The abnormal thermogram has been shown to be a reliable indicator of a high risk of breast cancer. Nevertheless, a
major weakness of current infrared breast thermography is its poor sensitivity for deeper tumors. Numerical modeling
for breast thermography provides an effective tool to investigate the complex relationships between the breast thermal
behaviors and the underlying patho-physiological conditions. We have developed a set of new modeling techniques to
take into account some subtle factors usually ignored in previous studies, such as gravity-induced elastic deformations of
the breast, nonlinear elasticity of soft tissues, and dynamic behavior of thermograms. Conventional "forward problem"
modeling cannot be used directly to improve tumor detectability, however, because the underlying tissue thermal
properties are generally unknown. Therefore, we propose an "inverse problem" modeling technique that aims to estimate
the tissue thermal properties from the breast surface thermogram. Our data suggest that the estimation of the tumor-induced
thermal contrast can be improved significantly by using the proposed inverse problem solving techniques to
provide the individual-specific thermal background, especially for deeper tumors. We expect the proposed new methods,
taken together, to provide a stronger foundation for, and greater specificity and precision in, thermographic diagnosis,
and treatment, of breast cancer.