In this work, we present X-Diffusion, a cross-sectional diffusion model tailored for Magnetic Resonance Imaging (MRI) data. X-Diffusion is capable of generating the entire MRI volume from just a single MRI slice or optionally from few multiple slices, setting new benchmarks in the precision of synthesized MRIs from extremely sparse observations. The uniqueness lies in the novel view-conditional training and inference of X-Diffusion on MRI volumes, allowing for generalized MRI learning. Our evaluations span both brain tumour MRIs from the BRATS dataset and full-body MRIs from the UK Biobank dataset. Utilizing the paired pre-registered Dual-energy X-ray Absorptiometry (DXA) and MRI modalities in the UK Biobank dataset, X-Diffusion is able to generate detailed 3D MRI volume from a single full-body DXA. Remarkably, the resultant MRIs not only stand out in precision on unseen examples (surpassing state-of-the-art results by large margins) but also flawlessly retain essential features of the original MRI, including tumour profiles, spine curvature, brain volume, and beyond. Furthermore, the trained X-Diffusion model on the MRI datasets attains a generalization capacity out-of-domain (e.g. generating knee MRIs even though it is trained on brains). The code is available on the project website https://emmanuelleb985.github.io/XDiffusion/ .

翻译：本文提出X-Diffusion，一种专为磁共振成像（MRI）数据定制的横截面扩散模型。该模型能够仅通过单张MRI切片（或可选的多张切片）生成完整MRI体素，在极端稀疏观测条件下合成MRI的精度上树立了新标杆。其独特创新在于对MRI体素实施新颖的视角条件训练与推理，实现了泛化的MRI学习。我们基于BRATS数据集中的脑肿瘤MRI与UK Biobank数据集中的全身MRI进行了双重评估。通过利用UK Biobank数据集中配准的双能X射线吸收测定法（DXA）与MRI模态对，X-Diffusion可从单张全身DXA图像生成精细3D MRI体素。值得注意的是，生成的MRI不仅在对未见示例的精度上表现卓越（大幅超越现有最优结果），还能完美保留原始MRI的关键特征，包括肿瘤形态、脊柱曲度、脑体积等。此外，在MRI数据集上训练的X-Diffusion模型展现出跨领域泛化能力（例如即使仅用脑部数据训练，也能生成膝关节MRI）。代码已发布于项目网站：https://emmanuelleb985.github.io/XDiffusion/