  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.
