During the last few years a quite large number of fluorescence imaging applications have been reported in the literature, as one of the most challenging problems in medical imaging is to ''see'' a tumor embedded in tissue, which is a turbid medium. This problem has not been fully encountered yet, due to the non-linear nature of the inverse problem. In this paper, a novel method for processing the forward solver outcomes is presented. Through this technique the comparison between the simulated and the acquired data can be performed only at the region-of-interest, minimizing time-consuming pixel-to-pixel comparison. With this modus operandi a-priori information about the initial fluorophore distribution becomes available, leading to a more feasible inverse problem solution.
In the past decade, there has been significant progress in the development of water soluble near-infrared fluorochromes for use in a wide range of imaging applications. Fluorochromes with high photo and thermal stability, sensitivity, adequate pharmacological properties and absorptio...
A contraction analysis of risk-sensitive Riccati equations is proposed. When
the state-space model is reachable and observable, a block-update
implementation of the risk-sensitive filter is used to show that the N-fold
composition of the Riccati map is strictly contractive with respect to the
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