PROJECT TITLE :
Sensitivity of Noninvasive Cardiac Electrophysiological Imaging to Variations in Personalized Anatomical Modeling
Objective: Noninvasive cardiac electrophysiological (EP) imaging techniques depend upon anatomically-detailed heart-torso models derived from high-quality tomographic pictures of individual subjects. But, anatomical modeling involves variations that cause unresolved uncertainties in the result of EP imaging, bringing queries to the robustness of these strategies in clinical follow. During this study, we have a tendency to design a systematic statistical approach to assess the sensitivity of EP imaging methods to the variations in personalized anatomical modeling. Methods: We have a tendency to initial quantify the variations in personalised anatomical models by a unique application of statistical form modeling. Given the statistical distribution of the variation in customized anatomical models, we have a tendency to then employ unscented transform to work out the sensitivity of EP imaging outputs to the variation in input personalised anatomical modeling. Results: We have a tendency to test the feasibility of our proposed approach using 2 of the existing EP imaging ways: epicardial-based electrocardiographic imaging and transmural electrophysiological imaging. Both phantom and real-data experiments show that variations in customized anatomical models have negligible impact on the outcome of EP imaging. Conclusion: This study verifies the robustness of EP imaging ways to the errors in personalised anatomical modeling and suggests the chance to simplify the process of anatomical modeling in future clinical practice. Significance: This study proposes a scientific statistical approach to quantify anatomical modeling variations and assess their impact on EP imaging, that can be extended to seek out a balance between the standard of customized anatomical models and therefore the accuracy of EP imaging that will improve the clinical feasibility of EP imaging.
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