Distillation-Driven Diffusion Model for Multi-Scale MRI Super-Resolution: Make 1.5T MRI Great Again
Journal:
arXiv
Published Date:
Jan 30, 2025
Abstract
Magnetic Resonance Imaging (MRI) offers critical insights into
microstructural details, however, the spatial resolution of standard 1.5T
imaging systems is often limited. In contrast, 7T MRI provides significantly
enhanced spatial resolution, enabling finer visualization of anatomical
structures. Though this, the high cost and limited availability of 7T MRI
hinder its widespread use in clinical settings. To address this challenge, a
novel Super-Resolution (SR) model is proposed to generate 7T-like MRI from
standard 1.5T MRI scans. Our approach leverages a diffusion-based architecture,
incorporating gradient nonlinearity correction and bias field correction data
from 7T imaging as guidance. Moreover, to improve deployability, a progressive
distillation strategy is introduced. Specifically, the student model refines
the 7T SR task with steps, leveraging feature maps from the inference phase of
the teacher model as guidance, aiming to allow the student model to achieve
progressively 7T SR performance with a smaller, deployable model size.
Experimental results demonstrate that our baseline teacher model achieves
state-of-the-art SR performance. The student model, while lightweight,
sacrifices minimal performance. Furthermore, the student model is capable of
accepting MRI inputs at varying resolutions without the need for retraining,
significantly further enhancing deployment flexibility. The clinical relevance
of our proposed method is validated using clinical data from Massachusetts
General Hospital. Our code is available at https://github.com/ZWang78/SR.
