DEMIST: \underline{DE}coupled \underline{M}ulti-stream latent d\underline{I}ffusion for Quantitative Myelin Map \underline{S}yn\underline{T}hesis
This work addresses a domain-specific problem for multiple sclerosis assessment by enabling faster and more accessible myelin mapping, though it is incremental as it builds on existing diffusion models with novel conditioning mechanisms.
The paper tackled the problem of synthesizing quantitative myelin maps (PSR) from standard MRI scans to avoid specialized 20-30 minute qMT imaging, achieving state-of-the-art performance with sharper boundaries and better quantitative agreement compared to baseline methods.
Quantitative magnetization transfer (qMT) imaging provides myelin-sensitive biomarkers, such as the pool size ratio (PSR), which is valuable for multiple sclerosis (MS) assessment. However, qMT requires specialized 20-30 minute scans. We propose DEMIST to synthesize PSR maps from standard T1w and FLAIR images using a 3D latent diffusion model with three complementary conditioning mechanisms. Our approach has two stages: first, we train separate autoencoders for PSR and anatomical images to learn aligned latent representations. Second, we train a conditional diffusion model in this latent space on top of a frozen diffusion foundation backbone. Conditioning is decoupled into: (i) \textbf{semantic} tokens via cross-attention, (ii) \textbf{spatial} per-scale residual hints via a 3D ControlNet branch, and (iii) \textbf{adaptive} LoRA-modulated attention. We include edge-aware loss terms to preserve lesion boundaries and alignment losses to maintain quantitative consistency, while keeping the number of trainable parameters low and retaining the inductive bias of the pretrained model. We evaluate on 163 scans from 99 subjects using 5-fold cross-validation. Our method outperforms VAE, GAN and diffusion baselines on multiple metrics, producing sharper boundaries and better quantitative agreement with ground truth. Our code is publicly available at https://github.com/MedICL-VU/MS-Synthesis-3DcLDM.