Inclusion mechanical property estimation using tactile images, finite element method, and artificial neural network

A model study of 3-dimensional localization of breast tumors using piezoelectric fingers of different probe sizes

Mammography is the only Food and Drug Administration approved breast cancer screening method. The drawback of the tumor image in a mammogram is the lack of tumor depth information as it is only a 2-dimensional projection of a 3-dimensional (3D) tumor. In this work, we investigated 3D tumor imaging by assessing tumor depth information using a set of piezoelectric fingers (PEFs) with different probe sizes which were known to be capable of eliciting tissue elastic responses to different depths and tested it on model tumor tissues consisted of gelatin with suspended clay inclusions. The locations of the top and bottom surfaces of an inclusion were resolved by solving a simple spring model using the elastic measurements of the PEFs of different probe sizes as the input. The lateral sizes of an inclusion were determined as the full width at half maximum of the Gaussian fit to the measured lateral tumor elastic modulus profile. The obtained lateral inclusion sizes were in close agreement with the actual values, and the deduced depth profiles of an inclusion also agreed with the actual depth profiles so long as the bottom surface of the inclusion was within the depth sensitivity of the PEF with the largest probe size. This work offers a simple non-invasive method to predict the extent of a tumor in all 3 dimensions. The method is also non-radioactive.

The tactile sensation imaging system for embedded lesion characterization

Elasticity is an important indicator of tissue health, with increased stiffness pointing to an increased risk of cancer. We investigated a tissue inclusion characterization method for the application of early breast tumor identification. A tactile sensation imaging system (TSIS) is developed to capture images of the embedded lesions using total internal reflection principle. From tactile images, we developed a novel method to estimate that size, depth, and elasticity of the embedded lesion using 3-D finite-element-model-based forward algorithm, and neural-network-based inversion algorithm are employed. The proposed characterization method was validated by the realistic tissue phantom with inclusions to emulate the tumors. The experimental results showed that, the proposed characterization method estimated the size, depth, and Young's modulus of a tissue inclusion with 6.98%, 7.17%, and 5.07% relative errors, respectively. A pilot clinical study was also performed to characterize the lesion of human breast cancer patients using TSIS.