A 3D Conditional Diffusion Model for Image Quality Transfer -- An Application to Low-Field MRI
This work addresses the issue of inaccurate clinician interpretations due to poor image quality in low-resource settings, representing an incremental improvement in domain-specific image enhancement.
The paper tackles the problem of enhancing low-field MRI images, which have lower resolution and contrast than high-field scanners, by proposing a 3D conditional diffusion model that outperforms existing methods in image quality transfer and brain parcellation tasks on the HCP dataset.
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}.