Deformation (meteorology)

Materials science

Computer science

Mammography

Radiology

Thermodynamics

Imaging phantom

Physics

Finite element method

Voxel

Segmentation

Breast imaging

Internal medicine

Artificial intelligence

Composite material

Medicine

Nuclear medicine

Breast cancer

Breast MRI

Cancer

Magnetic resonance imaging

Physica Medica

Overcoming the drawbacks of X-ray mammography and magnetic resonance imaging (MRI) by fusing the information in order to assist clinicians in the task of early detection of breast cancer in women with dense breasts. A detailed 3D computer-generated breast phantom based on empirical data extracted from breast MRI was constructed for each patient. To achieve this goal, MRI data were classified into the different components of breast tissue (glandular, adipose, skin and eventually tumor) using a semi-automated segmentation algorithm based on voxel intensity. Then, a geometrical model of the breast was constructed through the isosurfaces of this segmented volume. In order to perform a study on breast deformation using the finite element method, the geometrical model was automatically meshed into tetrahedral elements and material properties were assigned to the different kinds of breast tissues. To represent the large deformation of breast during a mammography exam, a linear elastic model was chosen to describe the constitutive relations of breast tissues, and the compression was simulated using a stiff plate model. After compressing the phantom, mammograms were simulated based on the deformed configuration. During this step, a parametric optimization of the model was conducted (mesh refinement, mechanical properties and friction coefficient). Small variations of the model parameters strongly influence the deformation and modify significantly the resultant simulated images. During the optimization process, both a better conservation of details and a convergence toward a distribution of components were observed for finer meshes, whereas the friction coefficient affects mostly the skin deformation. The phantom developed in this study allows the modeling of large deformations through the use of the finite element method, and also allows the simulation of mammographic images containing highresolution details. Moreover, this phantom combines flexibility and realism, and can be used for multimodality imaging research but also for clinical performance optimization. Overcoming the drawbacks of X-ray mammography and magnetic resonance imaging (MRI) by fusing the information in order to assist clinicians in the task of early detection of breast cancer in women with dense breasts. A detailed 3D computer-generated breast phantom based on empirical data extracted from breast MRI was constructed for each patient. To achieve this goal, MRI data were classified into the different components of breast tissue (glandular, adipose, skin and eventually tumor) using a semi-automated segmentation algorithm based on voxel intensity. Then, a geometrical model of the breast was constructed through the isosurfaces of this segmented volume. In order to perform a study on breast deformation using the finite element method, the geometrical model was automatically meshed into tetrahedral elements and material properties were assigned to the different kinds of breast tissues. To represent the large deformation of breast during a mammography exam, a linear elastic model was chosen to describe the constitutive relations of breast tissues, and the compression was simulated using a stiff plate model. After compressing the phantom, mammograms were simulated based on the deformed configuration. During this step, a parametric optimization of the model was conducted (mesh refinement, mechanical properties and friction coefficient). Small variations of the model parameters strongly influence the deformation and modify significantly the resultant simulated images. During the optimization process, both a better conservation of details and a convergence toward a distribution of components were observed for finer meshes, whereas the friction coefficient affects mostly the skin deformation. The phantom developed in this study allows the modeling of large deformations through the use of the finite element method, and also allows the simulation of mammographic images containing highresolution details. Moreover, this phantom combines flexibility and realism, and can be used for multimodality imaging research but also for clinical performance optimization.

Registration of breast magnetic resonance imaging and X-ray mammography through a biomechanical model based on clinical data and a finite element method