modeling

Rahimi

Wang

2015

IEEE Trans. Biomed. Eng.

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“…Miss-specifications of the geometry such as position of the heart or conductivities of the organs introduce error into the forward model and thus the inverse solutions. The literature addressing this problem is still small, but there has been some work trying to characterize the uncertainty introduced by these model errors [30–32] and, more recently to correct for those from the data [33–35].…”

Section: Modeling Approachesmentioning

confidence: 99%

A Common-Ground Review of the Potential for Machine Learning Approaches in Electrocardiographic Imaging Based on Probabilistic Graphical Models

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Wang

Brooks

2018

2018 Computing in Cardiology Conference (CinC)

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Machine learning (ML) methods have seen an explosion in their development and application. They are increasingly being used in many different fields with considerable success. However, although the interest is growing, their impact in the field of electrocardiographic imaging (ECGI) remains limited. One of the main reasons that ML has yet to become more prevalent in ECGI is that the published literature is scattered and there is no common ground description and comparison of these methods in an ML framework. Here we address this limitation with a review of ECGI methods from the perspective of ML. We will use probabilistic modeling to provide a common ground framework to compare different methods and well known approaches. Finally, we will discuss which approaches have been used to do inference on these models and which alternatives could be utilized as the methods in ML become more mature.

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Computational reduction for noninvasive transmural electrophysiological imaging

Wang¹

2013

Computers in Biology and Medicine

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Toward Clinically-Feasible Noninvasive Electrophysiological Imaging: Investigating the Impact of Local Anatomical Details

Cited by 3 publications

References 11 publications

Sensitivity of Noninvasive Cardiac Electrophysiological Imaging to Variations in Personalized Anatomical Modeling

Sensitivity of Noninvasive Cardiac Electrophysiological Imaging to Variations in Personalized Anatomical Modeling

A Common-Ground Review of the Potential for Machine Learning Approaches in Electrocardiographic Imaging Based on Probabilistic Graphical Models

Computational reduction for noninvasive transmural electrophysiological imaging

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