摘要
In this paper, a collection of three-dimensional(3D)numerical breast models are developed based on clinical magnetic resonance images(MRIs). A hybrid contour detection method is used to create the contour, and the internal space is filled with different breast tissues, with each corresponding to a specified interval of MRI pixel intensity. The developed models anatomically describe the complex tissue structure and dielectric properties in breasts. Besides, they are compatible with finite-difference-time-domain(FDTD)grid cells. Convolutional perfect matched layer(CPML)is applied in conjunction with FDTD to simulate the open boundary outside the model. In the test phase, microwave breast cancer detection simulations are performed in four models with varying radiographic densities. Then, confocal algorithm is utilized to reconstruct the tumor images. Imaging results show that the tumor voxels can be recognized in every case, with 2 mm location error in two low density cases and 7 mm─8 mm location errors in two high density cases, demonstrating that the MRI-derived models can characterize the individual difference between patients' breasts.
In this paper, a collection of three-dimensional（3D）numerical breast models are developed based onclinical magnetic resonance images（MRIs）. A hybrid contour detection method is used to create the contour, andthe internal space is filled with different breast tissues, with each corresponding to a specified interval of MRI pixelintensity. The developed models anatomically describe the complex tissue structure and dielectric properties inbreasts. Besides, they are compatible with finite-difference-time-domain（FDTD）grid cells. Convolutional perfectmatched layer（CPML）is applied in conjunction with FDTD to simulate the open boundary outside the model. Inthe test phase, microwave breast cancer detection simulations are performed in four models with varying radiographicdensities. Then, confocal algorithm is utilized to reconstruct the tumor images. Imaging results show thatthe tumor voxels can be recognized in every case, with 2 mm location error in two low density cases and 7 mm─8mm location errors in two high density cases, demonstrating that the MRI-derived models can characterize the individualdifference between patients’ breasts.
基金
Supported by the National Natural Science Foundation of China(No.61271323)
关键词
天津大学学报
英文版
3D breast model
contour detection
finite-difference-time-domain（FDTD）
convolutional perfectmatched layer（CPML）
microwave imaging
