title: 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: OBJECTIVE
  To describe a quantitative method for determination of SNR that extracts the local noise level using a single diffusion data set.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Radiology, Children's Medical Center, Dallas, Texas 75235 and Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas 75390.
authors.forename: Zhiyue J
authors.initials: ZJ
authors.lastname: Wang
authors.affiliation: Clinical Science, Philips Healthcare, Cleveland, Ohio 44143.
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Chia
authors.affiliation: Department of Radiology, Children's Medical Center, Dallas, TX 75235 and Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390.
authors.forename: Shaheen
authors.initials: S
authors.lastname: Ahmed
authors.affiliation: Department of Radiology, Children's Medical Center, Dallas, TX 75235 and Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390.
authors.forename: Nancy K
authors.initials: NK
authors.lastname: Rollins
mesh_terms: D000293:Adolescent; D016477:Artifacts; D001921:Brain / Q000473:pathology; D002648:Child; D002675:Child, Preschool; D066264:Datasets as Topic; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D015337:Multicenter Studies as Topic; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000188:drug therapy / Q000473:pathology; D059629:Signal-To-Noise Ratio; D055815:Young Adult