Let Distortion Guide Restoration (DGR): A physics-informed learning framework for Prostate Diffusion MRI

TL;DR

DGR is a physics-informed hybrid CNN-diffusion framework that effectively corrects severe susceptibility-induced distortions in prostate DWI without additional acquisitions, improving image quality and diagnostic confidence.

Contribution

This work introduces a novel physics-informed learning framework combining CNN and diffusion models for acquisition-free distortion correction in prostate DWI.

Findings

DGR outperforms FSL TOPUP and FUGUE in PSNR and NMSE on synthetic data.

DGR improves geometric fidelity and radiologist-rated image quality in real clinical cases.

Learning the inverse of a physical distortion model offers a practical alternative to traditional correction methods.

Abstract

We present Distortion-Guided Restoration (DGR), a physics-informed hybrid CNN-diffusion framework for acquisition-free correction of severe susceptibility-induced distortions in prostate single-shot EPI diffusion-weighted imaging (DWI). DGR is trained to invert a realistic forward distortion model using large-scale paired distorted and undistorted data synthesized from distortion-free prostate DWI and co-registered T2-weighted images from 410 multi-institutional studies, together with 11 measured B0 field maps from metal-implant cases incorporated into a forward simulator to generate low-b DWI (b = 50 s per mm squared), high-b DWI (b = 1400 s per mm squared), and ADC distortions. The network couples a CNN-based geometric correction module with conditional diffusion refinement under T2-weighted anatomical guidance. On a held-out synthetic validation set (n = 34) using ground-truth simulated distortion fields, DGR achieved higher PSNR and lower NMSE than FSL TOPUP and FUGUE. In 34 real clinical studies with severe distortion, including hip prostheses and marked rectal distension, DGR improved geometric fidelity and increased radiologist-rated image quality and diagnostic confidence. Overall, learning the inverse of a physically simulated forward process provides a practical alternative to acquisition-dependent distortion-correction pipelines for prostate DWI.