SubDIVIDE: Efficient tensor‐valued diffusion magnetic resonance imaging via joint diffusional variance decomposition modeling and subspace reconstruction
TL;DR
A model‐aware subspace reconstruction framework that integrates DTD‐based DIVIDE modeling with low‐rank signal representations is proposed that suggests that subspace‐constrained reconstruction provides a principled pathway toward efficient and robust multidimensional diffusion MRI.
Abstract
Tensor‐valued diffusion MRI enables disentangling microscopic diffusion anisotropy and isotropic heterogeneity through models such as diffusional variance decomposition (DIVIDE) and diffusion tensor distributions (DTD). However, these models require dense sampling across multiple b ‐values and b‐tensor shapes, making them highly sensitive to noise and acquisition time constraints. In this work, we propose a model‐aware subspace reconstruction framework that integrates DTD‐based DIVIDE modeling with low‐rank signal representations. By exploiting the intrinsic low‐dimensional structure of DIVIDE signals in the diffusion encoding domain, we construct a physics‐consistent subspace that enables joint reconstruction and parameter estimation from highly undersampled data. Simulation and in vivo experiments demonstrate that the proposed approach substantially improves the stability and precision of DIVIDE‐derived microstructural parameters compared with conventional reconstruction methods, while enabling significant acceleration. These results suggest that subspace‐constrained reconstruction provides a principled pathway toward efficient and robust multidimensional diffusion MRI.