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. 2017 Sep;22(9):1-6.
doi: 10.1117/1.JBO.22.9.096011.

Optical tomographic imaging for breast cancer detection

Wenxiang Cong  1 , Xavier Intes  1 , Ge Wang  1
Affiliations

Optical tomographic imaging for breast cancer detection

Wenxiang Cong et al. J Biomed Opt. 2017 Sep.

Abstract

Diffuse optical breast imaging utilizes near-infrared (NIR) light propagation through tissues to assess the optical properties of tissues for the identification of abnormal tissue. This optical imaging approach is sensitive, cost-effective, and does not involve any ionizing radiation. However, the image reconstruction of diffuse optical tomography (DOT) is a nonlinear inverse problem and suffers from severe illposedness due to data noise, NIR light scattering, and measurement incompleteness. An image reconstruction method is proposed for the detection of breast cancer. This method splits the image reconstruction problem into the localization of abnormal tissues and quantification of absorption variations. The localization of abnormal tissues is performed based on a well-posed optimization model, which can be solved via a differential evolution optimization method to achieve a stable reconstruction. The quantification of abnormal absorption is then determined in localized regions of relatively small extents, in which a potential tumor might be. Consequently, the number of unknown absorption variables can be greatly reduced to overcome the underdetermined nature of DOT. Numerical simulation experiments are performed to verify merits of the proposed method, and the results show that the image reconstruction method is stable and accurate for the identification of abnormal tissues, and robust against the measurement noise of data.

Keywords: breast imaging; compressive sensing; differential evolution; diffuse optical tomography; image reconstruction.

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Figures

Fig. 1
Fig. 1
3-D finite-element model of the breast phantom.
Fig. 2
Fig. 2
Reconstruction of abnormal tissues from the dataset with 30 dB noise. (a) The true absorption slice of the phantom at depth of 15 mm, (b) the corresponding reconstructed absorption slice using the proposed method, (c) the corresponding slice reconstructed using the CS-based regularization method, (d) the true variations of abnormalities at depth of 15 mm, (e) the corresponding variations reconstructed using the proposed method, and (f) the variations reconstructed using the CS-based regularization method.
Fig. 3
Fig. 3
Reconstruction of abnormal tissues from the dataset with 25 dB noise. (a) The true absorption slice of the phantom at depth of 15 mm, (b) the corresponding reconstructed absorption slice using the proposed method, (c) the corresponding slice reconstructed using the CS-based regularization method, (d) the true variations of abnormalities at depth of 15 mm, (e) the corresponding variations reconstructed using the proposed method, and (f) the variations reconstructed using the CS-based regularization method.
See this image and copyright information in PMC

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