  Static cardiac imaging such as late gadolinium enhancement, mapping, or 3-D
coronary angiography require prior information, e.g., the phase during a
cardiac cycle with least motion, called resting phase (RP). The purpose of this
work is to propose a fully automated framework that allows the detection of the
right coronary artery (RCA) RP within CINE series. The proposed prototype
system consists of three main steps. First, the localization of the regions of
interest (ROI) is performed. Second, the cropped ROI series are taken for
tracking motions over all time points. Third, the output motion values are used
to classify RPs. In this work, we focused on the detection of the area with the
outer edge of the cross-section of the RCA as our target. The proposed
framework was evaluated on 102 clinically acquired dataset at 1.5T and 3T. The
automatically classified RPs were compared with the reference RPs annotated
manually by a expert for testing the robustness and feasibility of the
framework. The predicted RCA RPs showed high agreement with the experts
annotated RPs with 92.7% accuracy, 90.5% sensitivity and 95.0% specificity for
the unseen study dataset. The mean absolute difference of the start and end RP
was 13.6 $\pm$ 18.6 ms for the validation study dataset (n=102). In this work,
automated RP detection has been introduced by the proposed framework and
demonstrated feasibility, robustness, and applicability for static imaging
acquisitions.
