Mapping prostatic microscopic anisotropy using linear and spherical b-tensor encoding: A preliminary study.

PURPOSE

Tensor-valued diffusion encoding provides more specific information than conventional diffusion-weighted imaging (DWI), but has mainly been applied in neuroimaging studies. This study aimed to assess its potential for the imaging of prostate cancer (PCa).

METHODS

Seventeen patients with histologically proven PCa were enrolled. DWI of the prostate was performed with linear and spherical tensor encoding using a maximal b-value of 1.5 ms/µm and a voxel size of 3 × 3 × 4 mm . The gamma-distribution model was used to estimate the mean diffusivity (MD), the isotropic kurtosis (MK ), and the anisotropic kurtosis (MK ). Regions of interest were placed in MR-defined cancerous tissues, as well as in apparently healthy tissues in the peripheral and transitional zones (PZs and TZs).

RESULTS

DWI with linear and spherical encoding yielded different image contrasts at high b-values, which enabled the estimation of MK and MK . Compared with healthy tissue (PZs and TZs combined) the cancers displayed a significantly lower MD (P < .05), higher MK (P < 10 ), and lower MK (P < .05). Compared with the TZ, tissue in the PZ showed lower MD (P < 10 ) and higher MK (P < 10 ). No significant differences were found between cancers of different Gleason scores, possibly because of the limited sample size.

CONCLUSION

Tensor-valued diffusion encoding enabled mapping of MK and MK in the prostate. The elevated MK in PCa compared with normal tissues suggests an elevated heterogeneity in the cancers. Increased in-plane resolution could improve tumor delineation in future studies.