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(2) and a voxel size of 3 × 3 × 4 mm(3). The gamma-distribution model was used to estimate the mean diffusivity (MD), the isotropic kurtosis (MK(I)), and the anisotropic kurtosis (MK(A)). 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(A) and MK(I). Compared with healthy tissue (PZs and TZs combined) the cancers displayed a significantly lower MD (P < .05), higher MK(I) (P < 10(−5)), and lower MK(A) (P < .05). Compared with the TZ, tissue in the PZ showed lower MD (P < 10(−3)) and higher MK(A) (P < 10(−3)). 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(A) and MK(I) in the prostate. The elevated MK(I) in PCa compared with normal tissues suggests an elevated heterogeneity in the cancers. Increased in-plane resolution could improve tumor delineation in future studies.