To explore breast microstructure in ex vivo tissue using a wide range of diffusion scan parameters. Microstructural models that included restriction and anisotropy were tested, as well as conventional ADC and DTI.

Seven breast tissue samples (~1 x 2 x 0.5 cm³) containing a portion of tumour were obtained from the tissue biobank with ethics approval. Samples were immersed in formalin within 30 minutes for a minimum of four months. Samples were rehydrated in phosphate-buffered saline for at least 2 weeks, then transferred to Fomblin Perfluorosolv PFS-1 (Solvay Solexis) for scanning.

Scans were performed on a 9.4 T (Varian Inc) using a 33 mm quadrature coil (RAPID Biomedical). Temperature was maintained at 18.5 ± 0.5°C. Diffusion images were acquired using a pulsed gradient spin echo (3.2 x 3.2 cm² FOV, 0.25 x 0.25 x 0.5 mm³ resolution). There were a total of 42 DWIs (TR=1 s, TEs and numbers of averages shown in Table 1) in three orthogonal directions + 1 b=0 image, and two DTIs (TR=1 s, TE=45 ms, δ=4.5 ms, Δ=30 ms, G=18.7 and 22.6 G/cm) in 42 directions + 6 b=0 images.

Table 2 shows the one- and two-compartment models tested^1,2. The intracellular compartment was assumed to be isotropic, but both restricted (Sphere) and free (Ball) diffusion compartments were tested, including several conventional models (ADC, Bi-exponential). The extracellular compartment was assumed to be hindered but not restricted, ie. Gaussian displacement distribution, but could be isotropic (Ball), a tensor (Tensor) or cylindrically symmetric tensor (Zeppelin). The volume fractions of all compartments sum to 1 and all models included the equilibrium signal and T2 as free fit parameters. The radius of the restricted compartment, R, was constrained between 0.1-20 µm.

Each voxel was fitted separately using a maximum likelihood procedure that accounts for local minima and Rician noise¹. The Akaike Information Criterion (AIC), $$$AIC=-2\ln(L)+2k$$$, was calculated, where L is the maximum likelihood obtained from the fit and k is the number of parameters. The parameters for a region of interest (ROI) in fibroglandular tissue (FGT) for each sample are shown as median ± interquartile range (IQR).

This is the first study examining diffusion models that incorporate intracellular restriction and anisotropy in breast tissue. Models with restriction better explained the data (Figure 1), consistent with clinical studies showing non-Gaussian diffusion at high b values^3,4. Parametric maps demonstrated observable differences in the intracellular volume fraction and radius parameters even when ADC was similar (eg. Figs 2; 3a, b arrows).

In four samples (#2-5), models with restriction and anisotropy (ZeppelinSphere, TensorSphere) best explained the data from most voxels; the remaining three samples, which had smaller amounts of FGT, had more voxels where the BallSphere model explained the data better. Maps of the anisotropy (Fig 3c) showed small regions with coherent direction, which may explain why results from coarser-resolution clinical studies of anisotropy are inconsistent (compare ^5–7).

The mechanisms contributing to anisotropy are uncertain: a preclinical breast cancer model noted lower fractional anisotropy (FA) in hypoxic vs normoxic regions⁸, but the difference was small, ~0.03; an ex vivo study suggested FA was higher in the fibrous stroma than in the remainder of the breast gland⁹, but did not report values because of low reliability. Both of these studies used a simple DTI model. A two-compartment model that includes an isotropic, restricted component to represent the intracellular space may yield a more accurate measure of the anisotropy in the extracellular space with increased sensitivity to the microstructure. Future work will compare MRI measures with histology.

ADC values were slightly lower than those typically observed in vivo, which may reflect changes in water content, protein cross-linking and temperature. However, the microstructural restrictions and orientation present in vivo should be preserved. Tissue degradation would likely remove barriers to diffusion, whereas single compartment models such as ADC and DTI were insufficient to characterize the diffusion signal.

Although the scan protocol presented here is impractical for clinical use, these results may explain why ADC is often sensitive to the presence of cancer, but has low specificity for grade or degree of invasion. This study provides useful information for improving the specificity of clinical breast protocols.

Models with both restriction and anisotropy best characterized the signal in breast tissue samples containing cancer. The findings provide insight on the origins of diffusion signal and may enable better design of models and scan protocols for more specific information in healthy and diseased breast.