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Mapping prostatic microscopic anisotropy using linear and spherical b-tensor encoding: A preliminary study
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DOI：10.1002/mrm.28856.png)
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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/mu m(2) and a voxel size of 3 x 3 x 4 mm(3). The gamma-distribution model was used to estimate the mean diffusivity (MD), the isotropic kurtosis (MKI), and the anisotropic kurtosis (MKA). 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 MKA and MKI. Compared with healthy tissue (PZs and TZs combined) the cancers displayed a significantly lower MD (P <.05), higher MKI (P < 10(-5)), and lower MKA (P <.05). Compared with the TZ, tissue in the PZ showed lower MD (P < 10(-3)) and higher MKA (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 MKA and MKI in the prostate. The elevated MKI in PCa compared with normal tissues suggests an elevated heterogeneity in the cancers. Increased in-plane resolution could improve tumor delineation in future studies.
Keyword:
diffusion-weighted imaging
heterogeneity
microscopic anisotropy
prostate cancer
tensor-valued diffusion encoding
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1.2W
被引数:
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