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Abstract #3423

Estimating cortical soma and neurite densities from diffusion MRI measures using a machine learning approach

Tianjia Zhu1,2, Minhui Ouyang1, Nikou Lei3, David Wolk4, Paul Yushkevich5, and Hao Huang1,5
1Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States, 2Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States, 3Department of Physics, University of Washington Seattle, Seattle, WA, United States, 4Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States, 5Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States

Diffusion MRI (dMRI) has ushered in a new era in which conventional brain cortical histological measures such as soma and neurite densities may be assessed noninvasively through advanced dMRI. However, analytical dMRI microstructural models are restricted by the model assumptions and lack of validation from quantitative histology data. Individual dMRI parameters characterize only limited microstructural information. By leveraging a variety of dMRI-based parameters delineating cortical microstructure from multiple aspects, we established a machine learning based method accurately estimating cortical soma and neurite densities in the cortex, paving the way for data-driven noninvasive virtual histology for potential applications to Alzheimer’s diseases.

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