用分量建模的扩散模型，让稀疏DTI重建更准更合理。
TensorLDM: A Component-Wise Latent Diffusion Model for Volumetric DTI Reconstruction from Sparse DWIs
- 分六个张量分量处理，用双编码器+共享条件保持解剖一致性
- 在单壳四体积采集下，张量物理有效性接近真实值（违规率1.54%）
- 适合需要高保真张量重建的临床DTI加速场景
从稀疏弥散加权图像（DWIs）重建扩散张量对加速临床扩散张量成像（DTI）至关重要，但现有深度学习方法常生成解剖不一致或物理上不可信的张量。本文提出TensorLDM，一种分量级潜在扩散模型，通过两个分组编码器（分别处理对角与非对角元素）并利用共享的DWI条件保持解剖一致性。该模型采用解剖条件自编码器，使潜在表示聚焦于张量属性而非结构信息。在自编码器精炼与扩散微调中引入共享跨分量注意力机制，建模分量间依赖关系；同时使用多专家（MoE）DWI条件器实现分量自适应条件输入。在人类连接组计划（HCP）数据集上，单壳四体积稀疏采集条件下，TensorLDM在下游纤维追踪和张量重建方面表现最优，物理有效性接近真实水平（SPD违规率1.54%对比1.40%），体素级重建精度最佳或相当。基于对数欧氏度量（LEM）的测地线张量误差验证了其优势。
原文摘要 · Abstract (English)
Reconstructing diffusion tensors from sparse DWIs is critical for accelerating Diffusion Tensor Imaging (DTI) in clinical settings, yet current deep learning approaches frequently yield anatomically inconsistent or physically implausible tensors. We introduce TensorLDM, a component-wise latent diffusion model that processes the six tensor components through two group-specific encoders (for diagonal and off-diagonal elements) while maintaining anatomical consistency via shared DWI conditioning. TensorLDM uses an Anatomy-Conditioned Autoencoder that encourages the latent to focus on tensor properties rather than re-encoding structural information. A shared Cross-Component Attention (CCA) mechanism, applied in both autoencoder refinement and diffusion fine-tuning, models inter-component dependencies, while a Mixture-of-Experts (MoE) DWI conditioner provides component-adaptive conditioning. On the Human Connectome Project (HCP) dataset under a single-shell, four-volume sparse acquisition, TensorLDM produces the most accurate downstream tractography and tensors with near-ground-truth physical validity (SPD-violation rate 1.54% vs. 1.40%), with the best or comparable voxel-wise reconstruction accuracy. Geodesic tensor error measured by the Log-Euclidean Metric (LEM) corroborates these gains.
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