DTIPrep: quality control of diffusion-weighted images.

TitleDTIPrep: quality control of diffusion-weighted images.
Publication TypeJournal Article
Year of Publication2014
AuthorsOguz, Ipek, Mahshid Farzinfar, Joy Matsui, Francois Budin, Zhexing Liu, Guido Gerig, Hans J. Johnson, and Martin Styner
JournalFront Neuroinform
Volume8
Pagination4
Date Published2014
ISSN1662-5196
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.

DOI10.3389/fninf.2014.00004
Alternate JournalFront Neuroinform
Original PublicationDTIPrep: quality control of diffusion-weighted images.
PubMed ID24523693
PubMed Central IDPMC3906573
Grant ListR01 MH086633 / MH / NIMH NIH HHS / United States
P50 MH064065 / MH / NIMH NIH HHS / United States
P30 HD003110 / HD / NICHD NIH HHS / United States
R01 MH070890 / MH / NIMH NIH HHS / United States
U01 MH070890 / MH / NIMH NIH HHS / United States
R01 MH091645 / MH / NIMH NIH HHS / United States
P50 MH078105 / MH / NIMH NIH HHS / United States
R21 AG033387 / AG / NIA NIH HHS / United States
R01 HD055741 / HD / NICHD NIH HHS / United States
P01 DA022446 / DA / NIDA NIH HHS / United States
U54 EB005149 / EB / NIBIB NIH HHS / United States
T32 NS007431 / NS / NINDS NIH HHS / United States
R01 NS061965 / NS / NINDS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 AI067518 / AI / NIAID NIH HHS / United States