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}.

我们提出了一种3D条件扩散模型来改进3D体积数据，特别是低磁场MR图像，并在我们的网络中引入了跨批量机制以确保在小的3D补丁下有更广泛的上下文意识。对于IQT和脑区划，我们在公开的Human Connectome Project（HCP）数据集上的实验证明了我们的模型在定量和定性上优于现有方法。代码公开可用于https://github.com/edshkim98/DiffusionIQT。

一种用于图像质量转换的3D条件扩散模型 - 一种针对低场MRI的应用