Optical Based Elastography Method for Breast Tumor Detection
J Lee1*, C Won2, (1) Keimyung University, Daegu, Gyeongbuk, (2) Temple University, Philadelphia, PASU-E-CAMPUS-I-5 Sunday 3:00PM - 6:00PM Room: Exhibit Hall
Purpose: In this research, we present an optical based elastography device which can be used for practical medical diagnostic experiments for measuring stiffness and geometry of lesion.
Methods: The optical elastography incorporates an optical waveguide sensing probe unit, a light source unit, a camera unit, and a computer unit. The optical method of total internal reflection phenomenon in an optical waveguide is adapted for the tissue elasticity measurement principle. The light sources are attached along the edges of the waveguide and illuminates at a critical angle to totally reflect the light within the waveguide. Once the waveguide is deformed due to the stiff inclusion, it causes the trapped light to change the critical angle and diffuse outside the waveguide. The scattered light is captured by a camera. From elastography 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.
Results: The proposed elasticity characterization device and method were validated by the realistic tissue phantom with inclusions to emulate the tumors. The phantom was made of a silicone composite having a Youngs modulus of approximately 5kPa. The inclusion was made using another silicone composite; the stiffness of which was higher than the surrounding tissue phantom. The Youngs modulus of each inclusion was 120kPa, which is for fibrous tissue at 5% pre-compression with loading frequency of 4.0Hz. The experimental results showed that, the proposed characterization method estimated the size, depth, and Youngs modulus of a tissue inclusion within 0.16mm, 0.18mm, and 0.66kPa relative errors, respectively.
Conclusion: An elastography device using the total internal reflection principle and its algorithms were designed and experimentally evaluated. The work presented in this research is the initial step towards early detection of malignant breast tumors.