Abstract #2592
Evaluating the accuracy of diffusion models at multiple b-values with cross-validation
Ariel Rokem 1 , Kimberly L Chan 1 , Jason D Yeatman 1 , Franco Pestilli 1 , Aviv Mezer 2 , and Brian A Wandell 2
1
Stanford University, Stanford, CA, United
States,
2
Stanford
University, Stanford, California, United States
Models of diffusion MRI (DWI) are used for inferences
about the properties of the tissue and fiber
orientations. Though stability of DWI model parameters
is often evaluated, there are no extensive studies of
model prediction accuracy. We evaluated different models
using cross-validation in a test-retest data set and
data from the Human Connectome Project. In most of the
white matter and multiple b-values, we find that the
classic diffusion tensor model predicts the data more
accurately than test-retest reliability. However,
modeling the signal as a combination of contributions
from distinct white matter fascicles provides more
accurate model predictions.
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