  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}.
