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Proceedings Paper

DTI quality control assessment via error estimation from Monte Carlo simulations
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Paper Abstract

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.

Paper Details

Date Published: 13 March 2013
PDF: 8 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86692C (13 March 2013); doi: 10.1117/12.2006925
Show Author Affiliations
Mahshid Farzinfar, Univ. of North Carolina at Chapel Hill (United States)
Yin Li, Univ. of North Carolina at Chapel Hill (United States)
Audrey R. Verde, Univ. of North Carolina at Chapel Hill (United States)
Ipek Oguz, Univ. of North Carolina at Chapel Hill (United States)
Guido Gerig, The Univ. of Utah (United States)
Martin A. Styner, Univ. of North Carolina at Chapel Hill (United States)


Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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