Deep Learning Superresolution for 7T Knee MR Imaging: Impact on Image Quality and Diagnostic Performance

Pinzhen Chen; Libo Xu; Boyang Pan; Jing Li; Yuting Wang; Ran Xiong; Xiaoli Gou; Long Qing; Wenjing Hou; Gong Nan-Jie; Wei Chen

doi:10.48550/arxiv.2601.02436

Deep Learning Superresolution for 7T Knee MR Imaging: Impact on Image Quality and Diagnostic Performance

arXiv (Cornell University) 2026 Preprint Open access

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Authors: Pinzhen Chen, Libo Xu, Boyang Pan, Jing Li, Yuting Wang, Ran Xiong, Xiaoli Gou, Long Qing, Wenjing Hou, Gong Nan-Jie, Wei Chen

DOI: 10.48550/arxiv.2601.02436

Cited by: 0

Abstract

Background: Deep learning superresolution (SR) may enhance musculoskeletal MR image quality, but its diagnostic value in knee imaging at 7T is unclear. Objectives: To compare image quality and diagnostic performance of SR, low-resolution (LR), and high-resolution (HR) 7T knee MRI. Methods: In this prospective study, 42 participants underwent 7T knee MRI with LR (0.8*0.8*2 mm3) and HR (0.4*0.4*2 mm3) sequences. SR images were generated from LR data using a Hybrid Attention Transformer model. Three radiologists assessed image quality, anatomic conspicuity, and detection of knee pathologies. Arthroscopy served as reference in 10 cases. Results: SR images showed higher overall quality than LR (median score 5 vs 4, P

Fields of Study & Subjects

Advanced MRI Techniques and Applications Ultrasound Imaging and Elastography Total Knee Arthroplasty Outcomes

Publication details

Journal / source: arXiv (Cornell University)
Publication date: 5 January 2026
Document type: Preprint