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The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data., Competing Interests: The authors have declared that no competing interests exist.

Objective: The objective of this study was to establish normative diffusion tensor imaging (DTI) eigenvalues (λ1,λ2,λ3), apparent diffusion coefficient, and fractional anisotropy in asymptomatic foot muscles., Methods: Ten healthy adults (mean [SD], 25.9 [4.3] years) were examined using a 3-T magnetic resonance imaging scanner. Diffusion tensor imaging indices were evaluated in 5 muscles in the foot: quadratus plantae, abductor hallucis, flexor hallucis brevis, flexor digitorum brevis, and abductor digiti minimi. Signal-to-noise ratio was also measured for each muscle., Results: In the various foot muscles, λ1 ranged from 1.88 × 10 to 2.14 × 10 mm/s, λ2 ranged from 1.39 × 10 to 1.48 × 10 mm/s, and λ3 ranged from 0.91 × 10 to 1.27 × 10 mm/s; apparent diffusion coefficient ranged from 1.48 × 10 to 1.55 × 10 mm/s; and fractional anisotropy ranged from 0.21 to 0.40. Statistical differences were seen in some eigenvalues between muscle pairs. Mean signal-to-noise ranged from 47.5 to 69.1 in the various muscles examined., Conclusions: Assessment of anisotropy of water diffusion in foot muscles was feasible using DTI. The measured DTI metrics in the foot were similar to those in calf and thigh skeletal muscles.