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.