SLaM-DiMM: Shared Latent Modeling for Diffusion Based Missing Modality Synthesis in MRI
This addresses a critical challenge in medical image analysis for clinical applications, though it appears incremental as it builds on existing diffusion models for a specific domain.
The paper tackles the problem of missing MRI modalities in clinical practice by proposing SLaM-DiMM, a diffusion-based framework that synthesizes any of four target MRI modalities from available ones, achieving high-fidelity and structurally coherent results as demonstrated on the BraTS-Lighthouse-2025 dataset.
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.