In the last decade, diffusion MRI (dMRI) studies of the human and animal brain have been used to investigate a multitude of pathologies and drug-related effects in neuroscience research. Study after study identifies white matter (WM) degeneration as a crucial biomarker for all these diseases. The tool of choice for studying WM is dMRI. However, dMRI has inherently low signal-to-noise ratio and its acquisition requires a relatively long scan time; in fact, the high loads required occasionally stress scanner hardware past the point of physical failure. As a result, many types of artifacts implicate the quality of diffusion imagery. Using these complex scans containing artifacts without quality control (QC) can result in considerable error and bias in the subsequent analysis, negatively affecting the results of research studies using them. However, dMRI QC remains an under-recognized issue in the dMRI community as there are no user-friendly tools commonly available to comprehensively address the issue of dMRI QC. As a result, current dMRI studies often perform a poor job at dMRI QC. Thorough QC of dMRI will reduce measurement noise and improve reproducibility, and sensitivity in neuroimaging studies; this will allow researchers to more fully exploit the power of the dMRI technique and will ultimately advance neuroscience. Therefore, in this manuscript, we present our open-source software, DTIPrep, as a unified, user friendly platform for thorough QC of dMRI data. These include artifacts caused by eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, as well as slice-wise and gradient-wise intensity inconsistencies. This paper summarizes a basic set of features of DTIPrep described earlier and focuses on newly added capabilities related to directional artifacts and bias analysis.

A number of studies are now collecting diffusion tensor imaging (DTI) data across sites. While the reliability of anatomical images has been established by a number of groups, the reliability of DTI data has not been studied as extensively. In this study, five healthy controls were recruited and imaged at eight imaging centers. Repeated measures were obtained across two imaging protocols allowing intra-subject and inter-site variability to be assessed. Regional measures within white matter were obtained for standard rotationally invariant measures: fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity. Intra-subject coefficient of variation (CV) was typically < 1% for all scalars and regions. Inter-site CV increased to *1%-3%. Inter-vendor variation was similar to inter-site variability. This variability includes differences in the actual implementation of the sequence.

Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts.

Huntington’s disease (HD) is a devastating neurodegenerative disease with no effective disease-modifying treatments. There is considerable interest in finding reliable indicators of disease progression to judge the efficacy of novel treatments that slow or stop disease onset before debilitating signs appear. Diffusion-weighted imaging (DWI) may provide a reliable marker of disease progression by characterizing diffusivity changes in white matter (WM) in individuals with prodromal HD. The prefrontal cortex (PFC) may play a role in HD progression due to its prominent striatal connections and documented role in executive function. This study uses DWI to characterize diffusivity in specific regions of PFC WM defined by FreeSurfer in 53 prodromal HD participants and 34 controls. Prodromal HD individuals were separated into three CAG-Age Product (CAP) groups (16 low, 22 medium, 15 high) that indexed baseline progression. Statistically significant increases in mean diffusivity (MD) and radial diffusivity (RD) among CAP groups relative to controls were seen in inferior and lateral PFC regions. For MD and RD, differences among controls and HD participants tracked with baseline disease progression. The smallest difference was for the low group and the largest for the high group. Significant correlations between Trail Making Test B (TMTB) and mean fractional anisotropy (FA) and/or RD paralleled group differences in mean MD and/or RD in several right hemisphere regions. The gradient of effects that tracked with CAP group suggests DWI may provide markers of disease progression in future longitudinal studies as increasing diffusivity abnormalities in the lateral PFC of prodromal HD individuals.