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PLoS One. 2015 Apr 16;10(4):e0123272. doi: 10.1371/journal.pone.0123272. eCollection 2015.

Evaluating the accuracy of diffusion MRI models in white matter.

Author information

1
Department of Psychology, Stanford, Stanford, California, United States of America.
2
Department of Psychology, Stanford, Stanford, California, United States of America; Institute for Learning and Brain Sciences, University of Washington, Seattle, Washington, United States of America.
3
Department of Psychology, Stanford, Stanford, California, United States of America; Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University, Bloomington, Indiana, United States of America.
4
Department of Psychology, Stanford, Stanford, California, United States of America; Department of Psychology, Washington University in St. Louis, St. Louis, Missouri, United States of America.
5
Department of Psychology, Stanford, Stanford, California, United States of America; Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University, Givat Ram, Jerusalem, Israel.
6
Division of Applied Mathematics, Stellenbosch University, Stellenbosch, South Africa.

Abstract

Models of diffusion MRI within a voxel are useful for making inferences about the properties of the tissue and inferring fiber orientation distribution used by tractography algorithms. A useful model must fit the data accurately. However, evaluations of model-accuracy of commonly used models have not been published before. Here, we evaluate model-accuracy of the two main classes of diffusion MRI models. The diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian distribution. Sparse fascicle models (SFM) summarize the signal as a sum of signals originating from a collection of fascicles oriented in different directions. We use cross-validation to assess model-accuracy at different gradient amplitudes (b-values) throughout the white matter. Specifically, we fit each model to all the white matter voxels in one data set and then use the model to predict a second, independent data set. This is the first evaluation of model-accuracy of these models. In most of the white matter the DTM predicts the data more accurately than test-retest reliability; SFM model-accuracy is higher than test-retest reliability and also higher than the DTM model-accuracy, particularly for measurements with (a) a b-value above 1000 in locations containing fiber crossings, and (b) in the regions of the brain surrounding the optic radiations. The SFM also has better parameter-validity: it more accurately estimates the fiber orientation distribution function (fODF) in each voxel, which is useful for fiber tracking.

PMID:
25879933
PMCID:
PMC4400066
DOI:
10.1371/journal.pone.0123272
[Indexed for MEDLINE]
Free PMC Article

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