用稀疏CT重建脑部MRI，提升低剂量扫描下的图像质量
ReBrain: Brain MRI Reconstruction from Sparse CT Slice via Retrieval-Augmented Diffusion
- 通过检索相似CT片并结合扩散模型生成连续脑MRI
- 在稀疏数据下实现领先性能，峰值信噪比达32.4
- 适合临床低剂量CT患者，也适用于罕见病建模
磁共振成像（MRI）在脑疾病诊断中至关重要，但部分患者因生理或临床限制无法进行。现有研究尝试从计算机断层扫描（CT）合成MRI，但低剂量协议常导致切片稀疏、层面分辨率差，使完整脑MRI重建极具挑战。为此，我们提出ReBrain：一种基于检索增强的扩散框架，用于脑部MRI重建。给定任意3D稀疏CT扫描，先用布朗桥扩散模型（BBDM）在二维方向生成MRI切片；同时，通过微调的检索模型从大型先验数据库中获取结构与病理相似的CT切片，作为参考，通过ControlNet分支引导中间MRI切片生成，确保结构连贯性。针对数据库中缺乏合适参考的情况，引入球面线性插值提供补充指导。在SynthRAD2023和BraTS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表现。
原文摘要 · Abstract (English)
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.
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