Comparison of image sensitivity between conventional tensor-based and fast diffusion kurtosis imaging protocols in a rodent model of acute ischemic stroke

Academic Article


  • Diffusion kurtosis imaging (DKI) can offer a useful complementary tool to routine diffusion MRI for improved stratification of heterogeneous tissue damage in acute ischemic stroke. However, its relatively long imaging time has hampered its clinical application in the emergency setting. A recently proposed fast DKI approach substantially shortens the imaging time, which may help to overcome the scan time limitation. However, to date, the sensitivity of the fast DKI protocol for the imaging of acute stroke has not been fully described. In this study, we performed routine and fast DKI scans in a rodent model of acute stroke, and compared the sensitivity of diffusivity and kurtosis indices (i.e. axial, radial and mean) in depicting acute ischemic lesions. In addition, we analyzed the contrast-to-noise ratio (CNR) between the ipsilateral ischemic and contralateral normal regions using both conventional and fast DKI methods. We found that the mean kurtosis shows a relative change of 47.1±7.3% between the ischemic and contralateral normal regions, being the most sensitive parameter in revealing acute ischemic injury. The two DKI methods yielded highly correlated diffusivity and kurtosis measures and lesion volumes (R2≥0.90, p< 0.01). Importantly, the fast DKI method exhibited significantly higher CNR of mean kurtosis (1.6±0.2) compared with the routine tensor protocol (1.3±0.2, p< 0.05), with its CNR per unit time (CNR efficiency) approximately doubled when the scan time was taken into account. In conclusion, the fast DKI method provides excellent sensitivity and efficiency to image acute ischemic tissue damage, which is essential for image-guided and individualized stroke treatment.
  • Authors

    Published In

  • NMR in Biomedicine  Journal
  • Digital Object Identifier (doi)

    Author List

  • Wu Y; Kim J; Chan ST; Zhou IY; Guo Y; Igarashi T; Zheng H; Guo G; Sun PZ
  • Start Page

  • 625
  • End Page

  • 630
  • Volume

  • 29
  • Issue

  • 5