Optical Tomographic Imaging for Breast Cancer Detection

TL;DR

This paper introduces a new stable and accurate method for reconstructing images in diffuse optical tomography to detect breast cancer, effectively localizing and quantifying abnormal tissues despite noise and incomplete data.

Contribution

It proposes a novel two-step image reconstruction approach that improves stability and accuracy in breast cancer detection using diffuse optical tomography.

Findings

Method is stable and accurate in simulations

Robust against measurement noise

Reduces unknown variables for better reconstruction

Abstract

Diffuse optical breast imaging utilizes near-infrared (NIR) light propagation through tissues to assess the optical properties of tissue for the identification of abnormal tissue. This optical imaging approach is sensitive, cost-effective, and does not involve any ionizing radiation. However, the image reconstruction of diffuse optical tomography (DOT) is a nonlinear inverse problem and suffers from severe ill-posedness, especially in the cases of strong noise and incomplete data. In this paper, a novel image reconstruction method is proposed for the detection of breast cancer. This method split the image reconstruction problem into the localization of abnormal tissues and quantification of absorption variations. The localization of abnormal tissues is performed based on a new well-posed optimization model, which can be solved via differential evolution optimization method to achieve a stable image reconstruction. The quantification of abnormal absorption variations is then determined in localized regions of relatively small extents, which are potentially tumors. Consequently, the number of unknown absorption variables can be greatly reduced to overcome the underdetermined nature of diffuse optical tomography (DOT), allowing for accurate and stable reconstruction of the abnormal absorption variations in the breast. Numerical simulation experiments show that the image reconstruction method is stable and accurate for the identification of abnormal tissues, and robust against measurement noise of data.