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Proceedings Paper

Patient-specific coronary artery blood flow simulation using myocardial volume partitioning
Author(s): Kyung Hwan Kim; Dongwoo Kang; Nahyup Kang; Ji-Yeon Kim; Hyong-Euk Lee; James D. K. Kim
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Paper Abstract

Using computational simulation, we can analyze cardiovascular disease in non-invasive and quantitative manners. More specifically, computational modeling and simulation technology has enabled us to analyze functional aspect such as blood flow, as well as anatomical aspect such as stenosis, from medical images without invasive measurements. Note that the simplest ways to perform blood flow simulation is to apply patient-specific coronary anatomy with other average-valued properties; in this case, however, such conditions cannot fully reflect accurate physiological properties of patients. To resolve this limitation, we present a new patient-specific coronary blood flow simulation method by myocardial volume partitioning considering artery/myocardium structural correspondence. We focus on that blood supply is closely related to the mass of each myocardial segment corresponding to the artery. Therefore, we applied this concept for setting-up simulation conditions in the way to consider many patient-specific features as possible from medical image: First, we segmented coronary arteries and myocardium separately from cardiac CT; then the myocardium is partitioned into multiple regions based on coronary vasculature. The myocardial mass and required blood mass for each artery are estimated by converting myocardial volume fraction. Finally, the required blood mass is used as boundary conditions for each artery outlet, with given average aortic blood flow rate and pressure. To show effectiveness of the proposed method, fractional flow reserve (FFR) by simulation using CT image has been compared with invasive FFR measurement of real patient data, and as a result, 77% of accuracy has been obtained.

Paper Details

Date Published: 18 March 2013
PDF: 5 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867019 (18 March 2013); doi: 10.1117/12.2007898
Show Author Affiliations
Kyung Hwan Kim, Samsung Advanced Institute of Technology (Korea, Republic of)
Dongwoo Kang, The Univ. of Southern California (United States)
Nahyup Kang, Samsung Advanced Institute of Technology (Korea, Republic of)
Ji-Yeon Kim, Samsung Advanced Institute of Technology (Korea, Republic of)
Hyong-Euk Lee, Samsung Advanced Institute of Technology (Korea, Republic of)
James D. K. Kim, Samsung Advanced Institute of Technology (Korea, Republic of)

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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