Quantitative imaging with electrical impedance spectroscopy.
Electrical impedance spectroscopy (EIS) is a noninvasive modality that can be used to determine the electrical admittivity inside a body given a discrete set of current/voltage measurements made on the surface. Of particular interest is the use of EIS in the diagnosis of breast cancer, as the admittivity spectra of malignant and benign tumors differ significantly. Due to the fact that x-ray mammography is the current standard method of breast imaging to detect tumors, it is natural to see if we can use the admittivity distribution along with the mammogram image to improve the diagnosis, with the hopes that the specificity of these two methods combined will be greatly improved from using the mammogram image on its own. EIS is a highly ill-posed inverse problem, but regularization, in the form of structural prior information from the mammogram image as well as modeling error, allows for the problem to be solved for in a computationally efficient manner with improved results. To interpret the solution from the EIS inverse problem, a classification scheme is added, providing a quantitative image which maps out the tissue classification of the inside of the breast. The computational methods for solving the EIS inverse problem and the classification scheme are discussed and computed examples are presented to demonstrate the high simulated sensitivity and specificity of the method.
Department of Mathematics, Case Western Reserve University, Cleveland, OH 44106, USA. ejs49@case.edu
Debra
D
McGivney
Daniela
D
Calvetti
Erkki
E
Somersalo
D000465:Algorithms; D058266:Dielectric Spectroscopy / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008327:Mammography / Q000379:methods*; D019047:Phantoms, Imaging