AI-Assisted Compressed Sensing Enables Faster Brain MRI for the Elderly: Image Quality and Diagnostic Equivalence with Conventional Imaging

Wenquan Gu,1,* Chunhong Yang,1,* Yuhui Wang,2 Wentao Hu,3 Dongmei Wu,4 Sunmei Cai,1 Guoxiong Hong,1 Peng Hu,5 Qi Zhang,1 Yongming Dai5 1Department of Radiology, Shanghai Punan Hospital of Pudong New Area, Shanghai, People’s Republic of China; 2Department of Neurology, Shanghai Punan Hospital of Pudong New Area, Shanghai, People’s Republic of China; 3Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 4Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People’s Republic of China; 5School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yongming Dai, School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, People’s Republic of China, Tel/Fax +86 21 20685265, Email [email protected] Qi Zhang, Department of Radiology, Shanghai Punan Hospital of Pudong New Area, Shanghai, People’s Republic of China, Tel/Fax +86 21 20302000, Email [email protected]: Conventional brain MRI protocols are time-consuming, which can lead to patient discomfort and inefficiency in clinical settings. This study aims to assess the feasibility of using artificial intelligence-assisted compressed sensing (ACS) to reduce brain MRI scan time while maintaining image quality and diagnostic accuracy compared to a conventional imaging protocol.Patients and Methods: Seventy patients from the department of neurology underwent brain MRI scans using both conventional and ACS protocols, including axial and sagittal T2-weighted fast spin-echo sequences and T2-fluid attenuated inversion recovery (FLAIR) sequence. Two radiologists independently evaluated image quality based on avoidance of artifacts, boundary sharpness, visibility of lesions, and overall image quality using a 5-point Likert scale. Pathological features, including white matter hyperintensities, lacunar infarcts, and enlarged perivascular spaces, were also assessed. The interchangeability of the two protocols was determined by calculating the 95% confidence interval (CI) for the individual equivalence index. Additionally, Cohen’s weighted kappa statistic was used to assess inter-protocol intra-observer agreement.Results: The ACS images demonstrated superior quality across all qualitative features compared to the conventional ones. Both protocols showed no significant difference in detecting pathological conditions. The 95% CI for the individual equivalence index was below 5% for all variables except enlarged perivascular spaces, indicating the interchangeability of the conventional and ACS protocols in most cases. The inter-rater reliability between the two radiologists was strong, with kappa values of 0.78, 0.74, 0.70 and 0.86 for image quality evaluation and 0.74, 0.80 and 0.70 for diagnostic performance, indicating good-to-excellent agreement in their evaluations.Conclusion: The ACS technique reduces brain MRI scan time by 29.2% while achieving higher image quality and equivalent diagnostic accuracy compared to the conventional protocol. This suggests that ACS could be potentially adopted for routine clinical use in brain MRI.Keywords: brain, magnetic resonance imaging, fast imaging, deep learning, compressed sensing