CT-based subchondral bone microstructural analysis in knee osteoarthritis via MR-guided distillation learning

作者：Yuqi Hu, Xiangyu Zhao, G. Qing, Kai Xie, Chenglei Liu, Lichi Zhang · 发表于：Frontiers in Radiology · 年份：2026 · DOI：10.3389/fradi.2026.1798348 · 被引用次数：1 · 研究领域：Osteoarthritis Treatment and Mechanisms、Rheumatoid Arthritis Research and Therapies、Bone and Joint Diseases

Reliable analysis of subchondral trabecular microstructure is critical for knee osteoarthritis assessment. However, this analysis largely relies on high-resolution MRI acquired using balanced fast field echo (BFFE) sequences, which are rarely included in routine clinical protocols. Clinical CT is widely acquired, yet its limited spatial resolution and soft-tissue contrast makes direct trabecular parameter estimation unreliable. Therefore, it is specifically demanded to enable accurate trabecular microstructural analysis and osteoarthritis diagnosis using routine clinical CT, while also approaching the reliability of MR-based analysis. In this paper, we propose CT-based Subchondral Microstructural Analysis (CT-SMA) method, which utilizes distillation learning technology to transfer high-resolution structural knowledge from MR to CT while enforcing CT-only inference. The core idea of CT-SMA is to transfer microstructural knowledge learned from high-resolution MR to CT through cross-modal knowledge distillation, using a pre-trained MR-based teacher model to supervise CT-based student model on feature maps. To support effective distillation, CT-SMA further introduces a synthesis-based, multi-stage MR-CT registration strategy that establishes patch-level correspondences across modalities, despite substantial differences in resolution, contrast, and appearance. Experiments on a clinical knee imaging cohort demonstrate that CT-SMA substantially improves CT-based trabecular parameter...