Multimodal Breast Parenchymal Patterns Correlation Using a Patient-Specific Biomechanical Model

In this paper, we aim to produce a realistic 2-D projection of the breast parenchymal distribution from a 3-D breast magnetic resonance image (MRI). To evaluate the accuracy of our simulation, we compare our results with the local breast density (i.e., density map) obtained from the complementary full-field digital mammogram. To achieve this goal, we have developed a fully automatic framework, which registers MRI volumes to X-ray mammograms using a subject-specific biomechanical model of the breast. The optimization step modifies the position, orientation, and elastic parameters of the breast model to perform the alignment between the images. When the model reaches an optimal solution, the MRI glandular tissue is projected and compared with the one obtained from the corresponding mammograms. To reduce the loss of information during the ray-casting, we introduce a new approach that avoids resampling the MRI volume. In the results, we focus our efforts on evaluating the agreement of the distributions of glandular tissue, the degree of structural similarity, and the correlation between the real and synthetic density maps. Our approach obtained a high-structural agreement regardless the glandularity of the breast, whilst the similarity of the glandular tissue distributions and correlation between both images increase in denser breasts. Furthermore, the synthetic images show continuity with respect to large structures in the density maps.

[1]  Jürgen Weese,et al.  A comparison of similarity measures for use in 2-D-3-D medical image registration , 1998, IEEE Transactions on Medical Imaging.

[2]  K. Bathe Finite Element Procedures , 1995 .

[3]  Scott Kirkpatrick,et al.  Optimization by Simmulated Annealing , 1983, Sci..

[4]  Martyn P. Nash,et al.  Modelling Mammographic Compression of the Breast , 2008, MICCAI.

[5]  Arnau Oliver,et al.  Local breast density assessment using reacquired mammographic images. , 2017, European journal of radiology.

[6]  C. D'Orsi Breast Imaging Reporting and Data System (BI-RADS) , 2018 .

[7]  M. Doblaré,et al.  A finite element model to accurately predict real deformations of the breast. , 2008, Medical engineering & physics.

[8]  Hilde Bosmans,et al.  Development and validation of a modelling framework for simulating 2D-mammography and breast tomosynthesis images , 2014, Physics in medicine and biology.

[9]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[10]  L. Liberman,et al.  Breast imaging reporting and data system (BI-RADS). , 2002, Radiologic clinics of North America.

[11]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[12]  Tom Vercauteren,et al.  Diffeomorphic demons: Efficient non-parametric image registration , 2009, NeuroImage.

[13]  M. Daly,et al.  Dietary intake and breast density in high-risk women: a cross-sectional study , 2007, Breast Cancer Research.

[14]  J. Wolfe Risk for breast cancer development determined by mammographic parenchymal pattern , 1976, Cancer.

[15]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[16]  Nico Karssemeijer,et al.  Automatic breast density segmentation: an integration of different approaches , 2011, Physics in medicine and biology.

[17]  R. Siddon Fast calculation of the exact radiological path for a three-dimensional CT array. , 1985, Medical physics.

[18]  S. Cowin,et al.  Biomechanics: Mechanical Properties of Living Tissues, 2nd ed. , 1994 .

[19]  William M. Wells,et al.  Multi-modal image registration by minimizing Kullback-Leibler distance between expected and observed joint class histograms , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[20]  Nico Karssemeijer,et al.  Volumetric breast density estimation from full-field digital mammograms , 2006, IEEE Trans. Medical Imaging.

[21]  Martyn P. Nash,et al.  Breast lesion co-localisation between X-ray and MR images using finite element modelling , 2013, Medical Image Anal..

[22]  J. P. Lewis Fast Normalized Cross-Correlation , 2010 .

[23]  K. Miller,et al.  Total Lagrangian explicit dynamics finite element algorithm for computing soft tissue deformation , 2006 .

[24]  N. Boyd,et al.  Mammographic density and breast cancer risk: current understanding and future prospects , 2011, Breast Cancer Research.

[25]  Hang Si,et al.  TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator , 2015, ACM Trans. Math. Softw..

[26]  L. Herrmann Laplacian-Isoparametric Grid Generation Scheme , 1976 .

[27]  Torsten Hopp,et al.  2D/3D Registration for Localization of Mammographically Depicted Lesions in Breast MRI , 2012, Digital Mammography / IWDM.

[28]  John A Shepherd,et al.  Measurement of breast density with dual X-ray absorptiometry: feasibility. , 2002, Radiology.

[29]  Nico Karssemeijer,et al.  MRI to X-ray mammography intensity-based registration with simultaneous optimisation of pose and biomechanical transformation parameters , 2014, Medical Image Anal..

[30]  Nico Karssemeijer,et al.  Robust Breast Composition Measurement - VolparaTM , 2010, Digital Mammography / IWDM.

[31]  W. Paul Segars,et al.  An analysis of the mechanical parameters used for finite element compression of a high-resolution 3D breast phantom. , 2011, Medical physics.

[32]  Eloy García,et al.  Comparison of Four Breast Tissue Segmentation Algorithms for Multi-modal MRI to X-ray Mammography Registration , 2016, Digital Mammography / IWDM.

[33]  Frank D Gilliland,et al.  Physical activity, body mass index, and mammographic density in postmenopausal breast cancer survivors. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[34]  Yianni Attikiouzel,et al.  Automatic pectoral muscle segmentation on mediolateral oblique view mammograms , 2004, IEEE Transactions on Medical Imaging.

[35]  Nico Karssemeijer,et al.  Volumetric Breast Density Estimation from Full-Field Digital Mammograms: A Validation Study , 2014, IEEE Transactions on Medical Imaging.

[36]  Michael Bachmann Nielsen,et al.  Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case–control study , 2016, BMC Cancer.

[37]  N. Boyd,et al.  The quantitative analysis of mammographic densities. , 1994, Physics in medicine and biology.

[38]  Ares Lagae,et al.  Compact, Fast and Robust Grids for Ray Tracing , 2008, Comput. Graph. Forum.

[39]  Nico Karssemeijer,et al.  Breast Segmentation and Density Estimation in Breast MRI: A Fully Automatic Framework , 2015, IEEE Journal of Biomedical and Health Informatics.

[40]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[41]  David R. Haynor,et al.  Nonrigid multimodality image registration , 2001, SPIE Medical Imaging.

[42]  Torsten Hopp,et al.  Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization , 2013, Medical Image Anal..

[43]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[44]  M. J. Rupérez,et al.  A complete software application for automatic registration of x-ray mammography and magnetic resonance images. , 2014, Medical physics.

[45]  Sébastien Ourselin,et al.  NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics , 2014, International Journal of Computer Assisted Radiology and Surgery.

[46]  W. Kaiser,et al.  Model-based registration of X-ray mammograms and MR images of the female breast , 2006, IEEE Transactions on Nuclear Science.

[47]  Robert Marti,et al.  A Novel Breast Tissue Density Classification Methodology , 2008, IEEE Transactions on Information Technology in Biomedicine.

[48]  Eloy García,et al.  Mapping 3D breast lesions from full-field digital mammograms using subject-specific finite element models , 2017, Medical Imaging.

[49]  Xavier Lladó,et al.  Breast Density Analysis Using an Automatic Density Segmentation Algorithm , 2015, Journal of Digital Imaging.

[50]  K. J. Bathe,et al.  Frontiers in Finite Element Procedures and Applications , 2014 .

[51]  M. Yaffe,et al.  Validation of a method for measuring the volumetric breast density from digital mammograms , 2010, Physics in medicine and biology.

[52]  Koenraad Van Leemput,et al.  Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.