Markus Nilsson^{1}, Filip Szczepankiewicz^{2}, Mikael Skorpil^{3,4}, Carl-Fredrik Westin^{5}, Lennart Blomqvist^{3,4,6,7}, and Fredrik Jäderling^{6,7}

Diffusion tensor imaging (DTI) has the potential to improve prostate cancer detection, since anisotropy is expected to correlate with tumor aggressiveness and differentiation. Differences in fractional anisotropy between cancer and normal tissue have been observed, although data is somewhat contradictory. A problem with DTI is its inability to distinguish low anisotropy from high orientation dispersion. In this study, we map the anisotropy independent of orientation in the prostate, by the use of a novel diffusion-encoding technique that permits encodings with variable b-tensor shapes. The microscopic anisotropy was found to be generally higher in cancer than in normal prostatic tissue.

Bourne RM, Bongers A, Chatterjee A, Sved P, Watson G (2016) Diffusion anisotropy in fresh and fixed prostate tissue ex vivo. Magnetic Resonance Medicine 76(2):626–634.

Kozlowski P, et al. (2006) Combined diffusion-weighted and dynamic contrast-enhanced MRI for prostate cancer diagnosis--correlation with biopsy and histopathology. J Magn Reson Imaging 24(1):108–113.

Li L, et al. (2015) Correlation of gleason scores with magnetic resonance diffusion tensor imaging in peripheral zone prostate cancer. J Magn Reson Imaging 42(2):460–467.

Manenti G, et al. (2007) Diffusion tensor magnetic resonance imaging of prostate cancer. Invest Radiol 42(6):412–419.

Lampinen B, et al. (2016) Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: A model comparison using spherical tensor encoding. NeuroImage, in revision (implementation available at https://github.com/markus-nilsson/md-dmri).

Lasic S, Szczepankiewicz F, Eriksson S, Nilsson M, Topgaard D (2014) Microanisotropy imaging: quantification of microscopic diffusion anisotropy and orientational order parameter by diffusion MRI with magic-angle spinning of the q-vector. Frontiers in Physics:1–35.

Sjölund J, et al. (2015) Constrained optimization of gradient waveforms for generalized diffusion encoding. J Magn Reson 261:157–168.

Stejskal EO, Tanner JE (1965) Spin diffusion measurements: Spin echoes in the presence of a time-dependent field gradient. J Chem Phys 42(1):288–292.

Szczepankiewicz F, et al. (2015) Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors. NeuroImage 104:241–252.

Szczepankiewicz F, et al. (2016) The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE). NeuroImage. doi:10.1016/j.neuroimage.2016.07.038.

Uribe CF, et al. (2015) In vivo 3T and ex vivo 7T diffusion tensor imaging of prostate cancer: Correlation with histology. Magn Reson Imaging 33(5):577–583.

Figure 1.
Transversal sections of the prostate showing columns with signal intensity at
the highest b-value, free water fraction, fraction of water with microscopic
anisotropy, and apparent diffusion coefficient of the tissue. Each row shows a
patient. The last row shows a slice through a region with a tumor (white arrow)
showing increased microscopic anisotropy and reduced ADC.

Figure 2.
ROI-based evaluations of the free water fraction (f_{FW}),
microscopic anisotropy fraction (f_{AT}), and tissue ADC (D_{T})
in the peripheral zone (PZ), the transition zone (TZ), and in the tumors.
Tumors exhibited elevated microscopic anisotropy.