Evaluation of impedance technique for detecting breast carcinoma using a 2-D numerical model of the torso.
Previous experimental studies showed that significant changes occur in the electrical properties of breast cancer tissue compared to the surrounding normal tissue. This phenomenon motivated studies on cancer detection using electrical impedance techniques. In the present study, a two-dimensional model of the torso and a numerical method were used to investigate the changes in the potential distribution as a result of a malignant tissue present in the breast. A transverse MRI image of the woman's torso was scanned. Noise reduction and contour-following algorithms were applied to differentiate between eight compartments in the torso. The extracted tissue types were lungs, blood, ribs, bone marrow of the cord, breast fat, skin, skeletal muscle, and heart muscle. Isotropic homogeneous conductivity was assigned to each one of these compartments. The volume conductor problem was solved numerically using the finite volume method to determine the potential distribution developed due to the dipole source. Cases without and with artificially inserted malignant region with realistic sizes were examined to investigate the sensitivity of impedance techniques to detect breast cancer. Significant changes were detected in the potential distribution inside the volume conductor as a result of the realistic size of breast tumors. A linear relation was found between the surface potential in the vicinity of the tumor region and the size of the tumor. For a small malignant area of 0.22 cm2, the surface potential near the tumor region decreased only slightly from a value of 13.81 mV in the normal case to 13.67 mV (0.14 mV change; 1.0%). For a larger malignant area of 5.43 cm2, the potential decrease was more pronounced, 11.29 mV (2.52 mV change; 18.3%), indicating that realistic sizes of breast tumor result in significant changes in the surface potential. Thus, impedance techniques employed in the present study show very good promise in detecting breast cancer.
Radai
M M
MM
Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Israel.
Abboud
S
S
Rosenfeld
M
M
D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D003952:Diagnostic Imaging / Q000379:methods*; D017097:Electric Impedance; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008954:Models, Biological