Improved image quality and detection of small cerebral infarctions with diffusion-tensor trace imaging

AJR Am J Roentgenol. 2013 Jun;200(6):1327-33. doi: 10.2214/AJR.12.9816.

Abstract

Objective: The purpose of this study was to test a hypothesis that routinely performed diffusion-tensor trace imaging is of sufficient image quality and sensitivity for infarct detection to safely and routinely replace standard diffusion-weighted imaging (DWI) in the clinical setting.

Materials and methods: Both routine DWI and 15-direction diffusion-tensor imaging (DTI) with parallel acquisition technique were obtained on all brain MRI studies from a single 1.5-T MRI scanner at a tertiary care referral center over a 1-year period, permitting direct comparison of the two different diffusion studies on the same patients (2537 studies, 365 infarct-positive studies). A subset of images was assessed for image quality and quantitatively for ability to detect brain infarctions. The total set of positive studies was reviewed qualitatively for ability to detect small cerebral infarctions.

Results: Fifteen-direction isotropic DWI (DTI trace images) with parallel acquisition technique resulted in consistently higher image quality with less distortion and higher image detail than routine DWI. Small infarcts were better seen, and in 12 cases, infarcts could only be seen on 15-direction isotropic diffusion-weighted images. The additional scanning time required for 15-direction isotropic DWI did not result in significantly increased motion-related reduction in image quality compared with standard DWI.

Conclusion: Diffusion-tensor trace images obtained with parallel acquisition technique are of improved image quality and improved sensitivity for detection of small cerebral infarctions relative to standard DWI. If such DTI data are acquired, routine DWI can be omitted.

MeSH terms

  • Aged
  • Analysis of Variance
  • Artifacts
  • Cerebral Infarction / diagnosis*
  • Diagnosis, Differential
  • Diffusion Tensor Imaging / methods*
  • Female
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods
  • Male
  • Middle Aged
  • Retrospective Studies
  • Sensitivity and Specificity