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DTI Quality Control Assessment via Error Estimation from Monte Carlo Simulations

1Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
2Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.
3Scientific Computing Imaging Institution, School of Computing, University of Utah, Salt Lake City, UT, USA.
Publication Date:
Proc Soc Photo Opt Instrum Eng. 2013 Mar 13;8669:1667549.
PubMed ID:
Diffusion Tensor Imaging, Quality Control and Monte Carlo Simulation
Appears in Collections:
P30 HD003110/HD/NICHD NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
R01 HD055741/HD/NICHD NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Track-HD project/CHDI/High Q Foundation
Generated Citation:
Farzinfar M., Li Y., Verde A.R., Oguz I., Gerig G., Styner M. DTI Quality Control Assessment via Error Estimation from Monte Carlo Simulations. Proc Soc Photo Opt Instrum Eng. 2013 Mar 13;8669:1667549. PMID: 23833547. PMCID: PMC3702180.
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Diffusion Tensor Imaging (DTI) is currently the state of the art method for characterizing the microscopic tissue structure of white matter in normal or diseased brain in vivo. DTI is estimated from a series of Diffusion Weighted Imaging (DWI) volumes. DWIs suffer from a number of artifacts which mandate stringent Quality Control (QC) schemes to eliminate lower quality images for optimal tensor estimation. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we have devised a more sophisticated, Monte-Carlo (MC) simulation based method for the assessment of resulting tensor properties. This allows for a consistent, error-based threshold definition in order to reject/accept the DWI-QC data. Specifically, we propose the estimation of two error metrics related to directional distribution bias of Fractional Anisotropy (FA) and the Principal Direction (PD). The bias is modeled from the DWI-QC gradient information and a Rician noise model incorporating the loss of signal due to the DWI exclusions. Our simulations further show that the estimated bias can be substantially different with respect to magnitude and directional distribution depending on the degree of spatial clustering of the excluded DWIs. Thus, determination of diffusion properties with minimal error requires an evenly distributed sampling of the gradient directions before and after QC.

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