Abstract #3423

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

Tianjia Zhu^1,2, Minhui Ouyang¹, Nikou Lei³, David Wolk⁴, Paul Yushkevich⁵, and Hao Huang^1,5

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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