Advanced diffusion MRI (dMRI) has enabled noninvasive microstructural assessment that can be only conventionally measured with histology1-9. However, analytical dMRI models are limited by their restrictive model assumptions, lack of validation, and biased microstructural measures. We have developed Diffusion-MRI based Estimation of Cortical Architecture using Machine-learning (DECAM), a data-driven dMRI-based method accurately estimating cortical soma and neurite densities (SD and ND) in the cortex10 leveraging a variety of complementary dMRI contrasts. By providing high-fidelity estimated soma and neurite density 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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