SignificanceCellular metabolism is highly dynamic and strongly influenced by its local vascular microenvironment, gaining a systems-level view of cell metabolism in vivo is essential in understanding many critical biomedical problems in a broad range of disciplines. However, very few existing metabolic tools can quantify the major metabolic and vascular parameters together in biological tissues in vivo with easy access.AimWe aim to fill the technical gap by demonstrating a point-of-care, easy-to-use, easy-to-access, rapid, systematic optical spectroscopy platform for metabolic and vascular characterizations on biological models in vivo to enable scientific discoveries to translate more efficiently to clinical interventions.ApproachWe developed a highly portable optical spectroscopy platform with a tumor-sensitive fiber probe and easy-to-use spectroscopic algorithms for multi-parametric metabolic and vascular characterizations of biological tissues in vivo. We then demonstrated our optical spectroscopy on tissue-mimicking phantoms, human subjects, and small in vivo tumor models. We also validated the proposed easy-to-use algorithms with the Monte Carlo inversion models for accurate and rapid spectroscopic data processing.ResultsOur tissue-mimicking phantom, human subjects, and in vivo animal studies showed that our portable optical spectroscopy along with the new spectroscopic algorithms could quantify the major metabolic and vascular parameters on biological tissues with a high accuracy. We also captured the highly diverse metabolic and vascular phenotypes of head and neck tumors with different radiation sensitivities.ConclusionsOur highly portable optical spectroscopy platform along with easy-to-use spectroscopic algorithms will provide an easy-to-access way for rapid and systematic characterizations of biological tissue metabolism and vascular microenvironment in vivo, which may significantly advance translational cancer research in the future.
Significance: Optical fluorescence spectroscopy technique has been explored extensively to quantify both glucose uptake and mitochondrial metabolism with proper fluorescent probes in small tumor models in vivo. However, it remains a great challenge to rapidly quantify the intrinsic metabolic fluorophores from the optically measured fluorescence spectra that contain significant distortions due to tissue absorption and scattering.
Aim: To enable rapid spectral data processing and quantify the in vivo metabolic parameters in real-time, we present an empirical ratio-metric method for rapid fluorescence spectra attenuation correction with high accuracy.
Approach: A first-order approximation of intrinsic fluorescence spectra can be obtained by dividing the fluorescence spectra by diffuse reflectance spectra with some variable powers. We further developed this approximation for rapid extraction of intrinsic key metabolic probes (2-NBDG for glucose uptake and TMRE for mitochondrial function) by dividing the distorted fluorescence spectra by diffuse reflectance intensities recorded at excitation and emission peak with a pair of system-dependent powers. Tissue-mimicking phantom studies were conducted to evaluate the method.
Results: The tissue-mimicking phantom studies demonstrated that our empirical method could quantify the key intrinsic metabolic probes in near real-time with an average percent error of ∼5 % .
Conclusions: An empirical method was demonstrated for rapid quantification of key metabolic probes from fluorescence spectra measured on a tissue-mimicking turbid medium. The proposed method will potentially facilitate real-time monitoring of key metabolic parameters of tumor models in vivo using optical spectroscopy, which will significantly advance translational cancer research.
Key tissue parameters, e.g., total hemoglobin concentration and tissue oxygenation, are important biomarkers in clinical diagnosis for various diseases. Although point measurement techniques based on diffuse reflectance spectroscopy can accurately recover these tissue parameters, they are not suitable for the examination of a large tissue region due to slow data acquisition. The previous imaging studies have shown that hemoglobin concentration and oxygenation can be estimated from color measurements with the assumption of known scattering properties, which is impractical in clinical applications. To overcome this limitation and speed-up image processing, we propose a method of sequential weighted Wiener estimation (WE) to quickly extract key tissue parameters, including total hemoglobin concentration (CtHb), hemoglobin oxygenation (StO2), scatterer density (α), and scattering power (β), from wide-band color measurements. This method takes advantage of the fact that each parameter is sensitive to the color measurements in a different way and attempts to maximize the contribution of those color measurements likely to generate correct results in WE. The method was evaluated on skin phantoms with varying CtHb, StO2, and scattering properties. The results demonstrate excellent agreement between the estimated tissue parameters and the corresponding reference values. Compared with traditional WE, the sequential weighted WE shows significant improvement in the estimation accuracy. This method could be used to monitor tissue parameters in an imaging setup in real time.
Experimental investigation and optimization of various optical parameters in the design of depth sensitive optical measurements in layered tissues would require a huge amount of time and resources. A computational method to model light transport in layered tissues using Monte Carlo simulations has been developed for decades to reduce the cost incurred during this process. In this work, we employed the Monte Carlo method to investigate the depth sensitivity achieved by various illumination and detection configurations including both the traditional cone configurations and new cone shell configurations, which are implemented by convex or axicon lenses. Phantom experiments have been carried out to validate the Monte Carlo modeling of fluorescence in a two-layered turbid, epithelial tissue model. The measured fluorescence and depth sensitivity of different illumination–detection configurations were compared with each other. The results indicate excellent agreement between the experimental and simulation results in the trends of fluorescence intensity and depth sensitivity. The findings of this study and the development of the Monte Carlo method for noncontact setups provide useful insight and assistance in the planning and optimization of optical designs for depth sensitive fluorescence measurements.
We have investigated multiple lens based non-contact illumination and detection
configurations, including a conventional cone configuration and a novel cone shell configuration,
for depth sensitive diffuse reflectance and fluorescence measurements numerically and
experimentally.
A general survey is provided on the capability of Monte Carlo (MC) modeling in tissue optics while paying special attention to the recent progress in the development of methods for speeding up MC simulations. The principles of MC modeling for the simulation of light transport in tissues, which includes the general procedure of tracking an individual photon packet, common light–tissue interactions that can be simulated, frequently used tissue models, common contact/noncontact illumination and detection setups, and the treatment of time-resolved and frequency-domain optical measurements, are briefly described to help interested readers achieve a quick start. Following that, a variety of methods for speeding up MC simulations, which includes scaling methods, perturbation methods, hybrid methods, variance reduction techniques, parallel computation, and special methods for fluorescence simulations, as well as their respective advantages and disadvantages are discussed. Then the applications of MC methods in tissue optics, laser Doppler flowmetry, photodynamic therapy, optical coherence tomography, and diffuse optical tomography are briefly surveyed. Finally, the potential directions for the future development of the MC method in tissue optics are discussed.
The accurate assessment of skin flap viability is vitally important in reconstructive surgery. Early identification of
vascular compromise increases the change of successful flap salvage. The ability to determine tissue viability intraoperatively
is also extremely useful when the reconstructive surgeon must decide how to inset the flap and whether any
tissue must be discarded. Visible diffuse reflectance and auto-fluorescence spectroscopy, which yield different sets of
biochemical information, have not been used in the characterization of skin flap viability simultaneously to our best
knowledge. We performed both diffuse reflectance and fluorescence measurements on a reverse MacFarlane rat dorsal
skin flap model to identify the additional value of auto-fluorescence spectroscopy to the assessment of flap viability. Our
result suggests that auto-fluorescence spectroscopy appears to be more sensitive to early biochemical changes in a failed
flap than diffuse reflectance spectroscopy, which could be a valuable complement to diffuse reflectance spectroscopy for
the assessment of flap viability.
We present a hybrid method to speed up the Monte Carlo simulation of diffuse reflectance from a multi-layered tissue
model with finite-size tumor-like heterogeneities. The proposed method consists of two steps. In the first step, a set of
photon trajectory information generated from a baseline Monte Carlo simulation is utilized to scale the exit weight and
exit distance of survival photons for the multi-layered tissue model by using a multiple scaling method. In the second
step, another set of photon trajectory information including the locations of all collision events from the baseline
simulation and the scaling result obtained from the first step are employed by the perturbation Monte Carlo method to
estimate diffuse reflectance from the multi-layered tissue model with tumor-like heterogeneities. Our method is
demonstrated to be able to shorten simulation time by several orders of magnitude. Moreover, this hybrid method works
for a larger range of probe configurations and tumor models compared to the scaling method or the perturbation method
alone.
We present a hybrid method that combines a multilayered scaling method and a perturbation method to speed up the Monte Carlo simulation of diffuse reflectance from a multilayered tissue model with finite-size tumor-like heterogeneities. The proposed method consists of two steps. In the first step, a set of photon trajectory information generated from a baseline Monte Carlo simulation is utilized to scale the exit weight and exit distance of survival photons for the multilayered tissue model. In the second step, another set of photon trajectory information, including the locations of all collision events from the baseline simulation and the scaling result obtained from the first step, is employed by the perturbation Monte Carlo method to estimate diffuse reflectance from the multilayered tissue model with tumor-like heterogeneities. Our method is demonstrated to shorten simulation time by several orders of magnitude. Moreover, this hybrid method works for a larger range of probe configurations and tumor models than the scaling method or the perturbation method alone.
We present our work toward implementing all-digital signal processing for Positron Emission Tomography (PET)
event detection. In the conventional PET system, proper calibration and extending event processing are challenging
tasks due to the huge number of channels and multiplexing of input signals in the mixed-signal front-end.
To alleviate such limitations, we have proposed a simple all-digital PET system utilizing digital signal processing
(DSP) technologies for analyzing event pulses generated in PET. In this work, we implement a Gaussian shaper
circuit for scintillation pulses, which followed by a moderate sampling rate Analog-to-Digital Converter (ADC).
We also evaluate two DSP algorithms for extracting time information from the digitized pulse samples, and the
two algorithms examined could generate a coincidence timing resolution of ~ 2.4ns FWHM, by using a 125MSps
sampling rate ADC.
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