A 3D Conditional Diffusion Model for Image Quality Transfer - An Application to Low-Field MRI

Seunghoi Kim, Daniel C. Alexander, Ahmed Karam Eldaly, Matteo Figini, Henry F J Tregidgo

Keywords: diffusion model, 3D super-resolution, image quality transfer, medical image enhancement

TL;DR: We propose a 3D conditional diffusion model for enhancing Low-field MR images, with a cross-batch mechanism to solve the limitation of patch-based method, outperforming previous studies in image quality transfer and brain parcellation tasks.

Abstract: Low-field (LF) MRI scanners (<1T) are still prevalent in settings with limited resources or unreliable power supply. However, they often yield images with lower spatial resolution and contrast than high-field (HF) scanners. This quality disparity can result in inaccurate clinician interpretations. Image Quality Transfer (IQT) has been developed to enhance the quality of images by learning a mapping function between low and high-quality images. Existing IQT models often fail to restore high-frequency features, leading to blurry output. In this paper, we propose a 3D conditional diffusion model to improve 3D volumetric data, specifically LF MR images. Additionally, we incorporate a cross-batch mechanism into the self-attention and padding of our network, ensuring broader contextual awareness even under small 3D patches. Experiments on the publicly available Human Connectome Project (HCP) dataset for IQT and brain parcellation demonstrate that our model outperforms existing methods both quantitatively and qualitatively. The code is publicly available at \url{https://github.com/edshkim98/DiffusionIQT}.

Submission Number: 47