Since DKI and transient anomalous diffusion imaging (tADI) are based on statistical models, they can be performed without the need of a-priori hypothesis on tissue micro-structures. However, the relation between tissue micro-structure DKI and tADI derived parameters have not been clearly established yet. In this work, we evaluated DKI, tAD and DTI diffusion parameters in normal and high-grade cancer prostate, by MR microimaging at 9.4T with a 70μmx70μm in plane resolution. As prostate tissue is a complex tissue, composed by several micro-compartments that exhibit different diffusion behaviors, it is an ideal tissue to investigate the biophysical features of diffusion parameters.
Tissue samples were obtained from radical prostatectomy specimens and kept in 4% PBS formaldehyde at 4°C for conservation. An expert uropathologist selected one normal and one cancer sample from 3 patients with high-grade Pca (Gleason Score ≥ 4+4). Acquisition was performed on a Bruker AV400 spectrometer operating at 9.4 T with a micro-imaging probe and maximum gradient strength of 1200mT/m. XWINNMR® and ParaVision® 3.0 software were employed for data acquisition. DWIs were acquired with a Pulsed Gradient Stimulated Echo (TE/TR=14,8/4500 (ms); resolution=70x70x1000μm3; δ/Δ=3/40 (ms); NSA=8), by varing diffusion gradient strengths; 9 b-values from 0 to 5000 s/mm2 were applied along 6 non-collinear directions
DTI was performed by FSL 5.0 software, with the b-value range 0-1500 s/mm2; non-Gaussian parameters were calculated by a customized algorithm developed in Matlab R2012b. Mean Kurtosis (MK) and kurtosis-derived mean diffusivity (MDk) were calculated by fitting DWI signal for each diffusion encoding direction, with the following equation:
$$\frac{S}{S_0}=e^{-bMD_k + \frac{1}{6}b^2MD_k^2\cdot K}$$
in the b-value range 0-2000 s/mm2. Moreover, proxy Kurtosis Fractional Anisotropy (KFA) was calculated as reported in [6].
tAD was performed, as described in [7], by fitting signal in the b-values range 0-5000s/mm2 with the following equation:
$$\frac{S}{S_0}=\prod_1^3 e^{-A_ib^{\gamma_i} (V_{ix}G_x+V_{iy}G_y+V_{iz}G_z)^{\gamma_i}}$$
where Gx,Gy,Gz are the diffusion-encoding directions, Ai are the generalized diffusion constants, γi, the three values of the anomalous exponent projected along the 3 main axes of the DTI reference frame, individuated by the eigenvectors Vix,Viy,Viz. Mean γ (Mγ), i.e. the mean values of the pseudo-superdiffusion γ-parameter, was calculated as reported in [3].
Region of Interests (ROI) were manually drawn on DW-images in tumoral and normal tissue.
Diffusion derived micro-images highlight tissue architecture and reflect structural modifications occurring with tumor. Histopathological evidences showed that Pca with Gleason Score (GS) ≥ 4+4 consists in a solid mass of undifferentiated cells (Fig.3), indeed no glandular structure is recognizable on DWIs or diffusion maps (Fig. 2). MD and MDk are lower in PCa, as a result of malignant cells proliferation that obstructs the almost-free diffusion compartments (acini, ducts), leading to an increase of tissue heterogeneity (K increases) and a reduction of tissue susceptibility differences (Mγ decreases). As a consequence of increasing cell density, FA and KFA are lower in cancer tissue.
In conclusion, MR micro-imaging in healthy and cancer prostate tissue allows to investigate diffusion proprieties of micro-structures approaching the cellular scale. As a consequence, micro-imaging technique could be employed to elucidate the biophysical underpinning of non–Gaussian diffusion parameters and in particular of the tAD parameters.
[1] Jensen J.H. and Helpern J.A., MRI quantification of Non-Gaussian water diffusion by Kurtosis Analysis. NMR Biomed, 2010.
[2] Metzler R. and Klafter J., The random walk’s guide to anomalous diffusion: a fractional dynamics approach. Physics Reports, 2000.
[3] Palombo M. et al., Spatio-temporal anomalous diffusion in heterogeneous media by nuclear magnetic resonance. J Chem Phys, 2011.
[4] Capuani S. et al., Spatio-temporal anomalous diffusion imaging: results in controlled phantoms and in excised human meningiomas. Magn Reson Imaging, 2013.
[5] Bourne R.M. et al., Microscopic diffusivity compartmentation in formalin-fixed prostate tissue.Magnetic Resonance in Medicine, 2012.
[6] Hansen B. and Jespersen S.N., Kurtosis fractional anisotropy, its contrast and estimation by proxy. Sci. Rep., 2016.
[7] Caporale A. et al.,The γ-parameter of Anomalous Diffusion quantified in human brain by MRI depends on local magnetic susceptibility differences, NeuroImage, 2016.
[8] Xu J.Q., et al.,. Magnetic resonance diffusion characteristics of histologically defined prostate cancer in humans. Magn Reson Med, 2009.