用扩散模型补全缺损的脑部MRI，保持解剖结构一致
SLaM-DiMM: Shared Latent Modeling for Diffusion Based Missing Modality Synthesis in MRI
- 基于共享潜在空间设计扩散模型，跨模态生成
- 在BraTS数据集上生成图像保真度高、结构连贯性好
- 适合临床缺失模态补全，尤其对多模态分析有帮助
脑部MRI通常包含四种模态：增强和非增强T1加权（T1ce、T1w）、T2加权（T2w）及Flair。这些模态互补信息有助于学习更丰富的特征，支持异常检测等下游任务。但临床上常因各种原因缺少某些模态，导致缺模态生成成为医学图像分析的关键挑战。本文提出SLaM-DiMM，一种基于扩散模型的新框架，可从已有的任意模态中合成其余三种目标模态。该方法不仅生成高质量图像，还通过专门设计的结构一致性增强机制，确保体积深度上的解剖结构连贯性。在BraTS-Lighthouse-2025挑战赛数据集上的定性和定量评估表明，该方法能生成解剖合理且结构一致的结果。代码已开源。
原文摘要 · Abstract (English)
Brain MRI scans are often found in four modalities, consisting of T1-weighted with and without contrast enhancement (T1ce and T1w), T2-weighted imaging (T2w), and Flair. Leveraging complementary information from these different modalities enables models to learn richer, more discriminative features for understanding brain anatomy, which could be used in downstream tasks such as anomaly detection. However, in clinical practice, not all MRI modalities are always available due to various reasons. This makes missing modality generation a critical challenge in medical image analysis. In this paper, we propose SLaM-DiMM, a novel missing modality generation framework that harnesses the power of diffusion models to synthesize any of the four target MRI modalities from other available modalities. Our approach not only generates high-fidelity images but also ensures structural coherence across the depth of the volume through a dedicated coherence enhancement mechanism. Qualitative and quantitative evaluations on the BraTS-Lighthouse-2025 Challenge dataset demonstrate the effectiveness of the proposed approach in synthesizing anatomically plausible and structurally consistent results. Code is available at https://github.com/BheeshmSharma/SLaM-DiMM-MICCAI-BraTS-Challenge-2025.
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