Diffusion imaging quality control via entropy of principal direction distribution.

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TitleDiffusion imaging quality control via entropy of principal direction distribution.
Publication TypeJournal Article
Year of Publication2013
AuthorsFarzinfar, M, Oguz, I, Smith, RG, Verde, AR, Dietrich, C, Gupta, A, Escolar, ML, Piven, J, Pujol, S, Vachet, C, Gouttard, S, Gerig, G, Dager, S, Mckinstry, RC, Paterson, S, Evans, AC, Styner, MA
Corporate AuthorsIBIS Network
JournalNeuroimage
Volume82
Pagination1-12
Date Published2013 Nov 15
ISSN1095-9572
KeywordsArtifacts, Brain, Diffusion Magnetic Resonance Imaging, Entropy, Humans, Image Processing, Computer-Assisted, Quality Control
Abstract

Diffusion MR imaging has received increasing attention in the neuroimaging community, as it yields new insights into the microstructural organization of white matter that are not available with conventional MRI techniques. While the technology has enormous potential, diffusion MRI suffers from a unique and complex set of image quality problems, limiting the sensitivity of studies and reducing the accuracy of findings. Furthermore, the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts, reduced signal-to-noise ratio (SNR), and increased proneness to a wide variety of artifacts, including eddy-current and motion artifacts, "venetian blind" artifacts, as well as slice-wise and gradient-wise inconsistencies. Such artifacts mandate stringent Quality Control (QC) schemes in the processing of diffusion MRI data. Most existing QC procedures are conducted in the DWI domain and/or on a voxel level, but our own experiments show that these methods often do not fully detect and eliminate certain types of artifacts, often only visible when investigating groups of DWI's or a derived diffusion model, such as the most-employed diffusion tensor imaging (DTI). Here, we propose a novel regional QC measure in the DTI domain that employs the entropy of the regional distribution of the principal directions (PD). The PD entropy quantifies the scattering and spread of the principal diffusion directions and is invariant to the patient's position in the scanner. High entropy value indicates that the PDs are distributed relatively uniformly, while low entropy value indicates the presence of clusters in the PD distribution. The novel QC measure is intended to complement the existing set of QC procedures by detecting and correcting residual artifacts. Such residual artifacts cause directional bias in the measured PD and here called dominant direction artifacts. Experiments show that our automatic method can reliably detect and potentially correct such artifacts, especially the ones caused by the vibrations of the scanner table during the scan. The results further indicate the usefulness of this method for general quality assessment in DTI studies.

DOI10.1016/j.neuroimage.2013.05.022
Alternate JournalNeuroimage
PubMed ID23684874
PubMed Central IDPMC3798052
Grant ListMH070890 / MH / NIMH NIH HHS / United States
P01-DA022446 / DA / NIDA NIH HHS / United States
HD-055741 / HD / NICHD NIH HHS / United States
U54EB005149-01 / EB / NIBIB NIH HHS / United States
P01 DA022446 / DA / NIDA NIH HHS / United States
U54 EB005149 / EB / NIBIB NIH HHS / United States
P30 HD03110 / HD / NICHD 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
R01 NS061965 / NS / NINDS NIH HHS / United States
U01 MH070890 / MH / NIMH NIH HHS / United States
R01 MH091645 / MH / NIMH NIH HHS / United States
HD-003110 / HD / NICHD NIH HHS / United States
P50 MH 064065 / MH / NIMH NIH HHS / United States
R01 HD055741 / HD / NICHD NIH HHS / United States
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