In the present contribution we investigate the images of CW diffusely reflected light for a point-like source, registered by a CCD camera imaging a turbid medium containing an absorbing lesion. We show that detection of μa variations (absorption anomalies) is achieved if images are normalized to background intensity. A theoretical analysis based on the diffusion approximation is presented to investigate the sensitivity and the limitations of our proposal and a novel procedure to find the location of the inclusions in 3D is given and tested. An analysis of the noise and its influence on the detection capabilities of our proposal is provided. Experimental results on phantoms are also given, supporting the proposed approach.

In diffuse optical tomography (DOT) the main objective is to estimate the absorption coefficent and the reduced scattering coefficient of a certain media given a set of boundary measurements. Biological tissues contains many objects such as arteries, skin and fat whose optical properties are rather different than those of the media. When these values are near to those of the background, linear techniques are usually used to estimate them. However, certain objects, such as tumors, may have properties which cannot be well estimated with linear models. In this article we present a non-linear approach for the frequency-domain problem based on an improvement of the extended Kalman filter (EKF) which is used in estimation-observation problems, and modified to the DOT parameter estimation problem. The EKF allows to incorporate prior information of the measurement noise as well as certain characteristics of the objective media. We show that the proposed methodology is equivalent to existing methods but can be applied to other schemes such as model reduction as suggested in previous works. Some computer simulations as well as experimental results are shown to validate our proposal.

In the present paper we propose an implementation of the Kalman filter algorithm, which allows simultaneous recovery of the absorption coefficient, the reduced scattering coefficient and the thicknesses of multi-layered turbid media, with the deepest layer taken as semi-infinite. The approach is validated by both Monte Carlo simulations and experiments, showing good results in structures made up of four layers. As it is a Bayesian algorithm, prior knowledge can be included to improve the accuracy of the retrieved unknowns. One of the most promising applications of this approach is the capability of real-time monitoring of living organs by near infrared spectroscopy. In particular, determination of blood perfusion in the adult head is one of the desired goals, allowing continuous control of stroke patients. This demands accurate measurement of the optical properties, especially absorption, of the head layers, from scalp to the cortex.

Functional near infrared spectroscopy (fNIRS) is a valuable tool for assessing oxy- and deoxyhemoglobin concentration changes (Δ[HbO] and Δ[HbR], respectively) in the human brain. To this end, photon pathlengths in tissue are needed to convert from light attenuation to Δ[HbO] and Δ[HbR]. Current techniques describe the human head as a homogeneous medium, in which case these pathlengths are easily computed. However, the head is more appropriately described as a layered medium; hence, the partial pathlengths in each layer are required. The current way to do this is by means of Monte Carlo (MC) simulations, which are time-consuming and computationally expensive. In this work, we introduce an approach to theoretically calculate these partial pathlengths, which are computed several times faster than MC simulations. Comparison of our approach with MC simulations show very good agreement. Results also suggest that these analytical expressions give much more specific information about light absorption in each layer than in the homogeneous case.

In this article we propose an implementation of the extended Kalman filter (EKF) for the retrieval of optical and geometrical properties in two-layered turbid media assuming a dynamic setting, where absorption of each layer was changed in different steps. Prior works implemented the EKF in frequency-domain with several pairs of light sources and detectors and for static parameters estimation problems. Here we explore the use of the EKF in single distance, time-domain measurements, together with a corresponding forward model. Results show good agreement between retrieved and nominal values, with rather narrow analytical credibility intervals, indicating that the recovery process has low uncertainty, especially for the absorption coefficients.