The detection of a window with the least motion, e.g. end-systolic or end-diastolic resting phases (RPs) within the cardiac cycle is necessary for data acquisition for static cardiac imaging. In the current workflow, it is manually performed on CINE images by visual inspection. To automate the workflow, we propose an improved Deep-Learning-based automated localized RP detection. While the first step is responsible to localize the anatomy of interest, the second is to quantify motion within the target, and third is to classify RPs. We validated the system with a prospective volunteer study and achieved accuracy in the range of 28ms.
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