Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Radiography (Lond). 2024 Oct;30(6):1704-1712. doi: 10.1016/j.radi.2024.08.012. Epub 2024 Aug 31.
Magnetic Resonance Imaging (MRI) has revolutionized the diagnosis and treatment of musculoskeletal disorders. Parallel imaging (PI) and compressed sensing (CS) techniques reduce scan time, but higher acceleration factors decrease image quality. Artificial intelligence has enhanced MRI reconstructions by integrating deep learning algorithms. Therefore, the study aims to review the impact of Artificial intelligence-assisted compressed sensing (AI-CS) and acceleration factors on scan time and image quality in musculoskeletal MRI.
Database searches were completed across PubMed, Scopus, CINAHL, Web of Science, Cochrane Library, and Embase to identify relevant articles focusing on the application of AI-CS in musculoskeletal MRI between 2022 and 2024. We utilized the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines to extract data from the selected studies.
Nine articles were included for the final review, with a total sample size of 730 participants. Of these, seven articles were rated as high, while two articles were considered to be of moderate quality. MRI examination with AI-CS showed scan time reduction of 18.9-38.8% for lumbar spine, 38-40% for shoulder, 54-75% for knee and 53-63% for ankle.
AI-CS showed a significant reduction in scan time and improved image quality for 2D and 3D sequences in musculoskeletal MRI compared with PI and CS. Determining the optimal acceleration factor necessary to achieve images with higher image quality compared to traditional PI techniques is required before clinical implementation. Higher acceleration factors currently lead to reduced image scores, although advancements in AI-CS are expected to address the limitation.
AI-CS in MRI improves patient care by shortening scan times, reducing patient discomfort and anxiety, and produces high quality images for accurate diagnosis.
磁共振成像（MRI）已经彻底改变了肌肉骨骼疾病的诊断和治疗方式。并行成像（PI）和压缩感知（CS）技术可减少扫描时间，但更高的加速因子会降低图像质量。人工智能通过整合深度学习算法增强了 MRI 重建。因此，本研究旨在回顾人工智能辅助压缩感知（AI-CS）和加速因子对肌肉骨骼 MRI 扫描时间和图像质量的影响。
在 PubMed、Scopus、CINAHL、Web of Science、Cochrane Library 和 Embase 数据库中进行了数据库搜索，以确定 2022 年至 2024 年间聚焦于 AI-CS 在肌肉骨骼 MRI 中应用的相关文章。我们利用系统评价和荟萃分析的首选报告项目指南从选定的研究中提取数据。
最终综述纳入了 9 篇文章，共有 730 名参与者的总样本量。其中，7 篇文章被评为高质量，2 篇文章被认为是中等质量。使用 AI-CS 的 MRI 检查显示，腰椎扫描时间减少了 18.9-38.8%，肩部扫描时间减少了 38-40%，膝关节扫描时间减少了 54-75%，踝关节扫描时间减少了 53-63%。
与 PI 和 CS 相比，AI-CS 在肌肉骨骼 MRI 的 2D 和 3D 序列中可显著减少扫描时间并提高图像质量。在将其应用于临床之前，需要确定与传统 PI 技术相比获得更高质量图像所需的最佳加速因子。虽然人工智能辅助压缩感知的进步有望解决这一限制，但目前更高的加速因子会导致图像评分降低。
MRI 中的 AI-CS 通过缩短扫描时间、减少患者不适和焦虑，以及生成用于准确诊断的高质量图像，改善了患者的护理。
