Keywords: Diffusion/other diffusion imaging techniques, Microstructure, machine learning/artificial intelligence, neuroAdvanced diffusion MRI (dMRI) has enabled noninvasive assessment of conventional cortical histological measures. However, analytical models are limited by their restrictive model assumptions and lack of validation from quantitative histology. We have developed a Diffusion-MRI based Estimation of Cortical micro-Architecture (DECAM) method using a novel deep learning technique Best Response Constraint Generative Adversarial Network (BRC-GAN) for accurately estimating cortical soma density (SD) leveraging rich dMRI data information. By providing high-fidelity, reproducible whole-brain estimated SD maps validated with histology, DECAM paves the way for data-driven noninvasive virtual histology for potential applications such as Alzheimer’s diseases.
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