Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery

• URL: http://arxiv.org/abs/2109.02342v1
• Date: Mon, 6 Sep 2021 10:29:52 GMT
• Title: Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery
• Authors: Seung Su Yoon, Elisabeth Preuhs, Michaela Schmidt, Christoph Forman, Teodora Chitiboi, Puneet Sharma, Juliano Lara Fernandes, Christoph Tillmanns, Jens Wetzl, Andreas Maier
• Abstract summary: 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 over all time points are taken for tracking motions quantitatively. Third, the output motion values are used to classify RPs.
• Score: 5.227072666312533
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Purpose: 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. Methods: The proposed prototype system consists of three main steps. First, the localization of the regions of interest (ROI) is performed. Second, as CINE series are time-resolved, the cropped ROI series over all time points are taken for tracking motions quantitatively. 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 ground truth RPs annotated manually by a medical expert for testing the robustness and feasibility of the framework. Results: 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). Conclusion: In this work, automated RP detection has been introduced by the proposed framework and demonstrated feasibility, robustness, and applicability for diverse static imaging acquisitions.

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