Abstract
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.
Highlights
IntroductionThousands of diffusion MRI (dMRI) datasets are collected every day across the world in studies of autism (Wolff et al, 2012), schizophrenia (Gilmore et al, 2010), Huntington’s disease (Dumas et al, 2012), Alzheimer’s disease (Rose et al, 2000), Parkinson’s disease, substance abuse (Parnell et al, 2009; Coleman et al, 2012) and many other conditions
Thousands of diffusion MRI datasets are collected every day across the world in studies of autism (Wolff et al, 2012), schizophrenia (Gilmore et al, 2010), Huntington’s disease (Dumas et al, 2012), Alzheimer’s disease (Rose et al, 2000), Parkinson’s disease, substance abuse (Parnell et al, 2009; Coleman et al, 2012) and many other conditions
Not using the hardware fix is a serious problem that can lead to artifacts in more than half of the scans at some scanning sites
Summary
Thousands of diffusion MRI (dMRI) datasets are collected every day across the world in studies of autism (Wolff et al, 2012), schizophrenia (Gilmore et al, 2010), Huntington’s disease (Dumas et al, 2012), Alzheimer’s disease (Rose et al, 2000), Parkinson’s disease, substance abuse (Parnell et al, 2009; Coleman et al, 2012) and many other conditions. Extensive research efforts have utilized dMRI in both normal subjects and patients in an attempt to yield new insights into the microstructural organization of WM that are not available with conventional MRI (Rose et al, 2000; Ciccarelli et al, 2001; McKinstry et al, 2002). Tractography techniques (Mori and Zijl, 2002), which estimate paths of brain WM fiber bundles based on dMRI data, can identify abnormalities in fiber shape or microstructure along the fiber bundles (Escolar et al, 2009). Findings have the potential to directly translate from basic to clinical science
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