A progressive processing method for breast cancer detection via UWB based on an MRI-derived model

Ultra-wideband (UWB) microwave imaging is a promising method for breast cancer detection based on the large contrast of electric parameters between the malignant tumor and its surrounded normal breast organisms. In the case of multiple tumors being present, the conventional imaging approaches may be ineffective to detect all the tumors clearly. In this paper, a progressive processing method is proposed for detecting more than one tumor. The method is divided into three stages: primary detection, refocusing and image optimization. To test the feasibility of the approach, a numerical breast model is developed based on the realistic magnetic resonance image (MRI). Two tumors are assumed embedded in different positions. Successful detection of a 3.6 mm-diameter tumor at a depth of 42 mm is achieved. The correct information of both tumors is shown in the reconstructed image, suggesting that the progressive processing method is promising for multi-tumor detection.

[1]  Nguyen Duc Thang,et al.  Confocal Microwave Imaging for Breast Cancer Detection: Delay-Multiply-and-Sum Image Reconstruction Algorithm , 2008, IEEE Transactions on Biomedical Engineering.

[2]  Xia Xiao,et al.  Early Breast Cancer Detection by Ultrawide Band Imaging with Dispersion Consideration , 2008 .

[3]  R. Dasari,et al.  Identifying microcalcifications in benign and malignant breast lesions by probing differences in their chemical composition using Raman spectroscopy. , 2002, Cancer research.

[4]  Veronica Santalla del Rio,et al.  3-D-Microwave Breast Tumor Detection: Study of System Performance , 2008, IEEE Transactions on Biomedical Engineering.

[5]  Li Xu,et al.  A double constrained robust capon beamforming based imaging method for early breast cancer detection , 2013 .

[6]  Xia Xiao,et al.  Influence of the organism interface on the breast cancer detection by UWB , 2008 .

[7]  Jian Li,et al.  Multistatic Adaptive Microwave Imaging for Early Breast Cancer Detection , 2006, IEEE Transactions on Biomedical Engineering.

[8]  Paul M. Meaney,et al.  Enhancing breast tumor detection with near-field imaging , 2002 .

[9]  Tumor size and survival in multicentric and multifocal breast cancer. , 2011, Breast.

[10]  K. T. Mathew,et al.  Characterization of benign and malignant breast tissues using 2‐D microwave tomographic imaging , 2007 .

[11]  Stephen D. Gedney,et al.  Convolution PML (CPML): An efficient FDTD implementation of the CFS–PML for arbitrary media , 2000 .

[12]  W. Joines,et al.  The measured electrical properties of normal and malignant human tissues from 50 to 900 MHz. , 1994, Medical physics.

[13]  S.C. Hagness,et al.  A confocal microwave imaging algorithm for breast cancer detection , 2001, IEEE Microwave and Wireless Components Letters.

[14]  Michal Okoniewski,et al.  TEM horn antenna for near‐field microwave imaging , 2010 .

[15]  Nirmala Ramanujam,et al.  Autofluorescence and diffuse reflectance properties of malignant and benign breast tissues , 2004, Annals of Surgical Oncology.

[16]  T. Braun,et al.  Pathologic, Immunohistochemical, and Molecular Features of Benign and Malignant Phyllodes Tumors of the Breast , 2001, Modern Pathology.

[17]  Xia Xiao,et al.  A Compact 4 $\times$ 4 Planar UWB Antenna Array for 3-D Breast Cancer Detection , 2013, IEEE Antennas and Wireless Propagation Letters.

[18]  X. Li,et al.  Confocal microwave imaging for breast cancer detection: localization of tumors in three dimensions , 2002, IEEE Transactions on Biomedical Engineering.