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Front Neuroinform. 2014 Jan 30;8:4. doi: 10.3389/fninf.2014.00004. eCollection 2014.

DTIPrep: quality control of diffusion-weighted images.

Author information

1
Department of Electrical and Computer Engineering, University of Iowa Iowa City, IA, USA.
2
Department of Psychiatry, University of North Carolina at Chapel Hill Chapel Hill, NC, USA.
3
Department of Psychiatry, University of Iowa Iowa City, IA, USA.
4
School of Biomedical Engineering, Southern Medical University Guangzhou, China.
5
SCI Institute, University of Utah Salt Lake City, UT, USA.

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.

KEYWORDS:

diffusion MRI; diffusion tensor imaging; open-source; preprocessing; quality control; software

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