Multi-Contrast MRI Super-Resolution in Brain Tumors: Arbitrary-Scale Implicit Sampling and Unsupervised Fine-Tuning

作者：Wenxuan Chen, Yulin Wang, Zhongsen Li, Shuai Wang, Sirui Wu, Chuyu Liu, Yonghong Fan, Benqi Zhao, Zhuozhao Zheng, Dinggang Shen, Xiaolei Song · 发表于：IEEE Transactions on Medical Imaging · 年份：2025 · DOI：10.1109/tmi.2025.3628113 · 被引用次数：2 · 研究领域：Advanced Image Processing Techniques、Advanced MRI Techniques and Applications、MRI in cancer diagnosis

Multi-contrast magnetic resonance imaging (MRI) has important value in clinical applications because it can reflect comprehensive tissue characterization from anatomy and function to metabolism. Previous studies utilize abundant details in high-resolution (HR) reference (Ref) images to guide the super-resolution (SR) of low-resolution (LR) images, termed multi-contrast MRI SR. Yet, their clinical applications are hindered by: 1) discrepancies in MRI equipment and acquisition protocols across hospitals (which lead to gaps in data distribution), and 2) lack of paired LR and HR images in certain modalities for supervised training. Herein, we rethink multi-contrast MRI from a clinical perspective, and propose an implicit sampling and generation (ISG) network plus an unsupervised fine-tuning (FT) framework. Briefly, the ISG network possesses a powerful representation capability, enabling arbitrary-scale LR inputs and SR outputs. The fine-tuning framework, as a test-time training technique, allows models to be adapted to testing data. Experiments are conducted on two clinical datasets containing amide proton transfer weighted (APTw) images from tumor patients and fluid-attenuated inversion recovery (FLAIR) images from a 5T scanner, respectively. For tumor patients, our ISG+FT proves $4{\times }$ SR capacity in APTw metabolic images, receiving good recognition from radiologists. In both quantitative and qualitative evaluations, ISG+FT outperforms state-of-the-art baselines. The abla...