Automatic identification of end-diastolic and end-systolic cardiac frames from invasive coronary angiography videos

Article type: Research Article

Authors: Meng, Yinghui^a | Dong, Minghao^a | Dai, Xumin^d | Tang, Haipeng^b | Zhao, Chen^c | Jiang, Jingfeng^c | Xu, Shun^e | Zhou, Ying^e | Zhu, Fubao^a | Xu, Zhihui^{e; *} | Zhou, Weihua^{c; f; *}

Affiliations: [a] School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China | [b] School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, USA | [c] Department of Applied Computing, Michigan Technological University, Houghton, MI, USA | [d] Department of Cardiology, Theresa and Eugene M. Lang Center for Ressearch and Education, New York Presbyterian Queens Hospital, New York, NY, USA | [e] Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China | [f] Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, USA

Correspondence: [*] Corresponding authors: Zhihui Xu, Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China. E-mail: [email protected]. Weihua Zhou, Department of Applied Computing, Michigan Technological University, Houghton, MI, USA. E-mail: [email protected].

Abstract: BACKGROUND: Automatic identification of proper image frames at the end-diastolic (ED) and end-systolic (ES) frames during the review of invasive coronary angiograms (ICA) is important to assess blood flow during a cardiac cycle, reconstruct the 3D arterial anatomy from bi-planar views, and generate the complementary fusion map with myocardial images. The current identification method primarily relies on visual interpretation, making it not only time-consuming but also less reproducible. OBJECITVE: In this paper, we propose a new method to automatically identify angiographic image frames associated with the ED and ES cardiac phases. METHOD: A detection algorithm is first used to detect the key points (i.e. landmarks) of coronary arteries, and then an optical flow method is employed to track the trajectories of the selected key points. The ED and ES frames are identified based on all these trajectories. Our method was tested with 62 ICA videos from two separate medical centers. RESULTS: Comparing consensus interpretations by two human expert readers, excellent agreement was achieved by the proposed algorithm: the agreement rates within a one-frame range were 92.99% and 92.73% for the automatic identification of the ED and ES image frames, respectively. CONCLUSION: The proposed automated method showed great potential for being an integral part of automated ICA image analysis.

Keywords: Coronary artery disease, invasive coronary angiography, cardiac cycle, optical flow

DOI: 10.3233/THC-213693

Journal: Technology and Health Care, vol. 30, no. 5, pp. 1107-1116, 2022