DTI quality control assessment via error estimation from Monte Carlo simulations

Mahshid Farzinfar; Yin Li; Audrey R. Verde; Ipek Oguz; Guido Gerig; Martin A. Styner

doi:10.1117/12.2006925

13 March 2013 DTI quality control assessment via error estimation from Monte Carlo simulations

Mahshid Farzinfar, Yin Li, Audrey R. Verde, Ipek Oguz, Guido Gerig, Martin A. Styner

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86692C (2013) https://doi.org/10.1117/12.2006925
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Mahshid Farzinfar, Yin Li, Audrey R. Verde, Ipek Oguz, Guido Gerig, and Martin A. Styner "DTI quality control assessment via error estimation from Monte Carlo simulations", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86692C (13 March 2013); https://doi.org/10.1117/12.2006925

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