Signal-to-noise assessment for diffusion tensor imaging with single data set and validation using a difference image method with data from a multicenter study.

Abstract

To describe a quantitative method for determination of SNR that extracts the local noise level using a single diffusion data set. Brain data sets came from a multicenter study (eight sites; three MR vendors). Data acquisition protocol required b=0, 700 s/mm2, fov=256×256 mm2, acquisition matrix size 128×128, reconstruction matrix size 256×256; 30 gradient encoding directions and voxel size 2×2×2 mm3. Regions-of-interest (ROI) were placed manually on the b=0 image volume on transverse slices, and signal was recorded as the mean value of the ROI. The noise level from the ROI was evaluated using Fourier Transform based Butterworth high-pass filtering. Patients were divided into two groups, one for filter parameter optimization (N=17) and one for validation (N=10). Six white matter areas (the genu and splenium of corpus callosum, right and left centrum semiovale, right and left anterior corona radiata) were analyzed. The Bland-Altman method was used to compare the resulting SNR with that from the difference image method. The filter parameters were optimized for each brain area, and a set of "global" parameters was also obtained, which represent an average of all regions. The Bland-Altman analysis on the validation group using "global" filter parameters revealed that the 95% limits of agreement of percent bias between the SNR obtained with the new and the reference methods were -15.5% (median of the lower limit, range [-24.1%, -8.9%]) and 14.5% (median of the higher limits, range [12.7%, 18.0%]) for the 6 brain areas. An FT-based high-pass filtering method can be used for local area SNR assessment using only one DTI data set. This method could be used to evaluate SNR for patient studies in a multicenter setting.