Microstructure models for diffusion MRI in breast cancer and surrounding stroma: an ex vivo study

Colleen Bailey^{1}, Bernard Siow^{2}, Eleftheria Panagiotaki^{1}, John H Hipwell^{1}, Sarah E Pinder^{3}, Daniel C Alexander^{1}, and David J Hawkes^{1}

Seven breast tissue samples (~1 x 2 x 0.5 cm^{3})
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 cm^{2} FOV, 0.25
x 0.25 x 0.5 mm^{3} 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.

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 regions^{8}, 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 gland^{9}, 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.

1. Panagiotaki, E. et al. Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison. Neuroimage 59, 2241–54 (2012).

2. Panagiotaki, E. et al. Noninvasive quantification of solid tumor microstructure using VERDICT MRI. Cancer Res. 74, 1902–1912 (2014).

3. Iima, M. et al. Quantitative Non-Gaussian Diffusion and Intravoxel Incoherent Motion Magnetic Resonance Imaging: Differentiation of Malignant and Benign Breast Lesions. Invest. Radiol. 50, 205–211 (2015).

4. Wu, D. et al. Characterization of Breast Tumors Using Diffusion Kurtosis Imaging (DKI). PLoS One 9, e113240 (2014).

5. Eyal, E. et al. Parametric diffusion tensor imaging of the breast. Invest. Radiol. 47, 284–91 (2012).

6. Partridge, S. C. et al. Diffusion tensor MRI: preliminary anisotropy measures and mapping of breast tumors. J. Magn. Reson. Imaging 31, 339–47 (2010).

7. Tsougos, I. et al. The contribution of diffusion tensor imaging and magnetic resonance spectroscopy for the differentiation of breast lesions at 3T. Acta Radiol. 55, 14–23 (2014).

8. Kakkad, S. M. et al. In vivo and ex vivo diffusion tensor imaging parameters follow Collagen 1 fiber distribution in breast cancer xenograft mode. in Proc. Intl. Soc. Mag. Reson. Med. 22 222 (2015).

9. Norddin, N. et al. Microscopic diffusion properties of fixed breast tissue: Preliminary findings. Magn. Reson. Med. Early View (2015).

Table 1 The number of
averages acquired for each set of scan parameters for 42 DWIs (3 orthogonal
directions + b=0). δ is the gradient duration, Δ the time between gradients and
G the gradient strength.

Table 2 Models
tested (fit parameters in parentheses). Ball = isotropic, unrestricted
diffusion. Tensor = unrestricted diffusion in three orthogonal directions.
Zeppelin = cylindrically symmetric tensor. Sphere = isotropic diffusion
restricted by an impermeable membrane at radius R. Total number fit parameters
summarized in the last column.

Figure 1. Fits for a representative sample a) Tensor was
unable to account for signal at all gradient strengths (arrows) b) ZeppelinSphere
c) ΔAIC, the difference between AIC for a given model and minimum AIC
from all models. d) Map of the best model (lowest AIC) for each voxel.

Figure 2. Values for selected parameters across all 7
samples (median +/- IQR) for (a) ADC and (b) ZeppelinSphere models. Comparing
eg. samples #3, 5 and 6, fits that produced similar ADC demonstrated differences
in parameters such as intracellular volume fraction, f_{I}, and cell
radius, R.

Figure 3 Parameter maps demonstrating heterogeneity (a and
b). Regions of similar ADC (arrows) showed differences in the intracellular
volume fraction, f_{I}, and cell radius, R. c) A vector map of the
primary eigenvector, λ_{1}, from the ZeppelinSphere fit with FA
coloured by direction showed regions of coherent directionality.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

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