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Abstract #0257

Diffusion-MRI based Estimation of Cortical Architecture using Machine-learning (DECAM)

Tianjia Zhu1,2, Minhui Ouyang1, and Hao Huang1,3
1Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States, 2Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States, 3Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States

Synopsis

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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