Evaluation of measurement uncertainties in human diffusion tensor imaging (DTI)-derived parameters and optimization of clinical DTI protocols with a wild bootstrap analysis.

PURPOSE: To quantify measurement uncertainties of fractional anisotropy, mean diffusivity, and principal eigenvector orientations in human diffusion tensor imaging (DTI) data acquired with common clinical protocols using a wild bootstrap analysis, and to establish optimal scan protocols for clinical DTI acquisitions.
MATERIALS AND METHODS: A group of 13 healthy volunteers were scanned using three commonly used DTI protocols with similar total scan times. Two important parameters-the number of unique diffusion gradient directions (NUDG) and the ratio of the total number of diffusion-weighted (DW) images to the total number of non-DW images (DTIR)-were analyzed in order to investigate their combined effects on uncertainties of DTI-derived parameters, using results from both the Monte Carlo simulation and the wild bootstrap analysis of uncertainties in human DTI data.
RESULTS: The wild bootstrap analysis showed that uncertainties in human DTI data are significantly affected by both NUDG and DTIR in many brain regions. These results agree with previous predictions based on error-propagations as well as results from simulations.
CONCLUSION: Our results demonstrate that within a clinically feasible DTI scan time of about 10 minutes, a protocol with number of diffusion gradient directions close to 30 provides nearly optimal measurement results when combined with a ratio of the total number of DW images over non-DW images equal to six. Wild bootstrap can serve as a useful tool to quantify the measurement uncertainty from human DTI data.