No abstract

Diffuse optical tomographic imaging has broad application to the field of clinical diagnosis of diseased tissue states. In particular, there has been considerable interest in applying diffuse optical imaging to breast cancer detection. We explore the effects of such parameters as regularization, source-to-source and detector-to-detector spacing, voxel size and source modulation frequency on characteristics of the point spread function (PSF) of the reconstructed image. An understanding of these effects is critical to the optimization of diffuse optical tomographic imaging techniques. We also present a figure of merit that can be used to compare various reconstruction techniques and parameters when considering the effects of noise on quantitative accuracy and resolution. In all cases we look at heterogeneities with changes in absorption only and limit the analysis to the linear approximation of the diffusion equation. We show that the resolution and quantitative accuracy are fundamentally limited by the degree of regularization required to achieve images of acceptable contrast in the presence of representative noise levels. All simulations are based on Tikhonov regularization and truncated singular value decomposition (TSVD) reconstruction techniques for which we have generally achieved best results.