Prior-Guided Implicit Neural Representations for Single-Subject Diffusion MRI Super-Resolution

2026-09-01

AI Summary

该研究提出了一种基于先验引导的隐式神经表示方法，通过迁移学习和微调实现单受试者扩散MRI超分辨率，解决了高分辨率扫描时间长的问题。

Abstract

Resolving complex fiber geometries in brain white matter requires high-resolution diffusion MRI at the cost of long acquisition times. This leads many clinical protocols to opt for low-resolution scans, making downstream microstructure estimation and tractography challenging. Implicit neural representations (INRs) can model the diffusion signal continuously, enabling native single-subject super-resolution by querying the network at arbitrary spatial coordinates, yet existing methods often suffer from long training times and lack a mechanism to incorporate anatomical priors to regularize super-resolution by constraining the space of plausible reconstructions. To address these limitations, we propose a novel transfer-learning framework that pre-trains an INR on a high-resolution template and then adapts it to subject-specific scans via registration and fine-tuning. For $4\times$ through-plane super-resolution from 5 mm to 1.25 mm on Human Connectome Project (HCP) data, our method reduces NRMSE by 36-49% and increases FSIM by 24-43% over a recent baseline with $6\times$ faster training, outperforming competing INR-based methods across both image quality and domain-specific metrics. Code is available on the project page at https://abdulkaderghandoura.github.io/research/msc-thesis/ .

Problem

• diffusion MRI
• super-resolution
• implicit neural representations
• anatomical priors

Innovation

• Implicit Neural Representations
• Transfer Learning
• Anatomical Priors
• Super-Resolution
• Diffusion MRI