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Observational Study
. 2023 Nov;58(5):1521-1530.
doi: 10.1002/jmri.28653. Epub 2023 Feb 27.

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

Xi Wu  1   2 , Liping Deng  1 , Wanjiang Li  1 , Pengfei Peng  1 , Xun Yue  1   2 , Lu Tang  1 , Qian Pu  1 , Yue Ming  1 , Xiaoyong Zhang  3 , Xiaohua Huang  2 , Yucheng Chen  4 , Juan Huang  1 , Jiayu Sun  1
Affiliations
Observational Study

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

Xi Wu et al. J Magn Reson Imaging. 2023 Nov.

Abstract

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.

Evidence level: 1.

Technical efficacy: 2.

Keywords: artificial intelligence; compressed sensing; coronary MR angiography; coronary artery disease; deep learning.

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