Keywords: Myocardium, Perfusion
Motivation: While extensive research has explored robust pixel-wise quantification of myocardial blood flow, there is also a need for further investigation into accurate myocardial signal delay estimation, given its diagnostic value, for enhancing conventional clinical myocardial perfusion protocols.
Goal(s): We seek to address the primary challenges in calculating delay and improve its estimation accuracy.
Approach: We introduce a deep learning approach designed to recognize motion artifacts as outliers along the temporal signal curve of each voxel, subsequently eliminating them from the perfusion quantification process.
Results: Our findings suggest that eliminating outliers enhances the accuracy of perfusion delay parameter estimation in scenarios with residual motion.
Impact: Enhancing the precision of delay estimation will increase its diagnostic value as a biomarker, offering crucial insights into the identification of perfusion defects in ischemic hearts.
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