Alexander, DC

Bailey, CEM

Gazinska, P

Grussu, F

Hawkes, DJ

Hipwell, JH

Mertzanidou, T

Owen, J

Panagiotaki, E

Pinder, SE

Siow, B

UCL Discovery

Di usion anisotropy in breast cancer tissue corresponds to spatial patterns of collagenalignment from structure tensor analysis of histologyColleen Bailey , Francesco Grussu , Bernard Siow , Thomy Mertzanidou , John H Hipwell , Julie Owen , PatrycjaGazinska , Sarah E Pinder , Daniel C Alexander , David J Hawkes , and Eleftheria PanagiotakiCentre for Medical Image Computing, University College London, London, United Kingdom, Institute of Neurology, University College London, London,United Kingdom, Centre for Advianced Biomedical Imaging, University College London, London, United Kingdom, Imaging, Francis Crick Insitute, London,United Kingdom, Breast Research Pathology, King's College London and Guy's Hospital, London, United KingdomSynopsisDirectional and anisotropy measures from a di usion model composed of VERDICT compartments were compared with directional and anisotropymeasures from structure tensor analysis of registered histology images. A signi cant positive correlation was found between the direction of theZeppelin component of the di usion model (assumed to represent the extracellular space) and the predominant direction of the structure tensorfrom the stroma, where the primary feature is aligned collagen. The correlation of anisotropy measures was weak, which may be due to di cultiesin detecting alignment in regions with densely-packed collagen, which have nearly uniform intensity on H&E staining.PurposeDi usion tensor imaging results in breast have been con icting  and the source of potential anisotropy is unknown. Some  suggest a relationshipto the breast ducts, while preclinical and ex vivo studies suggest an association with the stroma  and collagen/hypoxia . We hypothesize that thedi usion orientations will re ect those derived from structure tensor (ST) analysis of histology and clarify the origin of di usion anisotropy inbreast. This study examined ex vivo breast tissue using a model of tissue microstructure based on VERDICT MRI  and quantitatively compareddi usion orientations with ST from histology.MethodsMR acquisition and  ttingSeven formalin- xed tissue samples containing invasive breast cancers were rehydrated with saline and scanned at 9.4 T (Varian Inc). Theprotocol  consisted of 42 di usion-weighted images (0.25 x 0.25 x 0.5 mm , 3 gradient directions + 1 unweighted), b =21 960 s/mm  andgradient separations from 10-80 ms. Two DTIs (42 directions + 6 unweighted), b=1000 and 1500 s/mm  were also acquired. A fast spin echo (fSE,0.125 x 0.125 x 0.5 mm ) was acquired for registration. Data were  tted voxelwise to a Zeppelin-Sphere model (cylindrically symmetric tensor +restricted isotropic di usion) .Histology and structure tensor analysisThree micron slices were cut every 100 µm and stained with H&E. Slides were digitised (Hamamatsu Nanozoomer) at 20x magni cation.ST analysis was conducted at 5x magni cation using the freely available Structure Tensor Toolbox . The ST describes the local image-textureorientation by convolving the image with a 2D Gaussian weighting function over a neighbourhood . The Gaussian full width at half maximum usedwas 15 µm, approximately the distance a water molecule is expected to di use over a 30 ms experiment. Analysis was restricted to the extracellularspace by a mask of the stroma obtained via k-means clustering.An anisotropy index was calculated using the eigenvalues λ ≥λ  of the structure tensor: . The eigenvector of λ  gives the dominantdirection.Image registration and correlationAdjacent slices were stacked at 100 µm intervals into a 3D volume using 2D pairwise rigid registration with a block-matching strategy  andcorrelation coe cient as similarity measure. The 3D stack was registered to the fSE volume using an intensity-based a ne registration from ITKwith normalized mutual information as similarity metric. The resulting transformation was applied to the di usion parametric maps and directionalvectors.ST results were downsampled to the MRI resolution, normalizing AI by stromal area to re ect extracellular anisotropy. The Zeppelin component ofthe di usion model was assumed to represent the extracellular space. The 3D MRI vectors were projected into the 2D histological plane (fromregistration) for comparison.Direction of the Zeppelin projection (φ) and fractional anisotropy (FA) were compared to ST direction and AI, averaging over a 5x5 window aroundeach pixel. Pearson correlation coe cients were calculated, weighting the direction regression by the FA from MRI to limit the in uence of nearlyisotropic pixels with uncertain directions.Results and DiscussionFigure 1 shows the registration.Figure 2 demonstrates the results of the structure tensor analysis.Registered sections of the ST and di usion directional analysis are shown in Figure 3 for one sample.Figure 4 depicts the correlations between MRI and histology for both the direction and anisotropy measures. There is a signi cant positivecorrelation between the dominant directions directions of MRI and ST. The correlation plots demonstrate quite large variance. This is likely due toerrors in the registration, which a ect both the pixel locations for comparison and the pixel value φ, calculated when the transformation is appliedto the di usion vectors.1 2 3,4 1 1 55 5 1 1 11 23 451–3 45 6910 3max223987,81 2  ÃM MM M211,1213The anisotropy correlation is weaker. This may be due to di culties capturing orientation of dense collagen (Figure 5). Tightly packed collagendemonstrates less intensity variation in the histology images, producing AI estimates that may be low even when collagen anisotropy is high (visibleas the spread in AI as FA increases in the bottom left plot of Figure 4). Additionally, AI measures the gradient in the stroma, where the dominantfeature is collagen orientation, while FA measures the hindrance of water, which may result from structures other than collagen.ConclusionStructure tensor analysis on histological images was compared to directional and anisotropy parameters from a VERDICT model in ex vivo breasttissue. The primary di usion direction of the anisotropic MRI compartment has a signi cant positive correlation with the primary direction from STin the stroma. This is evidence of di usion anisotropy even in the absence of ductal structures. If this is con rmed in more samples, high-resolutionVERDICT measurements may provide a method of detecting collagen realignment, a valuable biomarker for hypoxia  and tumour progression .AcknowledgementsThis work was supported by funding from MIMIC (EP/K020439/1), the VPH-PRISM EU project (FP7-ICT-2011-9, 601040), the CRUK-EPSRC CancerImaging Centre at KCL/UCL, Horizon2020-EU.3.1 CDS-QUAMRI (ref: 634541) and by EPSRC fellowship EP/N021967/1.References1. Eyal, E. et al. Parametric di usion tensor imaging of the breast. Invest. Radiol. 47, 284–91 (2012).2. Partridge, S. C. et al. Di usion tensor MRI: preliminary anisotropy measures and mapping of breast tumors. J. Magn. Reson. Imaging 31, 339–47(2010).3. Tsougos, I. et al. The contribution of di usion tensor imaging and magnetic resonance spectroscopy for the di erentiation of breast lesions at 3T.Acta Radiol. 55, 14–23 (2014).4. Furman-Haran, E., Grobgeld, D., Nissan, N., Shapiro-Feinberg, M. & Degani, H. Can di usion tensor anisotropy indices assist in breast cancerdetection? J. Magn. Reson. Imaging 1–9 (2016).5. Norddin, N. et al. Microscopic di usion properties of  xed breast tissue: Preliminary  ndings. Magn. Reson. Med. 74, 1733–1739 (2015).6. Kakkad, S. et al. Collagen  bers mediate MRI-detected water di usion and anisotropy in breast cancers. Neoplasia 18, 585–593 (2016).7. Bigun, J. & Granlund, G. H. Optimal Orientation Detection of Linear Symmetry. Proc. IEEE First Int. Conf. Comput. Vis. 54, 433–438 (1987).8. Grussu, F. et al. A framework for optimal whole-sample histological quanti cation of neurite orientation dispersion in the human spinal cord. J.Neurosci. Methods 273, 20–32 (2016).9. Panagiotaki, E. et al. Noninvasive quanti cation of solid tumor microstructure using VERDICT MRI. Cancer Res. 74, 1902–1912 (2014).10. Bailey, C. et al. Modelling vasculature and cellular restriction in breast tumours using di usion MRI. in Proc. Intl. Soc. Mag. Reson. Med. 23 1082(2015).11. Mertzanidou, T. et al. in Breast Imaging: 12th International Workshop, IWDM 2014, Gifu City, Japan. Proceedings. 674–68012. Ourselin, S., Roche, A., Subsol, G., Pennec, X. & Ayache, N. Reconstructing a 3D structure from serial histological sections. Image Vis. Comput. 19,25–31 (2001).13. Yoo, T. S. et al. Engineering and algorithm design for an image processing API: a technical report on ITK--the Insight Toolkit. Stud. HealthTechnol. Inform. 85, 586–92 (2002).14. Conklin, M. W. & Keely, P. J. Why the stroma matters in breast cancer: insights into breast cancer patient outcomes through the examination ofstromal biomarkers. Cell Adh. Migr. 6, 249–60 (2012).5 14FiguresFigure 1 (a) A single histological slice and (b, c) orthogonal views of the stacked slides. The jagged black/white edges (blue arrows) demonstrate theslide translation needed to achieve a stacked histological volume. Overlaid in green is the aligned MRI di usion-weighted image. This slice does notcorrespond exactly to the histological slicing plane, meaning that only a portion of each histological image corresponds with the MRI data. Figure 2 (a) H&E-stained image and (b) the Structure Tensor analysis for all voxels in the image. (c) Mask of the stroma obtained by k-meansclustering and (d) the structure tensor analysis when restricted to only stromal voxels. Colours indicate direction and brightness indicatesmagnitude of anisotropy, AI, as depicted in the legend at the top right. Figure 3 (a) A histological slice and (b) the (unregistered) colour FA map for the Zeppelin portion of the model. Registered analyses are shownunderneath: (c) directional map of the ST, downsampled to MRI resolution and normalized to the stromal fraction; (d) projection of the primarydi usion direction vector into the histological plane. Colours and brightness correspond to direction and anisotropy, as depicted in the legend,where c  = 1 for FA from MRI and 0.8 for AI from histology. MRI and histology slices are not co-planar and maps have been stitched together,indicated by the dashed lines. Figure 4 Correlation between MRI and histology for (top) directional information and (bottom) anisotropy. The correlation plot for the sampleshown in Figure 3 is on the left, with the best  t line in black. Whiter points have lower anisotropy and are weighted less in the regression for φ (theanisotropy  t is not weighted). The slopes and correlation coe cients for all samples are shown in the bar graphs to the right, grouped by gradeand type (G1/3=grade 1/3, NST=no special type, muc=mucinous). * indicates r signi cantly di erent from 0 at p<0.01 signi cance level. maxFigure 5 A region with less dense collagen on the left and more dense collagen on the right. The structure tensor shows a dark region (blue arrow)even in the aligned collagen because there is less intensity variation when aligned collagen is tightly packed. The MRI appears bright in this regionand has lower FA toward the left, where loose collagen would not hinder di usion as much. 

Diffusion anisotropy in breast cancer tissue corresponds to spatial patterns of collagen alignment from structure tensor analysis of histology

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Diffusion anisotropy in breast cancer tissue corresponds to spatial patterns of collagen alignment from structure tensor analysis of histology

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