Let distortion-guided restoration: a physics-informed learning framework to correct prostate diffusion MRI artifacts.

Background: Susceptibility-induced distortion in single-shot echo-planar imaging (ssEPI) prostate diffusion-weighted imaging (DWI) can degrade lesion conspicuity, apparent diffusion coefficient (ADC) estimation, and confidence in prostate MRI interpretation. Conventional correction strategies typically require additional acquisitions and may be limited in severe artifact settings. Purpose: To develop a physics-informed deep learning framework, distortion-guided restoration (DGR), for acquisition-free correction of ssEPI distortions in prostate DWI. Materials and methods: Distortion-guided restoration was proposed to learn the inverse of a physically simulated ssEPI distortion process. Clinical prostate DWI and T Results: On synthetic data, the proposed DGR model achieved the highest peak signal-to-noise ratio (PSNR) and lowest normalized mean squared error (NMSE) across low-b DWI (0.089; 95% CI, 0.072-0.105) and ADC maps (0.062; 95% CI, 0.053-0.072), outperforming FSL TOPUP and FUGUE (all Conclusion: This proof-of-concept study suggests that a physics-informed hybrid CNN-diffusion framework offers a practical and acquisition-free solution for correcting severe prostate DWI distortions. It warrants further development for clinical utility and implementation.