Weighting scheme for robust depth normalization in reflectance time-domain diffuse optical tomography
Abstract
Significance: Reflectance mode diffuse optical tomography (DOT) is limited in its depth sensitivity. Current approaches to improve depth sensitivity need some prior knowledge to correctly localize deep inclusions. It has been shown that time-domain (TD) measurements can enhance depth sensitivity, but reconstructions still tend to be biased toward the surface, and TD modeling of diffuse light is computationally burdensome. Efficient TD modeling of diffuse light and prior-free reflectance DOT is critical to streamline the adoption of the technology into clinics.
Aim: Our aim is to develop an efficient simulation tool for TD fluence in tissue as well a weighted reconstruction algorithm that uses TD measurements to enhance both the depth sensitivity and spatial selectivity of DOT without relying on any prior knowledge of the tissue structure or inclusion location.
Approach: To overcome the computational burden of TD modeling of diffuse light, we solve the diffusion approximation in the Fourier domain for many frequencies parallelizing our solver across mesh nodes, sources, and frequencies. Using only the sensitivity matrix calculated from this modeled fluence, we calculate a weighting matrix that optimizes the spatial selectivity and depth sensitivity of our measurements. This weighting matrix was used in a Levenburg-Marquardt algorithm to retrieve the three-dimensional absorption profile of single inclusions at different depths, two complex scenes with inclusion(s) that spanned…
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