Magnetic Resonance Imaging (MRI) plays a crucial role in brain disease diagnosis, but it is not always feasible for certain patients due to physical or clinical constraints. Recent studies attempt to synthesize MRI from Computed Tomography (CT) scans; however, low-dose protocols often result in highly sparse CT volumes with poor through-plane resolution, making accurate reconstruction of the full brain MRI volume particularly challenging. To address this, we propose ReBrain, a retrieval-augmented diffusion framework for brain MRI reconstruction. Given any 3D CT scan with limited slices, we first employ a Brownian Bridge Diffusion Model (BBDM) to synthesize MRI slices along the 2D dimension. Simultaneously, we retrieve structurally and pathologically similar CT slices from a comprehensive prior database via a fine-tuned retrieval model. These retrieved slices are used as references, incorporated through a ControlNet branch to guide the generation of intermediate MRI slices and ensure structural continuity. We further account for rare retrieval failures when the database lacks suitable references and apply spherical linear interpolation to provide supplementary guidance. Extensive experiments on SynthRAD2023 and BraTS demonstrate that ReBrain achieves state-of-the-art performance in cross-modal reconstruction under sparse conditions.

翻译：磁共振成像（MRI）在脑部疾病诊断中发挥着至关重要的作用，但由于物理或临床限制，某些患者并不总是可行。近期研究尝试从计算机断层扫描（CT）图像合成MRI；然而，低剂量协议通常导致CT体数据高度稀疏且层间分辨率较差，使得完整脑部MRI体积的精确重建尤为困难。为解决此问题，我们提出了ReBrain，一种用于脑部MRI重建的检索增强扩散框架。给定任意具有有限切片的3D CT扫描，我们首先采用布朗桥扩散模型（BBDM）沿二维维度合成MRI切片。同时，我们通过微调的检索模型从综合先验数据库中检索结构和病理相似的CT切片。这些检索到的切片作为参考，通过ControlNet分支融入以指导中间MRI切片的生成并确保结构连续性。我们进一步考虑了当数据库缺乏合适参考时可能出现的罕见检索失败情况，并应用球面线性插值以提供补充指导。在SynthRAD2023和BraTS数据集上的大量实验表明，ReBrain在稀疏条件下实现了跨模态重建的最先进性能。