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• Review •
Clinical application progress of artificial intelligence-assisted compressed sensing technology in MRI
LI Tao  YIN Shuo  ZHANG Xuanxiao  ZHANG Huimao  ZHOU Hongwei 

DOI:10.12015/issn.1674-8034.2025.08.034.


[Abstract] Prolonged scan times remain a major bottleneck for the clinical utility of magnetic resonance imaging (MRI). While conventional compressed sensing techniques accelerate acquisition, they often introduce artifacts and exhibit limited efficacy in reconstructing complex anatomical structures at high acceleration factors.Artificial intelligence-assisted compressed sensing (ACS) addresses these limitations by integrating deep learning (DL) architectures—such as convolutional neural networks (CNNs) and generative adversarial networks (GANs)—with compressed sensing principles within end-to-end frameworks. This synergy enables substantial acceleration (>2×) while preserving diagnostic features. However, ACS faces critical challenges: lack of standardized acceleration factors, insufficient algorithm generalizability across diverse anatomies and pathological heterogeneity, and inadequate validation of diagnostic efficacy for subtle lesions (e.g., small metastatic lymph nodes). Furthermore, existing reviews predominantly focus on single-system applications or purely technical aspects, lacking a systematic evaluation of ACS's clinical utility across multiple body regions.This review systematically synthesizes technological advancements and MRI clinical progress in ACS, critically evaluating its strengths, limitations, and unresolved challenges in multi-system imaging (head-neck, musculoskeletal, cardiothoracic, abdominal, pelvic). We aim to provide evidence-based guidance for optimizing clinical implementation of ACS and direct future research toward advancing precision, efficiency, and intelligence in MRI.
[Keywords] artificial intelligence；compressed sensing；magnetic resonance imaging；cerebrovascular diseases；bone and joint diseases；coronary artery diseases；abdominal diseases；uterine-related disorders

LI Tao   YIN Shuo   ZHANG Xuanxiao   ZHANG Huimao   ZHOU Hongwei*  

Department of Radiology, the First Hospital of Jilin University, Changchun 130021, China

Corresponding author: ZHOU H W, E-mail: hwzhou@jlu.edu.cn

Conflicts of interest   None.

Received  2025-06-06
Accepted  2025-08-05
DOI: 10.12015/issn.1674-8034.2025.08.034
DOI:10.12015/issn.1674-8034.2025.08.034.

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