Strategies for Absolute Calibration of Near Infrared Tomographic Tissue Imaging

Abstract

Quantitative near infrared (NIR) imaging of tissue requires the use of a diffusion model-based reconstruction algorithm, which solves for the absorption and scattering coefficients of a tissue volume by matching transmission measurements of light to the predictive diffusion equation solution. Calibration problems as well as other practical considerations arise for an imaging system when using a model-based method for a real system. For example, systematic noise in the data acquisition hardware and source/detector fibers must be removed to prevent spurious results in the reconstructed image. Practical considerations for a NIR diffuse tomographic imaging system include: (1) calibration with a homogeneous phantom, (2) use of a homogeneous fitting algorithm to arrive at an initial optical property estimate for image reconstruction of a heterogeneous medium, and (3) correction for fluctuations in source strength and initial phase offset during data acquisition. These practical considerations, which rely on an accurate homogeneous fitting algorithm are described. They have allowed demonstration of a prototype imaging system that has the ability to quantitatively reconstruct heterogeneous images of hemoglobin concentrations within a highly scattering medium with no a priori information.