PW-GAN: Pseudo-Warping Field Guided GAN for Unsupervised Denoising of Fetal Brain MRI Images.

Abstract

Fetal brain MRI commonly uses thick-slice acquisitions to mitigate motion artifacts and preserve in-plane image quality. Multiple motion-corrupted stacks of 2D slices can be retrospectively reconstructed into isotropic 3D volumes; however, volumes reconstructed from thicker slices generally exhibit lower image quality than those reconstructed from thinner slices because limited through-plane information results in residual noise and inter-slice structural discontinuities. Existing post-reconstruction denoising methods are limited by their dependence on large-scale datasets comprising noisy-clean image pairs or insufficient modeling of inter-slice structural consistency. In this work, we propose a Pseudo-Warping Field Guided Unsupervised Generative Adversarial Network (PW-GAN), which formulates post-reconstruction denoising as unpaired translation between low- and high-quality MRI domains. By incorporating a pseudo-warping field module, our method significantly enhances inter-slice continuity in the reconstructed volumes while effectively suppressing residual noise and inter-slice structural discontinuities. On simulated data, PW-GAN achieved a PSNR of 39.65 dB and an SSIM of 0.9932. On real clinical data, it reduced the inter-slice warping error by up to 40% compared with unsupervised baselines. These results demonstrate that PW-GAN outperforms existing unsupervised models and achieves performance comparable to several state-of-the-art supervised methods. Qualitatively, PW-GAN produced cleaner results with smoother inter-slice structural transitions while preserving anatomical structures, demonstrating its effectiveness in post-reconstruction denoising of fetal brain MRI acquired from thick-slice scans.