Online Program Home
My Program

Abstract Details

Activity Number: 28 - SPEED: A Mixture of Topics in Health, Computing, and Imaging
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #329762 Presentation
Title: A Deep Learning Approach to the Estimation of Bias and Variance in HARDI
Author(s): Allison Hainline* and Hakmook Kang and Bennett Landman
Companies: Vanderbilt University and Vanderbilt University Medical Center and Vanderbilt University
Keywords: diffusion MRI; HARDI; deep learning; neuroimaging; neural network

The bias and variance of high angular resolution diffusion imaging (HARDI) methods have been shown to be estimable via simulation extrapolation (SIMEX) and the wild bootstrap, respectively. These methods, however, can be extremely computationally intensive, as metrics are determined on a voxel-wise basis. We propose an updated solution, utilizing a deep neural network to estimate both bias and variance, voxel-wise, without requiring extensive computation. Results are compared to the SIMEX and bootstrap estimates as well as the true values.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program