Deep Learning-Based Acceleration of Compressed Sensing for Noncontrast-Enhanced Coronary Magnetic Resonance Angiography in Patients With Suspected Coronary Artery Disease

被引:11
|
作者
Wu, Xi [1 ,2 ]
Deng, Liping [1 ]
Li, Wanjiang [1 ]
Peng, Pengfei [1 ]
Yue, Xun [1 ,2 ]
Tang, Lu [1 ]
Pu, Qian [1 ]
Ming, Yue [1 ]
Zhang, Xiaoyong [3 ]
Huang, Xiaohua [2 ]
Chen, Yucheng
Huang, Juan [1 ,4 ,5 ]
Sun, Jiayu [1 ,5 ]
机构
[1] Sichuan Univ, Dept Radiol, West China Hosp, Chengdu, Sichuan, Peoples R China
[2] North Sichuan Med Coll, Dept Radiol, Affiliated Hosp, Nanchong, Sichuan, Peoples R China
[3] Philips Healthcare, Clin Sci, Chengdu, Sichuan, Peoples R China
[4] Sichuan Univ, Dept Cardiol, West China Hosp, Chengdu, Sichuan, Peoples R China
[5] Sichuan Univ, Dept Radiol, West China Hosp, 37 Guo Xue Lane, Chengdu, Sichuan, Peoples R China
关键词
coronary artery disease; deep learning; artificial intelligence; compressed sensing; coronary MR angiography; MR-ANGIOGRAPHY; PERFORMANCE; ALGORITHM; PLAQUES;
D O I
10.1002/jmri.28653
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Background: The clinical application of coronary MR angiography (MRA) remains limited due to its long acquisition time and often unsatisfactory image quality. A compressed sensing artificial intelligence (CSAI) framework was recently introduced to overcome these limitations, but its feasibility in coronary MRA is unknown.Purpose: To evaluate the diagnostic performance of noncontrast-enhanced coronary MRA with CSAI in patients with suspected coronary artery disease (CAD).Study Type: Prospective observational study.Population: A total of 64 consecutive patients (mean age +/- standard deviation [SD]: 59 +/- 10 years, 48.4% females) with suspected CAD.Field Strength/Sequence: A 3.0-T, balanced steady-state free precession sequence.Assessment: Three observers evaluated the image quality for 15 coronary segments of the right and left coronary arteries using a 5-point scoring system (1 = not visible; 5 = excellent). Image scores >= 3 were considered diagnostic. Furthermore, the detection of CAD with >= 50% stenosis was evaluated in comparison to reference standard coronary computed tomography angiography (CTA). Mean acquisition times for CSAI-based coronary MRA were measured.Statistical Tests: For each patient, vessel and segment, sensitivity, specificity, and diagnostic accuracy of CSAI-based coronary MRA for detecting CAD with >= 50% stenosis according to coronary CTA were calculated. Intraclass correlation coefficients (ICCs) were used to assess the interobserver agreement.Results: The mean MR acquisition time +/- SD was 8.1 +/- 2.4 minutes. Twenty-five (39.1%) patients had CAD with >= 50% stenosis on coronary CTA and 29 (45.3%) patients on MRA. A total of 885 segments on the CTA images and 818/885 (92.4%) coronary MRA segments were diagnostic (image score >= 3). The sensitivity, specificity, and diagnostic accuracy were as follows: per patient (92.0%, 84.6%, and 87.5%), per vessel (82.9%, 93.4%, and 91.1%), and per segment (77.6%, 98.2%, and 96.6%), respectively. The ICCs for image quality and stenosis assessment were 0.76-0.99 and 0.66-1.00, respectively.Data Conclusion: The image quality and diagnostic performance of coronary MRA with CSAI may show good results in comparison to coronary CTA in patients with suspected CAD.
引用
收藏
页码:1521 / 1530
页数:10
相关论文
共 50 条
  • [31] Additive value of 3T cardiovascular magnetic resonance coronary angiography for detecting coronary artery disease
    Zhang, Lijun
    Song, Xiantao
    Dong, Li
    Li, Jianan
    Dou, Ruiyu
    Fan, Zhanming
    An, Jing
    Li, Debiao
    JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, 2018, 20
  • [32] Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography
    Yiming Li
    Yu Wu
    Jingjing He
    Weili Jiang
    Jianyong Wang
    Yong Peng
    Yuheng Jia
    Tianyuan Xiong
    Kaiyu Jia
    Zhang Yi
    Mao Chen
    European Radiology, 2022, 32 : 6037 - 6045
  • [33] Influence of the coronary calcium score on the ability to rule out coronary artery stenoses by coronary CT angiography in patients with suspected coronary artery disease
    Schuhbaeck, Annika
    Schmid, Jasmin
    Zimmer, Thomas
    Muschiol, Gerd
    Hell, Michaela M.
    Marwan, Mohamed
    Achenbach, Stephan
    JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY, 2016, 10 (05) : 343 - 350
  • [34] Contemporary Role of Cardiac Magnetic Resonance in the Management of Patients with Suspected or Known Coronary Artery Disease
    Bazoukis, George
    Papadatos, Stamatis S.
    Michelongona, Archontoula
    Lampropoulos, Konstantinos
    Farmakis, Dimitrios
    Vassiliou, Vassilis
    MEDICINA-LITHUANIA, 2021, 57 (07):
  • [35] A continuous-action deep reinforcement learning-based agent for coronary artery centerline extraction in coronary CT angiography images
    Zhang, Yuyang
    Luo, Gongning
    Wang, Wei
    Cao, Shaodong
    Dong, Suyu
    Yu, Daren
    Wang, Xiaoyun
    Wang, Kuanquan
    MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2025, : 1837 - 1847
  • [36] Deep learning-based image restoration algorithm for coronary CT angiography
    Tatsugami, Fuminari
    Higaki, Toru
    Nakamura, Yuko
    Yu, Zhou
    Zhou, Jian
    Lu, Yujie
    Fujioka, Chikako
    Kitagawa, Toshiro
    Kihara, Yasuki
    Iida, Makoto
    Awai, Kazuo
    EUROPEAN RADIOLOGY, 2019, 29 (10) : 5322 - 5329
  • [37] Regional differences in the utilisation of coronary angiography as initial investigation for the evaluation of patients with suspected coronary artery disease
    Kosa, Istvan
    Nemes, Attila
    Belicza, Eva
    Kiraly, Ferenc
    Vassanyi, Istvan
    INTERNATIONAL JOURNAL OF CARDIOLOGY, 2013, 168 (05) : 5012 - 5015
  • [38] Prognostic Value of Whole-Heart Coronary MR Angiography in Patients with Suspected Coronary Artery Disease
    Yoon, Yeonyee E.
    Kitagawa, Kakuya
    Kato, Shingo
    Nagata, Motonori
    Ishida, Masaki
    Nakajima, Hiroshi
    Kurita, Tairo
    Ito, Masaaki
    Sakuma, Hajime
    CIRCULATION, 2011, 124 (21)
  • [39] Deep learning powered coronary CT angiography for detecting obstructive coronary artery disease: The effect of reader experience, calcification and image quality
    Liu, Chun Yu
    Tang, Chun Xiang
    Zhang, Xiao Lei
    Chen, Sui
    Xie, Yuan
    Zhang, Xin Yuan
    Qiao, Hong Yan
    Zhou, Chang Sheng
    Xu, Peng Peng
    Lu, Meng Jie
    Li, Jian Hua
    Lu, Guang Ming
    Zhang, Long Jiang
    EUROPEAN JOURNAL OF RADIOLOGY, 2021, 142
  • [40] Deep learning-based stenosis quantification from coronary CT Angiography
    Hong, Youngtaek
    Commandeur, Frederic
    Cadet, Sebastien
    Goeller, Markus
    Doris, Mhairi K.
    Chen, Xi
    Kwiecinski, Jacek
    Berman, Daniel S.
    Slomka, Piotr J.
    Chang, Hyuk-Jae
    Dey, Damini
    MEDICAL IMAGING 2019: IMAGE PROCESSING, 2019, 10949