Registration of x-ray mammograms and three-dimensional speed of sound images of the female breast

Breast cancer is the most common type of cancer among women in Europe and North America. The established screening method to detect breast cancer is X-ray mammography, although X-ray frequently provides poor contrast for tumors located within glandular tissue. A new imaging approach is Ultrasound Tomography generating three-dimensional speed of sound images. This paper describes a method to evaluate the clinical applicability of three-dimensional speed of sound images by automatically registering the images with the corresponding X-ray mammograms. The challenge is that X-ray mammograms show two-dimensional projections of a deformed breast whereas speed of sound images render a three-dimensional undeformed breast in prone position. This conflict requires estimating the relation between deformed and undeformed breast and applying the deformation to the three-dimensional speed of sound image. The deformation is simulated based on a biomechanical model using the finite element method. After simulation of the compression, the contours of the X-ray mammogram and the projected speed of sound image overlap congruently. The quality of the matching process was evaluated by measuring the overlap of a lesion marked in both modalities. Using four test datasets, the evaluation of the registration resulted in an average tumor overlap of 97%. The developed registration provides a basis for systematic evaluation of the new modality of three-dimensional speed of sound images, e.g. allows a greater understanding of tumor depiction in these new images.

[1]  Derek L. G. Hill,et al.  Comparison of biomechanical breast models: a case study , 2002, SPIE Medical Imaging.

[2]  Ann-Katherine Carton,et al.  Simulation of tomosynthesis images based on an anthropomorphic software breast tissue phantom , 2008, SPIE Medical Imaging.

[3]  S. Diekmann,et al.  Mammographiescreening in Deutschland , 2007, Der Radiologe.

[4]  David Gavaghan,et al.  Predicting Tumour Location by Simulating Large Deformations of the Breast Using a 3D Finite Element Model and Nonlinear Elasticity , 2004, MICCAI.

[5]  Max A. Viergever,et al.  Normalized Mutual Information Based PET-MR Registration Using K-Means Clustering and Shading Correction , 2003, WBIR.

[6]  A. Richardson,et al.  The effectiveness of breast cancer screening by mammography in younger women. , 1993, The Online journal of current clinical trials.

[7]  Oleg S. Pianykh,et al.  Digital Imaging and Communications in Medicine (DICOM) , 2017, Radiopaedia.org.

[8]  Max A. Viergever,et al.  Normalized mutual information based registration using k-means clustering and shading correction , 2006, Medical Image Anal..

[9]  N. Magné,et al.  Phyllodes tumor of the breast. , 2008, International journal of radiation oncology, biology, physics.

[10]  Patrick M. Knupp,et al.  Fundamentals of Grid Generation , 2020 .

[11]  Josien P. W. Pluim,et al.  Image Registration , 2003, IEEE Trans. Medical Imaging.

[12]  Dimitris N. Metaxas,et al.  A deformable finite element model of the breast for predicting mechanical deformations under external perturbations. , 2001, Academic radiology.

[13]  R. Sivaramakrishna,et al.  Detection of breast cancer at a smaller size can reduce the likelihood of metastatic spread: a quantitative analysis. , 1997, Academic radiology.

[14]  Yang Xu,et al.  Breast Imaging with Ultrasound Tomography: Clinical Results at the Karmanos Cancer Institute , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

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

[16]  W. Kaiser,et al.  Robotic system for biopsy and therapy of breast lesions in a high-field whole-body magnetic resonance tomography unit. , 2000, Investigative radiology.

[17]  U. Bick.  Mammographie-Screening in Deutschland : Wie, wann und warum? , 2006 .

[18]  Martin J. Yaffe,et al.  Biomechanical 3-D finite element modeling of the human breast using MRI data , 2001, IEEE Transactions on Medical Imaging.

[19]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[20]  James F. Greenleaf,et al.  CLINICAL IMAGING WITH TRANSMISSIVE ULTRASONIC COMPUTERIZED TOMOGRAPHY , 1981 .

[21]  Ncbi.  National Center for Biotechnology Information , 2008 .

[22]  N. Duric,et al.  Detection of breast cancer with ultrasound tomography: first results with the Computed Ultrasound Risk Evaluation (CURE) prototype. , 2007, Medical physics.