Deep learning reconstruction for fast cardiovascular magnetic resonance imaging protocol: A comparative study with conventional cardiovascular magnetic resonance

作者：Yiying Hua, Hongfei Lü, Shiya Wang, Xiuzheng Yue, Fan Du, Nan Zhang, Mengmeng Yu, Yinyin Chen, Mengsu Zeng, Hang Jin · 发表于：Journal of Cardiovascular Magnetic Resonance · 年份：2025 · DOI：10.1016/j.jocmr.2025.102017 · 被引用次数：4 · 研究领域：Advanced MRI Techniques and Applications、Cardiac Imaging and Diagnostics、Medical Imaging Techniques and Applications

BACKGROUND: Cardiovascular magnetic resonance (CMR) is a reference-standard modality for diagnosis of cardiac diseases, although its clinical application is restricted by prolonged acquisition times. Recently, artificial intelligence, particularly deep learning (DL), has exhibited the potential to accelerate the CMR acquisition through technological advances. Prospective validation of its diagnostic performance across multiple clinical sequences remains underexplored. This research aims to assess the functions of the compressed sensing artificial intelligence (CSAI) algorithm in accelerating CMR acquisition, enhancing image quality, and maintaining diagnostic accuracy versus conventional sensitivity encoding (SENSE) reconstruction. METHODS: A total of 105 participants scheduled for clinical CMR between February and August 2024 underwent both SENSE and CSAI-accelerated sequences containing Cine, T2 short TI inversion recovery (STIR), and late gadolinium enhancement (LGE) during a single session. The subjective image quality was assessed by a 5-point Likert scale. Quantitative image quality metrics were evaluated, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge sharpness, ventricular function, T2 signal intensity (SI) ratio, and LGE percentage. RESULTS: Using standard resolution, the acquisition time of CSAI-CMR was 57.4%(159.2/277.1) lower than that of SENSE CMR (159.2 ± 22.4 s vs 277.1 ± 30.4 s; P < 0.001). Higher subjective scores could be found in...