Jointly Multi-Model Microstructural Estimation on Diffusion MRI with Contextual Features Aggregation

Motivation: DMRI techniques allow for examining microarchitecture profiles and subtle tissue changes, yet its clinical applicability needs to be improved by lengthy acquisition times and data processing. Goal(s): We aim to develop a method for effectively characterizing multi-functional microstructural parameters, enhancing clinical applications in dMRI across diverse acquisition protocols. Approach: We develop a novel network with Contextual Features Aggregation for joint estimation of multi-model microstructural features across various acquisition protocols. Our network captures multi-scale features and adaptively integrates contextual information, enhancing estimation accuracy. Results: The proposed method demonstrates an adequate, rapid, and robust estimation of multiple microstructural parameters, rendering it useful for clinical applications. Impact: This research proposes an innovative method for Jointly estimating Microstructure maps across acquisition Protocols (JEMAP). It lays the groundwork for future techniques that integrate clinical data acquisition protocols and improve sensitivity to microstructure measures.