Nominal Stiffness Identification for Tumor Detection of Women Breast in a Digital Image Elasto Tomography (DIET) System

Abstract

This research develops a hysteresis loop analysis (HLA) based method for breast cancer diagnosis in a Digital Imaging Elasto-tomography (DIET) system. Dynamic displacements induced by mechanical actuation for 4 silicone breast phantoms (1 homogeneous healthy, 3 with 10-20mm stiffer inclusion “tumors”) are captured using the DIET system. Hysteresis loops for each measured reference point across the breast surface are reconstructed using the measured displacement and a calculated mass normalized restoring force. The distribution of the nominal elastic stiffness over the breast surface is estimated using an F-type hypothesis test and regression analysis. A higher stiffness is identified in the region of the inclusions, which is at least 2x greater than the nominal stiffness in other areas of the phantom. Thus, the method accurately detects and locates the inclusion in typical, representative silicone breast phantoms without misidentifying other regions or a healthy no-inclusion phantom. The overall results show the capability of the proposed method, based on the identification of the local nominal stiffness over the phantom surface as an efficient index, to accurately detect inclusion presence and location in a rapid, objective fashion using the non-invasive DIET approach.