Advances in diffusion-weighted magnetic resonance imaging (DW-MRI) have led to many alternative diffusion sampling strategies and analysis methodologies. A common objective among methods is estimation of white matter fiber orientations within each voxel, as doing so permits in-vivo fiber-tracking and the ability to study brain connectivity and networks. Knowledge of how DW-MRI sampling schemes affect fiber estimation accuracy, and consequently tractography and the ability to recover complex white-matter pathways, as well as differences between results due to choice of analysis method and which method(s) perform optimally for specific data sets, all remain important problems, especially as tractography-based studies become common. In this work we begin to address these concerns by developing sets of simulated diffusion-weighted brain images which we then use to quantitatively evaluate the performance of six DW-MRI analysis methods in terms of estimated fiber orientation accuracy, false-positive (spurious) and false-negative (missing) fiber rates, and fiber-tracking. The analysis methods studied are: 1) a two-compartment “ball and stick” model (BSM) (Behrens et al., 2003); 2) a non-negativity constrained spherical deconvolution (CSD) approach (Tournier et al., 2007); 3) analytical q-ball imaging (QBI) (Descoteaux et al., 2007); 4) q-ball imaging with Funk-Radon and Cosine Transform (FRACT) (Haldar and Leahy, 2013); 5) q-ball imaging within constant solid angle (CSA) (Aganj et al., 2010); and 6) a generalized Fourier transform approach known as generalized q-sampling imaging (GQI) (Yeh et al., 2010). We investigate these methods using 20, 30, 40, 60, 90 and 120 evenly distributed q-space samples of a single shell, and focus on a signal-to-noise ratio (SNR = 18) and diffusion-weighting (b = 1000 s/mm2) common to clinical studies. We found the BSM and CSD methods consistently yielded the least fiber orientation error and simultaneously greatest detection rate of fibers. Fiber detection rate was found to be the most distinguishing characteristic between the methods, and a significant factor for complete recovery of tractography through complex white-matter pathways. For example, while all methods recovered similar tractography of prominent white matter pathways of limited fiber crossing, CSD (which had the highest fiber detection rate, especially for voxels containing three fibers) recovered the greatest number of fibers and largest fraction of correct tractography for a complex three-fiber crossing region. The synthetic data sets, ground-truth, and tools for quantitative evaluation are publically available on the NITRC website as the project “Simulated DW-MRI Brain Data Sets for Quantitative Evaluation of Estimated Fiber Orientations” at http://www.nitrc.org/projects/sim_dwi_brain

Objective Traumatic brain injury in contact sports has significant impact on short term neurological and neurosurgical function as well as longer term cognitive disability. In this study, we aim to demonstrate that contact sport participants exhibit differences in Diffusion Tensor Imaging (DTI) caused by repeated physical impact on the brain. We also aim to determine that impact incurred by the contact sports athletes during the season may result in differences between the pre- and post-season DTI scans. Methods DTI data was collected from 10 contact (mean age 20.4 +- 1.36) and 13 age-matched non-contact (mean age 19.5 +- 1.03) sport male athletes, on a 3T MRI scanner. A single shot echo-planar imaging sequence with b-value of 1000s/mm2 and 25 gradient directions was used. Eight of the athletes were again scanned post-season. The b0 non-diffusion weighted image was averaged 5 times. Voxel-wise 2-sample t-tests were run for all group comparisons, and in each case, the positive false discovery rate (pFDR) was computed to assess the whole map, multiple comparison corrected significance. Results There were significant differences in the FA values in the inferior fronto-occipital fasciculus, parts of the superior and posterior coronal radiate and the splenium of the corpus callosum (CC) as well as smaller clusters in the genu and parts of the body of the CC. In addition, the external capsule also shows some difference between the contact and non-contact athlete brains. Additionally, the pre-season and postseason show differences in these regions, however, the post-season p-values show significance in more areas of the CC. Conclusions There are significant DTI changes in the corpus callosum, the external capsule, the inferior fronto-occipital fasciculus as well as regions such as the superior/posterior corona radiata when comparing the pre-season contact versus the non-contact controls and also comparing the post-season contact athletes with the controls. There are also difference in the DTI between the post- vs. pre-season scans.

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