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LV systolic point-cloud model to quantify accuracy of CT derived regional strain
Author(s): Ashish Manohar; Gabrielle Colvert; Andrew Schluchter; Francisco Contijoch; Elliot R. McVeigh
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Paper Abstract

We present an analytical LV systolic model derived from human CT data to serve as the ground truth for optimization and validation of a previously published CT-derived regional strain metric called SQUEEZ. Physiologically-accurate strains were applied to each vertex of a clinically derived end-diastolic LV mesh to create analytical end-systolic poses exhibiting normal function as well as regional hypokinesia of four sizes (17.5mm, 14mm, 10.5mm, and 7mm in diameter), each with a programmed severe, medium, and subtle dysfunction. Regional strain estimates were obtained by registering the end-diastolic mesh to each end-systolic mesh condition using a non-rigid registration algorithm. Ground-truth models of normal function and of severe hypokinesia were used to identify the optimal parameters in the registration algorithm, and to measure the accuracy of detecting regional dysfunction of varying sizes and severities. We found that for normal LV systolic contraction, SQUEEZ values in all 16 AHA segments of the LV were accurately measured (within ±0.05). For cases with regional dysfunction, the errors in SQUEEZ in the region around the dysfunction increased with decreasing size of regional dysfunction. The mean SQUEEZ values of the 17.5mm and 14mm diameter dysfunctional regions, which we hypothesize are the most clinically relevant, were accurate to within 0.05.

Paper Details

Date Published: 8 March 2019
PDF: 14 pages
Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109510E (8 March 2019); doi: 10.1117/12.2512635
Show Author Affiliations
Ashish Manohar, Univ. of California, San Diego (United States)
Gabrielle Colvert, Univ. of California, San Diego (United States)
Andrew Schluchter, Univ. of California, San Diego (United States)
Francisco Contijoch, Univ. of California, San Diego (United States)
Elliot R. McVeigh, Univ. of California, San Diego (United States)

Published in SPIE Proceedings Vol. 10951:
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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