A semi-data-driven cellular microstructural model considering cell size distribution in diffusion MRI

Motivation: Incorporating the impact of cell size distribution is challenging in current dMRI-based microstructural imaging. All relevant models fail to provide an analytical signal expression, but instead replace the intracellular signal with the sum of signal terms corresponding to different cell diameters. Although this is intuitive, subsequent equations are usually ill-conditioned and hard to resolve. Goal(s): To derive the analytical expression for dMRI signals and rebuild a cellular microstructural model. Approach: We performed theoretical modelling based on simulated signals and validations on numerical simulations and in-vitro cell experiments. Results: A semi-data-driven cellular microstructural model is proposed and it outperforms the published method. Impact: This work provides the first analytical expression for dMRI signals while incorporating cell size distribution. The proposed microstructural model can extract not only accurate mean cell size, but also distribution information, which provides an additional biomarker for tumor monitoring.