Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery

Seung Su Yoon1,20000-0003-4945-9325, Elisabeth Preuhs1, Michaela Schmidt2, Christoph Forman2, Teodora Chitiboi3, Puneet Sharma3, Juliano L. Fernandes4, Christoph Tillmanns5, Jens Wetzl2, Andreas Maier1
1: Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, 2: Magnetic Resonance, Siemens Healthcare GmbH, 3: Siemens Medical Solutions USA, 4: Jose Michel Kalaf Research Institute, 5: Diagnostikum Berlin
Publication date: 2023/02/03
PDF · arXiv


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 ± 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.


Resting Phase Detection · Workflow Automation · Standardized Imaging · Cardiac Workflow · Static Cardiac Imaging

Bibtex @article{melba:2023:001:yoon, title = "Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery", author = "Yoon, Seung Su and Preuhs, Elisabeth and Schmidt, Michaela and Forman, Christoph and Chitiboi, Teodora and Sharma, Puneet and Fernandes, Juliano L. and Tillmanns, Christoph and Wetzl, Jens and Maier, Andreas", journal = "Machine Learning for Biomedical Imaging", volume = "2", issue = "January 2023 issue", year = "2023", pages = "1--26", issn = "2766-905X", doi = "", url = "" }
RISTY - JOUR AU - Yoon, Seung Su AU - Preuhs, Elisabeth AU - Schmidt, Michaela AU - Forman, Christoph AU - Chitiboi, Teodora AU - Sharma, Puneet AU - Fernandes, Juliano L. AU - Tillmanns, Christoph AU - Wetzl, Jens AU - Maier, Andreas PY - 2023 TI - Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery T2 - Machine Learning for Biomedical Imaging VL - 2 IS - January 2023 issue SP - 1 EP - 26 SN - 2766-905X DO - UR - ER -

2023:001 cover