Header logo is ps

Regional comparison of left ventricle systolic wall stress reveals intraregional uniformity in healthy subjects


Conference Paper


This study aimed to assess the feasibility of using the regional uniformity of the left ventricle (LV) wall stress (WS) to diagnose patients with myocardial infarction. We present a novel method using a similarity map that measures the degree of uniformity in nominal systolic WS across pairs of segments within the same patient. The values of the nominal WS are computed at each vertex point from a 1-to-1 corresponding mesh pair of the LV at the end-diastole (ED) and end-systole (ES) phases. The 3D geometries of the LV at ED and ES are reconstructed from border-delineated MRI images and the 1-to-1 mesh generated using a strain-energy minimization approach. The LV is then partitioned into 16 segments based on published clinical standard and the nominal WS histogram distribution for each of the segment was computed. A similarity index is then computed for each pair of histogram distributions to generate a 16-by-16 similarity map. Based on our initial study involving 12 MI patients and 9 controls, we observed uniformity for intra- regional comparisons in the controls compared against the patients. Our results suggest that the regional uniformity of the nominal systolic WS in the form of a similarity map can potentially be used as a discriminant between MI patients and normal controls.

Author(s): Soo Kng Teo and Si Yong Yeo and May Ling Tan and Chi Wan Lim and Liang Zhong and Ru San Tan and Yi Su
Book Title: Computing in Cardiology Conference
Pages: 575 - 578
Year: 2013

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference


  title = {Regional comparison of left ventricle systolic wall stress reveals intraregional uniformity in healthy subjects},
  author = {Teo, Soo Kng and Yeo, Si Yong and Tan, May Ling and Lim, Chi Wan and Zhong, Liang and Tan, Ru San and Su, Yi},
  booktitle = {Computing in Cardiology Conference},
  pages = {575 - 578},
  year = {2013}