SignificanceIn the last years, time-resolved near-infrared spectroscopy (TD-NIRS) has gained increasing interest as a tool for studying tissue spectroscopy with commercial devices. Although it provides much more information than its continuous wave counterpart, accurate models interpreting the measured raw data in real time are still lacking.AimWe introduce an analytical model that can be integrated and used in TD-NIRS data processing software and toolkits in real time. This is based on the so-called sensitivity factors (SFs) of the distributions of time of flight (DTOFs) of photons measured in optically turbid and semi-infinite multilayered media, such as the human head.ApproachWe derived analytical expressions for the SFs that link changes in the absorption coefficient of each layer to changes in the statistical moments of DTOFs acquired in a reflectance configuration. This was later validated with results from Monte Carlo (MC) simulations, which stand as the gold standard in terms of photon migration in biological tissue. Next, we designed a couple of simulated experiments depicting how the analytical SFs can be used to retrieve absorption changes in the particular case of a five-layered medium.ResultsComparison between theory and simulations in 2-, 5-, and 10-layered media showed very good agreement (in most cases with weighted mean absolute percentage errors below 10%). Moreover, our derivations could be run in a few milliseconds (except for the extreme case of the variance SF in the 10-layered medium), which means a speedup of up to 10,000× with respect to MC simulations, with a much better spatial resolution and without their typically associated stochastic noise.ConclusionsIn summary, our method achieves performances similar to those given by MC simulations, but orders of magnitude faster, which makes it very suitable for its implementation in real-time applications.
SignificanceFluorescence sensing within tissue is an effective tool for tissue characterization; however, the modality and geometry of the image acquisition can alter the observed signal.AimWe introduce a novel optical fiber-based system capable of measuring two fluorescent contrast agents through 2 cm of tissue with simple passive electronic switching between the excitation light, simultaneously acquiring fluorescence and excitation data. The goal was to quantify indocyanine green (ICG) and protoporphyrin IX (PpIX) within tissue, and the sampling method was compared with wide-field surface imaging to contrast the value of deep sensing versus surface imaging.ApproachThis was achieved by choosing filters for specific wavelengths that were mutually exclusive between ICG and PpIX and coupling these filters to two separate detectors, which allows for direct swapping of the excitation and emission channels by switching the on-time of each excitation laser between 780- and 633-nm wavelengths.ResultsThis system was compared with two non-contact surface imaging systems for both ICG and PpIX, which revealed that the fluorescence depth sensing system was superior in its ability to resolve kinetics differences in deeper tissues that would normally be dominated by strong signals from skin and other surface tissues. Specifically, the system was tested using pancreatic adenocarcinoma tumors injected into murine models, which were imaged at several time points throughout tumor growth to its ∼6-mm diameter. This demonstrated the system’s capability to track longitudinal changes in ICG and PpIX kinetics that result from tumor growth and development, with larger tumors showing sluggish uptake and clearance of ICG, which was not observable with surface imaging. Similarly, PpIX was quantified, which showed slower kinetics over different time points, and was further compared with the wide-filed imager. These results were further validated through depth measurements in tissue phantoms and model-based interpretation.ConclusionThis fluorescence depth sensing system can be used to sample the interior blood flow characteristics by ICG sensing of tissue as deep as 20 mm into the tissue with sensitivity to kinetics that are superior to surface imaging and may be combined with other imaging modalities such as ultrasound to provide guided deep fluorescence measurements.
SignificanceContinuous-wave functional near-infrared spectroscopy has proved to be a valuable tool for assessing hemodynamic activity in the human brain in a non-invasively and inexpensive way. However, most of the current processing/analysis methods assume the head is a homogeneous medium, and hence do not appropriately correct for the signal coming from the scalp. This effect can be reduced by considering light propagation in a layered model of the human head, being the Monte Carlo (MC) simulations the gold standard to this end. However, this implies large computation times and demanding hardware capabilities.AimIn this work, we study the feasibility of replacing the homogeneous model and the MC simulations by means of analytical multilayered models, combining in this way, the speed and simplicity of implementation of the former with the robustness and accuracy of the latter.ApproachOxy- and deoxyhemoglobin (HbO and HbR, respectively) concentration changes were proposed in two different layers of a magnetic resonance imaging (MRI)-based meshed model of the human head, and then these changes were retrieved by means of (i) a typical homogeneous reconstruction and (ii) a theoretical layered reconstruction.ResultsResults suggest that the use of analytical models of light propagation in layered models outperforms the results obtained using traditional homogeneous reconstruction algorithms, providing much more accurate results for both, the extra- and the cerebral tissues. We also compare the analytical layered reconstruction with MC-based reconstructions, achieving similar degrees of accuracy, especially in the gray matter layer, but much faster (between 4 and 5 orders of magnitude).ConclusionsWe have successfully developed, implemented, and validated a method for retrieving chromophore concentration changes in the human brain, combining the simplicity and speed of the traditional homogeneous reconstruction algorithms with robustness and accuracy much more similar to those provided by MC simulations.
In this work we introduce an analytical way of computing the photon measurement density functions in multilayered flat and spherical media. Comparisons with Monte Carlo simulations in the particular case of two-layered media show very good agreement (differences below 10%), with the additional advantage that the time taken by the theoretical calculations is several orders of magnitude (more than 6) lower than the corresponding Monte Carlo calculations.
In this work we derive general equations for the analytical calculation of photon mean partial pathlengths (MPPLs) in turbid media with an arbitrary number of layers. Comparisons with their Monte Carlo (MC) counterpart show excellent agreement. These quantities can now be used to retrieve haemoglobin concentrations changes in cerebral blood in real-time and with minimal computing requirements.
KEYWORDS: Spherical lenses, Data modeling, Near infrared, Head, Multilayers, Monte Carlo methods, Magnetic resonance imaging, Absorption, Skull, Photons
We compare flat and spherical models of the human head using Bayesian inference. Monte Carlo simulations are used to obtain the photons times of flight. Results suggest that the spherical model better represents the data.
We introduce an implementation of the Extended Kalman Filter for the retrieval of absorption coefficients in layered turbid media. The technique was validated with experiments in a liquid phantom.
In this work we introduce a theoretical model for light propagation in multilayered, turbid cylinders with an infinitely
thick bottom layer, which can be applied to the study of biological systems such as the human head. Our approach was
validated with experiments on a three-layered phantom and with Monte Carlo simulations. We show that the absorption
and the reduced scattering coefficient of the deepest layer can be retrieved within reasonable errors.
Diffuse optical imaging of the human brain requires methods to account for the layered structure of the head. In this work we present results of experiments performed on layered phantoms in reflection geometry by a time-resolved technique. We investigate structures with two and three layers with the goal to retrieve the optical properties of the deepest one. Data analysis is based on an existing solution of the time-resolved diffusion equation for a multilayer cylinder. Using a sufficiently large source-detector separation the absorption and reduced scattering coefficients of the deepest layer can be derived from time-resolved reflectance with a deviation of typically not more than 10% from the known values.
Optical imaging through highly scattering media such as biological tissues is a topic of intense research, especially for
biomedical applications. Diverse optical systems are currently under study and development for displaying the functional
imaging of the brain and for the detection of breast tumors. From the theoretical point of view, a suitable description of
light propagation in tissues involves the Radiative Transfer Equation, which considers the energetic aspects of light
propagation. However, this equation cannot be solved analytically in a closed form and the Diffusion Approximation is
normally used. Experimentally it is possible to use Transmission or Reflection geometries and Time Resolved,
Frequency Modulated or CW sources. Each configuration has specific advantages and drawbacks, depending on the
desired application. In the present contribution, we investigate the reflected light images registered by a CCD camera
when scattering and absorbing inhomogeneities are located at different depths inside turbid media. This configuration is
of particular interest for the detection and optical characterization of changes in blood flow in organs, as well as for the
detection and characterization of inclusions in those cases for which the transmission slab geometry is not well suited.
Images are properly normalized to the background intensity and allow analyzing relative large areas (typically 5 × 5 cm2)
of the tissue. We tested the proposal using Numerical Monte Carlo simulations implemented in a Graphic Processing
Unit (Video accelerating Card). Calculations are thus several orders of magnitude faster than those run in CPU.
Experimental results in phantoms are also given.
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