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.

翻译：静态心脏成像，如钆延迟增强、参数成像或三维冠状动脉成像，需要先期信息，例如心动周期中运动幅度最小的相位，称为静息期。本研究旨在提出一个全自动框架，用于在CINE序列中检测右冠状动脉的静息期。所提出的原型系统包含三个主要步骤：首先，执行感兴趣区域（ROI）的定位；其次，对裁剪后的ROI序列进行全时相运动追踪；第三，利用输出的运动数值对静息期进行分类。本研究以右冠状动脉横截面外缘区域作为检测目标。该框架在102个临床数据集（1.5T和3T场强）上进行了评估。将自动分类的静息期与专家手动标注的参考静息期进行对比，以检验框架的稳健性和可行性。对于未见研究数据集，预测的右冠状动脉静息期与专家标注的静息期高度吻合，准确率为92.7%，灵敏度为90.5%，特异度为95.0%。验证研究数据集（n=102）中，静息期起点与终点的平均绝对差值为13.6±18.6毫秒。本研究通过所提出的框架实现了自动化静息期检测，并证明了其在静态成像采集中的可行性、稳健性和适用性。