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23 February 2012Automated detection of contractile abnormalities from stress-rest motion changes
Shahryar Karimi-Ashtiani,1 Reza Arsanjani,1 Mathews Fish,2 Daniel Berman,3 Paul Kavanagh,1 Guido Germano,1,4 Piotr Slomka1,4
1Cedars-Sinai Medical Ctr. (United States) 2Oregon Heart and Vascular Institute, Sacred Heart Medical Ctr. (United States) 3Cedars-Sinai Heart Institute (United States) 4David Geffen School of Medicine at UCLA (United States)
Changes in myocardial function signatures such as wall motion and thickening are typically computed separately from
myocardial perfusion SPECT (MPS) stress and rest studies to assess for stress-induced function abnormalities. The
standard approach may suffer from the variability in contour placements and image orientation when subtle changes
between stress and rest scans in motion and thickening are being evaluated. We have developed a new measure of
regional change of function signature (motion and thickening) computed directly from registered stress and rest gated
MPS data. In our novel approach, endocardial surfaces at the end-diastolic and end-systolic frames for stress and rest
studies were registered by matching ventricular surfaces. Furthermore, we propose a new global registration method
based on finding the optimal rotation for myocardial best ellipsoid fit to minimize the indexing disparities between two
surfaces between stress and rest studies. Myocardial stress-rest function changes were computed and normal limits of
change were determined as the mean and standard deviation of the training set for each polar sample. Normal limits were
utilized to quantify the stress-rest function change for each polar map sample and the accumulated quantified function
signature values were used for abnormality assessments in territorial regions. To evaluate the effectiveness of our novel
method, we examined the agreements of our results against visual scores for motion change on vessel territorial regions
obtained by human experts on a test group with 623 cases and were able to show that our detection method has a
improved sensitivity on per vessel territory basis, compared to those obtained by human experts utilizing gated MPS
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Shahryar Karimi-Ashtiani, Reza Arsanjani, Mathews Fish, Daniel Berman, Paul Kavanagh, Guido Germano, Piotr Slomka, "Automated detection of contractile abnormalities from stress-rest motion changes," Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83152G (23 February 2012); https://doi.org/10.1117/12.911672