Evaluating the Accuracy of Diffusion MRI Models in White Matter

• DOI: 10.1371/journal.pone.0123272
• Publication Date: 2015-04-16T18:03:29Z
• Authors: Ariel Rokem; Jason D. Yeatman; Franco Pestilli; Kendrick N. Kay; Aviv Mezer; Stefan van der Walt; Brian A. Wandell
• Keywords: Diffusion Magnetic Resonance Imaging; Science; FOS: Biological sciences; Q; R; Medicine; Humans; Quantitative Biology - Quantitative Methods; White Matter; Quantitative Methods (q-bio.QM); Research Article

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 some of the models that are commonly used in analyzing human white matter 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 linear 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, 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.