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9 March 2010 A numerical study of the inverse problem of breast infrared thermography modeling
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Abstract
Infrared thermography has been shown to be a useful adjunctive tool for breast cancer detection. Previous thermography modeling techniques generally dealt with the "forward problem", i.e., to estimate the breast thermogram from known properties of breast tissues. The present study aims to deal with the so-called "inverse problem", namely to estimate the thermal properties of the breast tissues from the observed surface temperature distribution. By comparison, the inverse problem is a more direct way of interpreting a breast thermogram for specific physiological and/or pathological information. In tumor detection, for example, it is particularly important to estimate the tumor-induced thermal contrast, even though the corresponding non-tumor thermal background usually is unknown due to the difficulty of measuring the individual thermal properties. Inverse problem solving is technically challenging due to its ill-posed nature, which is evident primarily by its sensitivity to imaging noise. Taking advantage of our previously developed forward-problemsolving techniques with comprehensive thermal-elastic modeling, we examine here the feasibility of solving the inverse problem of the breast thermography. The approach is based on a presumed spatial constraint applied to three major thermal properties, i.e., thermal conductivity, blood perfusion, and metabolic heat generation, for each breast tissue type. Our results indicate that the proposed inverse-problem-solving scheme can be numerically stable under imaging noise of SNR ranging 32 ~ 40 dB, and that the proposed techniques can be effectively used to improve the estimation to the tumor-induced thermal contrast, especially for smaller and deeper tumors.
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Li Jiang, Wang Zhan, and Murray H. Loew "A numerical study of the inverse problem of breast infrared thermography modeling", Proc. SPIE 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging, 76260O (9 March 2010); https://doi.org/10.1117/12.844695
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