Patient-specific MRI super-resolution via implicit neural representations and knowledge transfer

作者：Yunxiang Li, Yen‐Peng Liao, Jing Wang, Weiguo Lu, You Zhang · 发表于：Physics in Medicine and Biology · 年份：2025 · DOI：10.1088/1361-6560/adbed4 · 被引用次数：3 · 研究领域：Medical Imaging and Analysis、Advanced Image Processing Techniques、Radiomics and Machine Learning in Medical Imaging

Abstract Objective. Magnetic resonance imaging (MRI) is a non-invasive imaging technique that provides high soft tissue contrast, playing a vital role in disease diagnosis and treatment planning. However, due to limitations in imaging hardware, scan time, and patient compliance, the resolution of MRI images is often insufficient. Super-resolution (SR) techniques can enhance MRI resolution, reveal more detailed anatomical information, and improve the identification of complex structures, while also reducing scan time and patient discomfort. However, traditional population-based models trained on large datasets may introduce artifacts or hallucinated structures, which compromise their reliability in clinical applications. Approach. To address these challenges, we propose a patient-specific knowledge transfer implicit neural representation (KT-INR) SR model. The KT-INR model integrates a dual-head INR with a pre-trained generative adversarial network (GAN) model trained on a large-scale dataset. Anatomical information from different MRI sequences of the same patient, combined with the SR mappings learned by the GAN model on a population-based dataset, is transferred as prior knowledge to the INR. This integration enhances both the performance and reliability of the SR model. Main results. We validated the effectiveness of the KT-INR model across three distinct clinical SR tasks on the brain tumor segmentation dataset. For task 1, KT-INR achieved an average structural similarity ...