Self‐supervised multicontrast super‐resolution for diffusion‐weighted prostate MRI

作者：Batuhan Gündoğdu, Milica Medved, Aritrick Chatterjee, Roger Engelmann, Avery Rosado, Grace Lee, Nisa Cem Ören, Aytekin Oto, Gregory S. Karczmar · 发表于：Magnetic Resonance in Medicine · 年份：2024 · DOI：10.1002/mrm.30047 · 被引用次数：8 · 研究领域：Advanced Neuroimaging Techniques and Applications、MRI in cancer diagnosis、Prostate Cancer Diagnosis and Treatment

PURPOSE: This study addresses the challenge of low resolution and signal-to-noise ratio (SNR) in diffusion-weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b-values through multiple acquisitions, but this results in diminished image resolution due to motion-induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions. METHODS: We introduce a novel approach employing a "Perturbation Network" to learn subvoxel-size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low-resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher-resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion-correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans. RESULTS: of super-resolution images were assessed to have superior diagnostic quality compared to interpolated images. CONCLUSION: High-resolution details in DWI can be obtained without the need for high-resolution training data. One notable advantage of the proposed method is that it does not require a super-resolution training set. This is important in clinical practice because the proposed method can...