Monte Carlo modeling of light propagation in highly scattering tissue--I: Model predictions and comparison with diffusion theory

Optical Goniometer Paired with Digital Monte Carlo Twin to Determine the Optical Properties of Turbid Media

We present a goniometer designed for capturing spectral and angular-resolved data from scattering and absorbing media. The experimental apparatus is complemented by a comprehensive Monte Carlo simulation, meticulously replicating the radiative transport processes within the instrument's optical components and simulating scattering and absorption across arbitrary volumes. Consequently, we were able to construct a precise digital replica, or "twin", of the experimental setup. This digital counterpart enabled us to tackle the inverse problem of deducing optical parameters such as absorption and scattering coefficients, along with the scattering anisotropy factor from measurements. We achieved this by fitting Monte Carlo simulations to our goniometric measurements using a Levenberg-Marquardt algorithm. Validation of our approach was performed using polystyrene particles, characterized by Mie scattering, supplemented by a theoretical analysis of algorithmic convergence. Ultimately, we demonstrate strong agreement between optical parameters derived using our novel methodology and those obtained via established measurement protocols.

MCOCT: an experimentally and numerically validated, open-source Monte Carlo simulator for optical coherence tomography

Here, we present MCOCT, a Monte Carlo simulator for optical coherence tomography (OCT), incorporating a Gaussian illumination scheme and bias to increase backscattered event collection. MCOCT optical fluence was numerically compared and validated to an established simulator (MCX) and showed concordance at the focus while diverging slightly with distance to it. MCOCT OCT signals were experimentally compared and validated to OCT signals acquired in tissue-mimicking phantoms with known optical properties and showed a similar attenuation pattern with increasing depth while diverging beyond 1.5 mm and proximal to layer interfaces. MCOCT may help in the design of OCT systems for a wide range of applications.

Transient simulation of laser ablation based on Monte Carlo light transport with dynamic optical properties model

Laser ablation is a minimally invasive therapeutic technique to denature tumors through coagulation and/or vaporization. Computational simulations of laser ablation can evaluate treatment outcomes quantitatively and provide numerical indices to determine treatment conditions, thus accelerating the technique's clinical application. These simulations involve calculations of light transport, thermal diffusion, and the extent of thermal damage. The optical properties of tissue, which govern light transport through the tissue, vary during heating, and this affects the treatment outcomes. Nevertheless, the optical properties in conventional simulations of coagulation and vaporization remain constant. Here, we propose a laser ablation simulation based on Monte Carlo light transport with a dynamic optical properties (DOP) model. The proposed simulation is validated by performing optical properties measurements and laser irradiation experiments on porcine liver tissue. The DOP model showed the replicability of the changes in tissue optical properties during heating. Furthermore, the proposed simulation estimated coagulation areas that were comparable to experimental results at low-power irradiation settings and provided more than 2.5 times higher accuracy when calculating coagulation and vaporization areas than simulations using static optical properties at high-power irradiation settings. Our results demonstrate the proposed simulation's applicability to coagulation and vaporization region calculations in tissue for retrospectively evaluating the treatment effects of laser ablation.

Designing and simulating realistic spatial frequency domain imaging systems using open-source 3D rendering software

Spatial frequency domain imaging (SFDI) is a low-cost imaging technique that maps absorption and reduced scattering coefficients, offering improved contrast for important tissue structures such as tumours. Practical SFDI systems must cope with various imaging geometries including imaging planar samples ex vivo, imaging inside tubular lumen in vivo e.g. for endoscopy, and measuring tumours or polyps of varying morphology. There is a need for a design and simulation tool to accelerate design of new SFDI systems and simulate realistic performance under these scenarios. We present such a system implemented using open-source 3D design and ray-tracing software Blender that simulates media with realistic absorption and scattering in a wide range of geometries. By using Blender's Cycles ray-tracing engine, our system simulates effects such as varying lighting, refractive index changes, non-normal incidence, specular reflections and shadows, enabling realistic evaluation of new designs. We first demonstrate quantitative agreement between Monte-Carlo simulated absorption and reduced scattering coefficients with those simulated from our Blender system, achieving 16 % discrepancy in absorption coefficient and 18 % in reduced scattering coefficient. However, we then show that using an empirically derived look-up table the errors reduce to 1 % and 0.7 % respectively. Next, we simulate SFDI mapping of absorption, scattering and shape for simulated tumour spheroids, demonstrating enhanced contrast. Finally we demonstrate SFDI mapping inside a tubular lumen, which highlighted a important design insight: custom look-up tables must be generated for different longitudinal sections of the lumen. With this approach we achieved 2 % absorption error and 2 % scattering error. We anticipate our simulation system will aid in the design of novel SFDI systems for key biomedical applications.

mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics

Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral trade-off. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from a red-green-blue (RGB) image without complete hyperspectral measurements. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral trade-off, offering simple hardware requirements and potential applications of various machine learning techniques.

Single scattering models for radiative transfer of isotropic and cone-shaped light sources in fog

The simulation of rare edge cases such as adverse weather conditions is the enabler for the deployment of the next generation of autonomous drones and vehicles into conditions where human operation is error-prone. Therefore, such settings must be simulated as accurately as possible and be computationally efficient, so to allow the training of deep learning algorithms for scene understanding, which require large-scale datasets disallowing extensive Monte Carlo simulations. One computationally-expensive step is the simulation of light sources in scattering media, which can be tackled by the radiative transfer equation and approximated by analytical solutions in the following. Traditionally, a single scattering event is assumed for fog rendering, since it is the dominant effect for relatively low scattering media. This assumption allows us to present an improved solution to calculate the so called air-light integral that can be evaluated fast and robustly for an isotropic point source in homogeneous media. Additionally, the solution is extended for a cone-shaped source and implemented in a computer vision rendering pipeline fulfilling computational restrictions for deep learning uses. All solutions can handle arbitrary azimuthally symmetric phase functions and were tested with the Henyey-Greenstein phase function and an advection fog phase function calculated from a particle distribution using Mie's theory. The used approximations are validated through extensive Monte Carlo simulations and the solutions are used to augment good weather images towards inclement conditions with focus on visible light sources, so to provide additional data in such hard-to-collect settings.

Monte Carlo-based full-wavelength simulator of Fourier-domain optical coherence tomography

Monte Carlo (MC) simulation has been widely used to study imaging procedures, including Fourier-domain optical coherence tomography (FD-OCT). Despite the broadband nature of FD-OCT, the results obtained at a single wavelength are often used in previous studies. Some wavelength-relied imaging applications, such as spectroscopic OCT (S-OCT), are unlikely to be simulated in this way due to the lack of information from the entire spectrum. Here, we propose a novel simulator for full-wavelength MC simulation of FD-OCT. All wavelengths within the emission spectrum of the light source will be simulated, and the optical properties derived from Mie theory will be applied. We further combine the inverse discrete Fourier transform (IDFT) with a probability distribution-based signal pre-processing to combat the excessive noises in the OCT signal reconstruction, which is caused by the non-uniform distribution of the scattering events at different wavelengths. Proof-of-concept simulations are conducted to show the excellent performance of the proposed simulator on signal reconstruction and optical properties extraction. Compared with the conventional method, the proposed simulator is more accurate and could better preserve the wavelength-dependent features. For example, the mean square error (MSE) computed between the backscattering coefficient extracted by the proposed simulator and the ground truth is 0.11, which is far less than the value (7.67) of the conventional method. We believe this simulator could be an effective tool to study the wavelength dependency in FD-OCT imaging as well as a preferred solution for simulating spectroscopic OCT.

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

SIGNIFICANCE

Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the development of these algorithms, data are generally preprocessed to remove variability in data not related to the tissue itself since this will improve the performance of the classification algorithm. In hyperspectral imaging, the measured spectra are also influenced by reflections from the surface (glare) and height variations within and between tissue samples.

AIM

To compare the ability of different preprocessing algorithms to decrease variations in spectra induced by glare and height differences while maintaining contrast based on differences in optical properties between tissue types.

APPROACH

We compare eight preprocessing algorithms commonly used in medical hyperspectral imaging: standard normal variate, multiplicative scatter correction, min-max normalization, mean centering, area under the curve normalization, single wavelength normalization, first derivative, and second derivative. We investigate conservation of contrast stemming from differences in: blood volume fraction, presence of different absorbers, scatter amplitude, and scatter slope-while correcting for glare and height variations. We use a similarity metric, the overlap coefficient, to quantify contrast between spectra. We also investigate the algorithms for clinical datasets from the colon and breast.

CONCLUSIONS

Preprocessing reduces the overlap due to glare and distance variations. In general, the algorithms standard normal variate, min-max, area under the curve, and single wavelength normalization are the most suitable to preprocess data used to develop a classification algorithm for tissue classification. The type of contrast between tissue types determines which of these four algorithms is most suitable.

Numerical model of the irradiance field surrounding a UV disinfection robot

OBJECTIVE

New technologies, including robots comprising germ-killing UV lamps, are increasingly being used to decontaminate hospitals and prevent the spread of COVID-19 and other superbugs. Existing approaches for modelling the irradiance field surrounding mobile UV disinfection robots are limited by their inability to capture the physics of their bespoke geometrical configurations and do not account for reflections. The goal of this research was to extend current models to address these limitations and to subsequently verify these models using empirically collected data.

APPROACH

Two distinct parametric models were developed to describe a multi-lamp robotic UV system and adapted to incorporate the effects of irradiance amplification from the device's reflectors. The first model was derived from electromagnetic wave theory while the second was derived from conservation of energy and diffusion methods. Both models were tuned using data from empirical testing of an existing UV robot, and then validated using an independent set of measurements from the same device.

RESULTS

For each parameter, predictions made using the conservation of energy method were found to closely approximate the empirical data, offering more accurate estimates of the 3D irradiance field than the electromagnetic wave theory model.

SIGNIFICANCE

The versatility of the proposed method ensures that it can be easily adapted to different embodiments, providing a systematic way for researchers to develop accurate numerical models of custom UV robots, which may be used to inform deployment and/or to improve the accuracy of virtual simulation.

Randomized multi-angle illumination for improved linear array photoacoustic computed tomography in brain

One of the key challenges in linear array transducer-based photoacoustic computed tomography is to image structures embedded deep within the biological tissue with limited optical energy. Here, we utilized a manually controlled multi-angle illumination technique to allow the incident photons to interact with the imaging targets for longer periods of time and diffuse further in all directions. We have developed and optimized a compact probe that enables manual changes to the angle of illumination while acquiring photoacoustic signals. The performance has been demonstrated and evaluated by imaging complex blood vessel mimicking phantoms in-vitro and sheep brain samples ex-vivo. For effective image reconstruction from the data acquired by multi-angle illumination method, we have utilized a method based on the extraction of maximum intensity. In both cases, multi-angle illumination has out-performed the conventional fixed angle illumination technique to improve the overall image quality. Specifically, extraction of the imaging targets located at greater axial depths was possible using this multi-angle illumination technique.

MCDataset: a public reference dataset of Monte Carlo simulated quantities for multilayered and voxelated tissues computed by massively parallel PyXOpto Python package

SIGNIFICANCE

Current open-source Monte Carlo (MC) method implementations for light propagation modeling are many times tedious to build and require third-party licensed software that can often discourage prospective researchers in the biomedical optics community from fully utilizing the light propagation tools. Furthermore, the same drawback also limits rigorous cross-validation of physical quantities estimated by various MC codes.

AIM

Proposal of an open-source tool for light propagation modeling and an easily accessible dataset to encourage fruitful communications amongst researchers and pave the way to a more consistent comparison between the available implementations of the MC method.

APPROACH

The PyXOpto implementation of the MC method for multilayered and voxelated tissues based on the Python programming language and PyOpenCL extension enables massively parallel computation on numerous OpenCL-enabled devices. The proposed implementation is used to compute a large dataset of reflectance, transmittance, energy deposition, and sampling volume for various source, detector, and tissue configurations.

RESULTS

The proposed PyXOpto agrees well with the original MC implementation. However, further validation reveals a noticeable bias introduced by the random number generator used in the original MC implementation.

CONCLUSIONS

Establishing a common dataset is highly important for the validation of existing and development of MC codes for light propagation in turbid media.

An ultrasonography-based approach for tissue modelling to inform photo-therapy treatment strategies

Currently, diagnostic medicine uses a multitude of tools ranging from ionising radiation to histology analysis. With advances in piezoelectric crystal technology, high-frequency ultrasound imaging has developed to achieve comparatively high resolution without the drawbacks of ionising radiation. This research proposes a low-cost, non-invasive and real-time protocol for informing photo-therapy procedures using ultrasound imaging. We combine currently available ultrasound procedures with Monte Carlo methods for assessing light transport and photo-energy deposition in the tissue. The measurements from high-resolution ultrasound scans are used as input for optical simulations. Consequently, this provides a pipeline that will inform medical practitioners for better therapy strategy planning. While validating known inferences of light transport through biological tissue, our results highlight the range of information such as temporal monitoring and energy deposition at varying depths. This process also retains the flexibility of testing various wavelengths for individual-specific geometries and anatomy.

Multi-scattering software part II: experimental validation for the light intensity distribution

This article, Part II of an article series on GPU-accelerated Monte Carlo simulation of photon transport through turbid media, focuses on the validation of the online software Multi-Scattering. While Part I detailed the implementation of the computational model, simulated and experimental results are now compared for the distribution of the scattered light intensity. The scattering phantoms prepared here are aqueous dispersions of polystyrene microspheres of diameter D = 0.5, 2 and 5 μm and at various concentrations, resulting in optical depth ranging from OD = 1 to 17.5. The Lorenz-Mie scattering phase functions used in the simulations have been verified experimentally at low particle concentrations by analyzing the angular light intensity distribution at the Fourier plane of a collecting lens. The validation approach herein accounts for the specific light collection and image formation by the camera. The front and side surfaces of the medium are imaged and the corresponding light intensity distributions are compared qualitatively and quantitatively. It is concluded that the model enables reliable simulations over the tested parameters, offering predictive simulations of transmitted intensities with a mean relative error ≤~19% over the full range. The online software is available at: https://multi-scattering.com/.

Musiré: multimodal simulation and reconstruction framework for the radiological imaging sciences

A software-based workflow is proposed for managing the execution of simulation and image reconstruction for SPECT, PET, CBCT, MRI, BLI and FMI packages in single and multimodal biomedical imaging applications. The workflow is composed of a Bash script, the purpose of which is to provide an interface to the user, and to organize data flow between dedicated programs for simulation and reconstruction. The currently incorporated simulation programs comprise GATE for Monte Carlo simulation of SPECT, PET and CBCT, SpinScenario for simulating MRI, and Lipros for Monte Carlo simulation of BLI and FMI. Currently incorporated image reconstruction programs include CASToR for SPECT and PET as well as RTK for CBCT. MetaImage (mhd) standard is used for voxelized phantom and image data format. Meshlab project (mlp) containers incorporating polygon meshes and point clouds defined by the Stanford triangle format (ply) are employed to represent anatomical structures for optical simulation, and to represent tumour cell inserts. A number of auxiliary programs have been developed for data transformation and adaptive parameter assignment. The software workflow uses fully automatic distribution to, and consolidation from, any number of Linux workstations and CPU cores. Example data are presented for clinical SPECT, PET and MRI systems using the Mida head phantom and for preclinical X-ray, PET and BLI systems employing the Digimouse phantom. The presented method unifies and simplifies multimodal simulation setup and image reconstruction management and might be of value for synergistic image research. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Incident Fluence Analysis for 755-nm Picosecond Laser Treatment of Pigmented Skin Lesions Based on Threshold Fluences for Melanosome Disruption

Background and Objectives

In this study, the threshold fluences for disrupting the melanosomes for pigmented skin lesion treatment were determined using a 755‐nm picosecond laser for clinical use. Based on the melanosome disruption thresholds, incident fluences corresponding to the target lesion depths were evaluated in silico for different laser spot sizes.

Study Design/Materials and Methods

Melanosome samples were isolated from porcine eyes as alternative samples for human cutaneous melanosomes. The isolated melanosomes were exposed to 755‐nm picosecond laser pulses to measure the mean particle sizes by dynamic light scattering and confirm their disruption by scanning electron microscopy. The threshold fluences were statistically determined from the relationships between the irradiated fluences and the mean particle sizes. Incident fluences of picosecond laser pulses for the disruption of melanosomes located at different depths in skin tissue were calculated through a light transport simulation using the obtained thresholds.

Results

The threshold fluences of 550‐ and 750‐picosecond laser pulses were determined to be 2.19 and 2.49 J/cm², respectively. The numerical simulation indicated that appropriate incident fluences of picosecond laser pulses differ depending on the depth distribution of the melanosomes in the skin tissue, and large spot sizes are desirable for disrupting the melanosomes more deeply located within the skin tissue.

Conclusion

The threshold fluences of picosecond laser pulses for melanosome disruption were determined. The incident fluence analysis based on the thresholds for melanosome disruption provides valuable information for the selection of irradiation endpoints for picosecond laser treatment of pigmented skin lesions. Lasers Surg. Med. © 2021 The Authors. Lasers in Surgery and Medicine published by Wiley Periodicals LLC

Malignant cell characterization via mathematical analysis of bio impedance and optical properties

Diagnosis in the early stage of breast cancer is crucial for the onset of preliminary treatment. Non-radiative bioimpedance measurement in the microwave frequency range can contribute to electrode-medium interface error and the malaise of electrode placement on the patient to take measurements. These reasons account for alternate diagnosis procedure and improved reliability of retrieved mensuration. Non-invasive optical diagnosis in the near infra-red (NIR) and visible light of the electromagnetic range is the shifting paradigm for healthcare diagnosis. An accurate quantitative measurement is unparalleled to circumvent false positives. The focus of this paper is to perform quantitative mathematical analysis for bioimpedance and optical properties for sample breast cancer cells for meticulous interpretation of malignant cell diagnosis. The analytical solution of the Cole-Cole plot, relaxation frequency, and capacitance measurement showed reliability with previous experimental findings. The dissimilitude of the frequency-dependent refractive index measurement of the malignant and healthy cell can be used by clinicians for pronouncement. The diffusion theory is also used to interpret the pathlength of the source light particle and the absorption property of the malignant cell. The synergistic analytical solutions of the bioimpedance and optical parameters can be used by licensed Physicians or Clinical Practitioners (CP) to meticulously interpret the diagnosis result. The quantitative parameters obtained from the dispersed bandwidth range from microwave to visible light offers a comprehensive understanding of the biophysical properties of the malignant cell.

Multi-Scattering software: part I: online accelerated Monte Carlo simulation of light transport through scattering media

In this article we present and describe an online freely accessible software called Multi-Scattering for the modeling of light propagation in scattering and absorbing media. Part II of this article series focuses on the validation of the model by rigorously comparing the simulated results with experimental data. The model is based on the use of the Monte Carlo method, where billions of photon packets are being tracked through simulated cubic volumes. Simulations are accelerated by the use of general-purpose computing on graphics processing units, reducing the computation time by a factor up to 200x in comparison with a single central processing unit thread. By using four graphic cards on a single computer, the simulation speed increases by a factor of 800x. For an anisotropy factor g = 0.86, this enables the transport path of one billion photons to be computed in 10 seconds for optical depth OD = 10 and in 20 minutes for OD = 500. Another feature of Multi-Scattering is the integration and implementation of the Lorenz-Mie theory in the software to generate the scattering phase functions from spherical particles. The simulations are run from a computer server at Lund University, allowing researchers to log in and use it freely without any prior need for programming skills or specific software/hardware installations. There are countless types of scattering media in which this model can be used to predict light transport, including medical tissues, blood samples, clouds, smoke, fog, turbid liquids, spray systems, etc. An example of simulation results is given here for photon propagation through a piece of human head. The software also includes features for modeling image formation by inserting a virtual collecting lens and a detection matrix which simulate a camera objective and a sensor array respectively. The user interface for setting-up simulations and for displaying the corresponding results is found at: https://multi-scattering.com/.

Optimizing Irradiation Geometry in LED-Based Photoacoustic Imaging with 3D Printed Flexible and Modular Light Delivery System

Photoacoustic (PA) imaging–a technique combining the ability of optical imaging to probe functional properties of the tissue and deep structural imaging ability of ultrasound–has gained significant popularity in the past two decades for its utility in several biomedical applications. More recently, light-emitting diodes (LED) are being explored as an alternative to bulky and expensive laser systems used in PA imaging for their portability and low-cost. Due to the large beam divergence of LEDs compared to traditional laser beams, it is imperative to quantify the angular dependence of LED-based illumination and optimize its performance for imaging superficial or deep-seated lesions. A custom-built modular 3-D printed hinge system and tissue-mimicking phantoms with various absorption and scattering properties were used in this study to quantify the angular dependence of LED-based illumination. We also experimentally calculated the source divergence of the pulsed-LED arrays to be 58° ± 8°. Our results from point sources (pencil lead phantom) in non-scattering medium obey the cotangential relationship between the angle of irradiation and maximum PA intensity obtained at various imaging depths, as expected. Strong dependence on the angle of illumination at superficial depths (−5°/mm at 10 mm) was observed that becomes weaker at intermediate depths (−2.5°/mm at 20 mm) and negligible at deeper locations (−1.1°/mm at 30 mm). The results from the tissue-mimicking phantom in scattering media indicate that angles between 30–75° could be used for imaging lesions at various depths (12 mm–28 mm) where lower LED illumination angles (closer to being parallel to the imaging plane) are preferable for deep tissue imaging and superficial lesion imaging is possible with higher LED illumination angles (closer to being perpendicular to the imaging plane). Our results can serve as a priori knowledge for the future LED-based PA system designs employed for both preclinical and clinical applications.

Surgical spectral imaging

Recent technological developments have resulted in the availability of miniaturised spectral imaging sensors capable of operating in the multi- (MSI) and hyperspectral imaging (HSI) regimes. Simultaneous advances in image-processing techniques and artificial intelligence (AI), especially in machine learning and deep learning, have made these data-rich modalities highly attractive as a means of extracting biological information non-destructively. Surgery in particular is poised to benefit from this, as spectrally-resolved tissue optical properties can offer enhanced contrast as well as diagnostic and guidance information during interventions. This is particularly relevant for procedures where inherent contrast is low under standard white light visualisation. This review summarises recent work in surgical spectral imaging (SSI) techniques, taken from Pubmed, Google Scholar and arXiv searches spanning the period 2013-2019. New hardware, optimised for use in both open and minimally-invasive surgery (MIS), is described, and recent commercial activity is summarised. Computational approaches to extract spectral information from conventional colour images are reviewed, as tip-mounted cameras become more commonplace in MIS. Model-based and machine learning methods of data analysis are discussed in addition to simulation, phantom and clinical validation experiments. A wide variety of surgical pilot studies are reported but it is apparent that further work is needed to quantify the clinical value of MSI/HSI. The current trend toward data-driven analysis emphasises the importance of widely-available, standardised spectral imaging datasets, which will aid understanding of variability across organs and patients, and drive clinical translation.

Measurement of absorption and reduced scattering coefficients in Asian human epidermis, dermis, and subcutaneous fat tissues in the 400- to 1100-nm wavelength range for optical penetration depth and energy deposition analysis

SIGNIFICANCE

In laser therapy and diagnosis of skin diseases, the irradiated light distribution, which is determined by the absorption coefficient μa and reduced scattering coefficient μs' of the epidermis, dermis, and subcutaneous fat, affects the treatment outcome and diagnosis accuracy. Although values for μa and μs' have been reported, detailed analysis for Asian skin tissues is still lacking.

AIM

We present μa and μs' measurements of Asian skin tissues in the 400- to 1100-nm wavelength range for evaluating optical penetration depth and energy deposition.

APPROACH

The measurements with Asian human skin samples are performed employing a double integrating sphere spectrometric system and an inverse Monte Carlo technique. Using the measured parameters, the optical penetration depth and energy deposition are quantitatively analyzed.

RESULTS

The μa of the epidermis layer varies among different ethnic groups, while the μa of the other layers and the μs' of all of the layers exhibit almost no differences. The analysis reveals that the optical penetration depth and the energy deposition affect the photodynamic therapy treatment depth and the heat production in skin tissue, respectively.

CONCLUSIONS

The experimentally measured values of μa and μs' for Asian skin tissues are presented, and the light behavior in Asian skin tissues is analyzed using a layered tissue model.

Imaging depth variations in hyperspectral imaging: Development of a method to detect tumor up to the required tumor-free margin width

Hyperspectral imaging is a promising technique for resection margin assessment during cancer surgery. Thereby, only a specific amount of the tissue below the resection surface, the clinically defined margin width, should be assessed. Since the imaging depth of hyperspectral imaging varies with wavelength and tissue composition, this can have consequences for the clinical use of hyperspectral imaging as margin assessment technique. In this study, a method was developed that allows for hyperspectral analysis of resection margins in breast cancer. This method uses the spectral slope of the diffuse reflectance spectrum at wavelength regions where the imaging depth in tumor and healthy tissue is equal. Thereby, tumor can be discriminated from healthy breast tissue while imaging up to a similar depth as the required tumor-free margin width of 2 mm. Applying this method to hyperspectral images acquired during surgery would allow for robust margin assessment of resected specimens. In this paper, we focused on breast cancer, but the same approach can be applied to develop a method for other types of cancer.

Method for coregistration of optical measurements of breast tissue with histopathology: the importance of accounting for tissue deformations

For the validation of optical diagnostic technologies, experimental results need to be benchmarked against the gold standard. Currently, the gold standard for tissue characterization is assessment of hematoxylin and eosin (H&E)-stained sections by a pathologist. When processing tissue into H&E sections, the shape of the tissue deforms with respect to the initial shape when it was optically measured. We demonstrate the importance of accounting for these tissue deformations when correlating optical measurement with routinely acquired histopathology. We propose a method to register the tissue in the H&E sections to the optical measurements, which corrects for these tissue deformations. We compare the registered H&E sections to H&E sections that were registered with an algorithm that does not account for tissue deformations by evaluating both the shape and the composition of the tissue and using microcomputer tomography data as an independent measure. The proposed method, which did account for tissue deformations, was more accurate than the method that did not account for tissue deformations. These results emphasize the need for a registration method that accounts for tissue deformations, such as the method presented in this study, which can aid in validating optical techniques for clinical use.

Towards real-time multispectral endoscopic imaging for cardiac lesion quality assessment

Atrial fibrillation (Afib) can lead to life threatening conditions such as heart failure and stroke. During Afib treatment, clinicians aim to repress unusual electrical activity by electrically isolating the pulmonary veins (PV) from the left atrium (LA) using radiofrequency ablation. However, current clinical tools are limited in reliably assessing transmurality of the ablation lesions and detecting the presence of gaps within ablation lines, which can warrant repeat procedures. In this study, we developed an endoscopic multispectral reflectance imaging (eMSI) system for enhanced discrimination of tissue treatment at the PV junction. The system enables direct visualization of cardiac lesions through an endoscope at acquisition rates up to 25 Hz. Five narrowband, high-power LEDs were used to illuminate the sample (450, 530, 625, 810 and 940nm) and combinatory parameters were calculated based on their relative reflectance. A stitching algorithm was employed to generate large field-of-view, multispectral mosaics of the ablated PV junction from individual eMSI images. A total of 79 lesions from 15 swine hearts were imaged, ex vivo. Statistical analysis of the acquired five spectral data sets and ratiometric maps revealed significant differences between transmural lesions, non-transmural lesions around the venoatrial junctions, unablated posterior wall of left atrium tissue, and pulmonary vein (p < 0.0001). A pixel-based quadratic discriminant analysis classifier was applied to distinguish four tissue types: PV, untreated LA, non-transmural and transmural lesions. We demonstrate tissue type classification accuracies of 80.2% and 92.1% for non-transmural and transmural lesions, and 95.0% and 92.8% for PV and untreated LA sites, respectively. These findings showcase the potential of eMSI for lesion validation and may help to improve AFib treatment efficacy.

Three-dimensional quantitative photoacoustic tomography using an adjoint radiance Monte Carlo model and gradient descent

Quantitative photoacoustic tomography aims to recover maps of the local concentrations of tissue chromophores from multispectral images. While model-based inversion schemes are promising approaches, major challenges to their practical implementation include the unknown fluence distribution and the scale of the inverse problem. We describe an inversion scheme based on a radiance Monte Carlo model and an adjoint-assisted gradient optimization that incorporates fluence-dependent step sizes and adaptive moment estimation. The inversion is shown to recover absolute chromophore concentrations, blood oxygen saturation, and the Grüneisen parameter from in silico three-dimensional phantom images for different radiance approximations. The scattering coefficient is assumed to be homogeneous and known a priori.

MCmatlab: an open-source, user-friendly, MATLAB-integrated three-dimensional Monte Carlo light transport solver with heat diffusion and tissue damage

While there exist many Monte Carlo (MC) programs for solving the radiative transfer equation (RTE) in biological tissues, we have identified a need for an open-source MC program that is sufficiently user-friendly for use in an education environment, in which detailed knowledge of compiling or UNIX command-line cannot be assumed. Therefore, we introduce MCmatlab, an open-source codebase thus far consisting of (a) a fast three-dimensional MC RTE solver and (b) a finite-element heat diffusion and Arrhenius-based thermal tissue damage simulator, both run in MATLAB. The kernel for both of these solvers is written in parallelized C and implemented as MATLAB MEX functions, combining the speed of C with the familiarity and versatility of MATLAB. We compare the RTE solver to Steven Jacques' mcxyz, which it is inspired by, and present example results generated by the thermal model. MCmatlab is easy to install and use and can be used by students and experienced researchers alike for simulating tissue light propagation and, optionally, thermal damage.

A Handheld Real-Time Photoacoustic Imaging System for Animal Neurological Disease Models: From Simulation to Realization

This article provides a guide to design and build a handheld, real-time photoacoustic (PA) imaging system from simulation to realization for animal neurological disease models. A pulsed laser and array-based ultrasound (US) platform were utilized to develop the system for evaluating vascular functions in rats with focal ischemia or subcutaneous tumors. To optimize the laser light delivery, finite element (FE)-based simulation models were developed to provide information regarding light propagation and PA wave generation in soft tissues. Besides, simulations were also conducted to evaluate the ideal imaging resolution of the US system. As a result, a PA C-scan image of a designed phantom in 1% Lipofundin was reconstructed with depth information. Performance of the handheld PA system was tested in an animal ischemia model, which revealed that cerebral blood volume (CBV) changes at the cortical surface could be monitored immediately after ischemia induction. Another experiment on subcutaneous tumors showed the anomalous distribution of the total hemoglobin concentration (HbT) and oxygen saturation (SO₂), while 3D and maximum intensity projection (MIP) PA images of the subcutaneous tumors are also presented in this article. Overall, this system shows promise for monitoring disease progression in vascular functional impairments.

In vivo characterization of light scattering properties of human skin in the 475- to 850-nm wavelength range in a Swedish cohort

We have determined in vivo optical scattering properties of normal human skin in 1734 subjects, mostly with fair skin type, within the Swedish CArdioPulmonary bioImage Study. The measurements were performed with a noninvasive system, integrating spatially resolved diffuse reflectance spectroscopy and laser Doppler flowmetry. Data were analyzed with an inverse Monte Carlo algorithm, accounting for both scattering, geometrical, and absorbing properties of the tissue. The reduced scattering coefficient was found to decrease from 3.16 ± 0.72 to 1.13 ± 0.27 mm-1 (mean ± SD) in the 475- to 850-nm wavelength range. There was a negative correlation between the reduced scattering coefficient and age, and a significant difference between men and women in the reduced scattering coefficient as well as in the fraction of small scattering particles. This large study on tissue scattering with mean values and normal variation can serve as a reference when designing diagnostic techniques or when evaluating the effect of therapeutic optical systems.

Absorption by water increases fluorescence image contrast of biological tissue in the shortwave infrared

Recent technology developments have expanded the wavelength window for biological fluorescence imaging into the shortwave infrared. We show here a mechanistic understanding of how drastic changes in fluorescence imaging contrast can arise from slight changes of imaging wavelength in the shortwave infrared. We demonstrate, in 3D tissue phantoms and in vivo in mice, that light absorption by water within biological tissue increases image contrast due to attenuation of background and highly scattered light. Wavelengths of strong tissue absorption have conventionally been avoided in fluorescence imaging to maximize photon penetration depth and photon collection, yet we demonstrate that imaging at the peak absorbance of water (near 1,450 nm) results in the highest image contrast in the shortwave infrared. Furthermore, we show, through microscopy of highly labeled ex vivo biological tissue, that the contrast improvement from water absorption enables resolution of deeper structures, resulting in a higher imaging penetration depth. We then illustrate these findings in a theoretical model. Our results suggest that the wavelength-dependent absorptivity of water is the dominant optical property contributing to image contrast, and is therefore crucial for determining the optimal imaging window in the infrared.

Full spectral optical modeling of quantum-dot-converted elements for light-emitting diodes considering reabsorption and reemission effect

Quantum dots (QDs) have attracted significant attention in light-emitting diode (LED) illumination and display applications, owing to their high quantum yield and unique spectral properties. However, an effective optical model of quantum-dot-converted elements (QDCEs) for (LEDs) that entirely considers the reabsorption and reemission effect is lacking. This suppresses the design of QDCE structures and further investigation of light-extraction/conversion mechanisms in QDCEs. In this paper, we proposed a full spectral optical modeling method for QDCEs packaged in LEDs, entirely considering the reabsorption and reemission effect, and its results are compared with traditional models without reabsorption or reemission. The comparisons indicate that the QDCE absorption loss of QD emission light is a major factor decreasing the radiant efficacy of LEDs, which should be considered when designing QDCE structures. According to the measurements of fabricated LEDs, only calculation results that entirely consider reabsorption and reemission show good agreement with experimental radiant efficacy, spectra, and peak wavelength at the same down-conversion efficiency. Consequently, it is highly expected that QDCE will be modeled considering the reabsorption and reemission events. This study provides a simple and effective modeling method for QDCEs, which shows great potential for their structure designs and fundamental investigations.

Measurements of the optical coefficients of the protoporphyrin IX endogenously producing yeast-based model in the visible and NIR

Models mimicking the endogenous production of protoporphyrin IX (PpIX), as well as its fluorescence, are of high interest for applied and fundamental studies in the fields of cancer detection by fluorescence imaging, photodynamic therapy (PDT), and photobiomodulation (PBM). Here, we present and describe optical properties of the yeast-based models able to produce PpIX endogenously after the administration of 5-aminolevulinic acid (ALA) and/or 2,2'-bipyridyl. As their optical properties have an important impact on the spatial distribution of the fluence rate in these liquid models, their absorption and reduced scattering coefficients were determined to be between 400 and 808 nm for two yeast solutions previously described by our group. These coefficients were derived from measurements of the total reflectance and light penetration depth using a dedicated Monte Carlo simulation. We observed that absorption and scattering coefficients were smaller than those of soft tissues at all wavelengths. This work will enable the production of a low-cost optical phantom loaded with appropriate amounts of light-absorbing and -scattering particles to mimic tumors containing PpIX, offering a useful tool to optimize the spectral and radiometric design of certain cancer photodetection setups.

Parallelized Monte Carlo software to efficiently simulate the light propagation in arbitrarily shaped objects and aligned scattering media

A GPU-based Monte Carlo software (MCtet) was developed to calculate the light propagation in arbitrarily shaped objects, like a human tooth, represented by a tetrahedral mesh. A unique feature of MCtet is a concept to realize different kinds of light-sources illuminating the complex-shaped surface of an object, for which no preprocessing step is needed. With this concept, it is also possible to consider photons leaving a turbid media and reentering again in case of a concave object. The correct implementation was shown by comparison with five other Monte Carlo software packages. A hundredfold acceleration compared with central processing units-based programs was found. MCtet can simulate anisotropic light propagation, e.g., by accounting for scattering at cylindrical structures. The important influence of the anisotropic light propagation, caused, e.g., by the tubules in human dentin, is shown for the transmission spectrum through a tooth. It was found that the sensitivity to a change in the oxygen saturation inside the pulp for transmission spectra is much larger if the tubules are considered. Another "light guiding" effect based on a combination of a low scattering and a high refractive index in enamel is described.

Study on Scattering and Absorption Properties of Quantum-Dot-Converted Elements for Light-Emitting Diodes Using Finite-Difference Time-Domain Method

CdSe/ZnS quantum-dot-converted elements (QDCEs) are good candidates for substituting rare-earth phosphor-converted elements (PCEs) in white light-emitting diodes (LEDs); however, studies on their scattering and absorption properties are scarce, suppressing further increment in the optical and thermal performance of quantum-dot-converted LEDs. Therefore, we introduce the finite-difference time-domain (FDTD) method to achieve the critical optical parameters of QDCEs when used in white LEDs; their scattering cross-section (coefficient), absorption cross-section (coefficient), and scattering phase distributions are presented and compared with those of traditional YAG phosphor-converted elements (PCEs) at varying particle size and concentration. At a commonly used concentration (<50 mg/cm3), QDCEs exhibit stronger absorption (tens of millimeters, even for green-to-red-wavelength light) and weaker scattering (<1 mm−1) compared to PCEs; the reabsorption, total internal reflection, angular uniformity, and thermal quenching would be more significant concerns for QDCEs. Therefore, the unique scattering and absorption properties of QDCEs should be considered when used in white LEDs. Furthermore, knowledge of these important optical parameters is helpful for beginning a theoretical study on quantum-dot-converted LEDs according to the ray tracing method.

Subcutaneous veins depth measurement using diffuse reflectance images

Intravenous (IV) procedures are often difficult due to the poor visualization of subcutaneous veins. Because existing vein locators lack the ability to assess depth, and also because mis-punctures and poor vascular access remain problematic, we propose an imaging system that employs diffuse reflectance images at three isosbestic wavelengths to measure both the depth and thickness of subcutaneous veins. This paper describes the proposed system as well as proof-of-principle experimental demonstrations. We initially introduce the working principle and structure of the system. All measurements were based on the Monte Carlo (MC) method and accomplished by referring an optical density (OD) ratio to a multi-layer diffuse reflectance model. Results were all validated by comparative ultrasound measurements. Experimental trials included 11 volunteers who were subjected to both ultrasound measurements and the proposed optical process to validate the system's applicability. However, the unreliability of the "thickness" measurement of the vein may be due to the fact that the veins have collapsible walls - so excess pressure by the transducer will give a false thickness.

Optimization of dual-wavelength intravascular photoacoustic imaging of atherosclerotic plaques using Monte Carlo optical modeling

Coronary heart disease (the presence of coronary atherosclerotic plaques) is a significant health problem in the industrialized world. A clinical method to accurately visualize and characterize atherosclerotic plaques is needed. Intravascular photoacoustic (IVPA) imaging is being developed to fill this role, but questions remain regarding optimal imaging wavelengths. We utilized a Monte Carlo optical model to simulate IVPA excitation in coronary tissues, identifying optimal wavelengths for plaque characterization. Near-infrared wavelengths (≤1800  nm) were simulated, and single- and dual-wavelength data were analyzed for accuracy of plaque characterization. Results indicate light penetration is best in the range of 1050 to 1370 nm, where 5% residual fluence can be achieved at clinically relevant depths of ≥2  mm in arteries. Across the arterial wall, fluence may vary by over 10-fold, confounding plaque characterization. For single-wavelength results, plaque segmentation accuracy peaked at 1210 and 1720 nm, though correlation was poor (<0.13). Dual-wavelength analysis proved promising, with 1210 nm as the most successful primary wavelength (≈1.0). Results suggest that, without flushing the luminal blood, a primary and secondary wavelength near 1210 and 1350 nm, respectively, may offer the best implementation of dual-wavelength IVPA imaging. These findings could guide the development of a cost-effective clinical system by highlighting optimal wavelengths and improving plaque characterization.

Hyperspectral data processing improves PpIX contrast during fluorescence guided surgery of human brain tumors

Fluorescence guided surgery (FGS) using aminolevulinic-acid (ALA) induced protoporphyrin IX (PpIX) provides intraoperative visual contrast between normal and malignant tissue during resection of high grade gliomas. However, maps of the PpIX biodistribution within the surgical field based on either visual perception or the raw fluorescence emissions can be masked by background signals or distorted by variations in tissue optical properties. This study evaluates the impact of algorithmic processing of hyperspectral imaging acquisitions on the sensitivity and contrast of PpIX maps. Measurements in tissue-simulating phantoms showed that (I) spectral fitting enhanced PpIX sensitivity compared with visible or integrated fluorescence, (II) confidence-filtering automatically determined the lower limit of detection based on the strength of the PpIX spectral signature in the collected emission spectrum (0.014–0.041 μg/ml in phantoms), and (III) optical-property corrected PpIX estimates were more highly correlated with independent probe measurements (r = 0.98) than with spectral fitting alone (r = 0.91) or integrated fluorescence (r = 0.82). Application to in vivo case examples from clinical neurosurgeries revealed changes to the localization and contrast of PpIX maps, making concentrations accessible that were not visually apparent. Adoption of these methods has the potential to maintain sensitive and accurate visualization of PpIX contrast over the course of surgery.

Evaluation of light detector surface area for functional Near Infrared Spectroscopy

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neuroimaging technique that utilizes near infrared light to detect cortical concentration changes of oxy-hemoglobin and deoxy-hemoglobin non-invasively. Using light sources and detectors over the scalp, multi-wavelength light intensities are recorded as time series and converted to concentration changes of hemoglobin via modified Beer-Lambert law. Here, we describe a potential source for systematic error in the calculation of hemoglobin changes and light intensity measurements. Previous system characterization and analysis studies looked into various fNIRS parameters such as type of light source, number and selection of wavelengths, distance between light source and detector. In this study, we have analyzed the contribution of light detector surface area to the overall outcome. Results from Monte Carlo based digital phantoms indicated that selection of detector area is a critical system parameter in minimizing the error in concentration calculations. The findings here can guide the design of future fNIRS sensors.

A fast forward solver of fluorescence diffuse optical tomography based on the lattice Boltzmann method

Fluorescence diffuse optical tomography (FDOT) is a new molecular imaging technology, which uses near-infrared light to excite the fluorophore in tissues. According to the measurements detected on the surface of imaged object, the fluorescent quantum yield as well as lifetime of the fluorescence can be reconstructed. However, the reconstruction of FDOT remains a challenging problem because the conventional forward solvers are time consuming. Thus, a forward model solver that would enable the fast imaging is necessary. This paper describes a new forward solver to simulate the propagation of photons in tissues based on the lattice Boltzmann method (LBM). This is accomplished by propagation photons in tissues guided by the LBM. To evaluate the performance of the proposed LBM, based on the numerical simulation, we compared the light distribution generated by the LBM with the diffusion equation implemented by COMSOL in four different cases. The experimental results indicate that compared to diffusion equation, the LBM can reduce the computation time for the forward solver of FDOT while preserving the similar accuracy.

Review of mesoscopic optical tomography for depth-resolved imaging of hemodynamic changes and neural activities

Understanding the functional wiring of neural circuits and their patterns of activation following sensory stimulations is a fundamental task in the field of neuroscience. Furthermore, charting the activity patterns is undoubtedly important to elucidate how neural networks operate in the living brain. However, optical imaging must overcome the effects of light scattering in the tissue, which limit the light penetration depth and affect both the imaging quantitation and sensitivity. Laminar optical tomography (LOT) is a three-dimensional (3-D) in-vivo optical imaging technique that can be used for functional imaging. LOT can achieve both a resolution of 100 to [Formula: see text] and a penetration depth of 2 to 3 mm based either on absorption or fluorescence contrast, as well as large field-of-view and high acquisition speed. These advantages make LOT suitable for 3-D depth-resolved functional imaging of the neural functions in the brain and spinal cords. We review the basic principles and instrumentations of representative LOT systems, followed by recent applications of LOT on 3-D imaging of neural activities in the rat forepaw stimulation model and mouse whisker-barrel system.

Single-shot real-time video recording of a photonic Mach cone induced by a scattered light pulse

Ultrafast video recording of spatiotemporal light distribution in a scattering medium has a significant impact in biomedicine. Although many simulation tools have been implemented to model light propagation in scattering media, existing experimental instruments still lack sufficient imaging speed to record transient light-scattering events in real time. We report single-shot ultrafast video recording of a light-induced photonic Mach cone propagating in an engineered scattering plate assembly. This dynamic light-scattering event was captured in a single camera exposure by lossless-encoding compressed ultrafast photography at 100 billion frames per second. Our experimental results are in excellent agreement with theoretical predictions by time-resolved Monte Carlo simulation. This technology holds great promise for next-generation biomedical imaging instrumentation.

FEF inactivation with improved optogenetic methods

Optogenetic methods have been highly effective for suppressing neural activity and modulating behavior in rodents, but effects have been much smaller in primates, which have much larger brains. Here, we present a suite of technologies to use optogenetics effectively in primates and apply these tools to a classic question in oculomotor control. First, we measured light absorption and heat propagation in vivo, optimized the conditions for using the red-light-shifted halorhodopsin Jaws in primates, and developed a large-volume illuminator to maximize light delivery with minimal heating and tissue displacement. Together, these advances allowed for nearly universal neuronal inactivation across more than 10 mm³ of the cortex. Using these tools, we demonstrated large behavioral changes (i.e., up to several fold increases in error rate) with relatively low light power densities (≤100 mW/mm²) in the frontal eye field (FEF). Pharmacological inactivation studies have shown that the FEF is critical for executing saccades to remembered locations. FEF neurons increase their firing rate during the three epochs of the memory-guided saccade task: visual stimulus presentation, the delay interval, and motor preparation. It is unclear from earlier work, however, whether FEF activity during each epoch is necessary for memory-guided saccade execution. By harnessing the temporal specificity of optogenetics, we found that FEF contributes to memory-guided eye movements during every epoch of the memory-guided saccade task (the visual, delay, and motor periods).

Color uniformity enhancement for COB WLEDs using a remote phosphor film with two freeform surfaces

The color uniformity (CU) of chip-on-board (COB) white light emitting diodes (WLEDs) has been improved by using remote phosphor films with two freeform surfaces (TFS-RPFs). The finite-difference time-domain (FDTD), Monte Carlo ray-tracing, and color-thickness feedback (CTFB) methods were used to design the TFS-RPFs: the blue light distribution of COB WLEDs is greatly affected by the angular thickness distribution of TFS-RPFs, and a high CU can be achieved iteratively. The directional inconsistency of incident and emergent blue light, scattering effect of TFS-RPFs, and illumination characteristics of the COB source were also investigated. COB WLEDs containing optimized TFS-RPFs achieved high CU with a decrease of 26.2% in maximum CCT deviation; thus, TFS-RPFs can improve the CU of COB WLEDs.

Theoretical optimal modulation frequencies for scattering parameter estimation and ballistic photon filtering in diffusing media

The efficiency of using intensity modulated light for the estimation of scattering properties of a turbid medium and for ballistic photon discrimination is theoretically quantified in this article. Using the diffusion model for modulated photon transport and considering a noisy quadrature demodulation scheme, the minimum-variance bounds on estimation of parameters of interest are analytically derived and analyzed. The existence of a variance-minimizing optimal modulation frequency is shown and its evolution with the properties of the intervening medium is derived and studied. Furthermore, a metric is defined to quantify the efficiency of ballistic photon filtering which may be sought when imaging through turbid media. The analytical derivation of this metric shows that the minimum modulation frequency required to attain significant ballistic discrimination depends only on the reduced scattering coefficient of the medium in a linear fashion for a highly scattering medium.

Mathematical model to interpret localized reflectance spectra measured in the presence of a strong fluorescence marker

Quantification of multiple fluorescence markers during neurosurgery has the potential to provide complementary contrast mechanisms between normal and malignant tissues, and one potential combination involves fluorescein sodium (FS) and aminolevulinic acid-induced protoporphyrin IX (PpIX). We focus on the interpretation of reflectance spectra containing contributions from elastically scattered (reflected) photons as well as fluorescence emissions from a strong fluorophore (i.e., FS). A model-based approach to extract μa and μ′s in the presence of FS emission is validated in optical phantoms constructed with Intralipid (1% to 2% lipid) and whole blood (1% to 3% volume fraction), over a wide range of FS concentrations (0 to 1000  μg/ml 1000  μg/ml ). The results show that modeling reflectance as a combination of elastically scattered light and attenuation-corrected FS-based emission yielded more accurate tissue parameter estimates when compared with a nonmodified reflectance model, with reduced maximum errors for blood volume (22% versus 90%), microvascular saturation (21% versus 100%), and μs′ (13% versus 207%). Additionally, quantitative PpIX fluorescence sampled in the same phantom as FS showed significant differences depending on the reflectance model used to estimate optical properties (i.e., maximum error 29% versus 86%). These data represent a first step toward using quantitative optical spectroscopy to guide surgeries through simultaneous assessment of FS and PpIX.

Adaptively Alternative Light-Transport-Model-Based Three-Dimensional Optical Imaging for Longitudinal and Quantitative Monitoring of Gastric Cancer in Live Animal

OBJECTIVE

The existence of void regions and the complexity of structural heterogeneity and optical specificity are two challenges encountered in three-dimensional (3-D) optical imaging of in situ gastric cancer. An adaptively alternative light-transport-model-based 3-D optical imaging method was developed for addressing these challenges.

METHODS

In this newly developed imaging method, both the anatomical structure and optical properties are considered as a priori information, which makes the full use of the specificity of the biological tissues and improves both the quality and efficiency of the reconstructed images. The soul of the adaptively alternative technique is technique is configured to first classify the tissues based on the anatomical structure and optical properties and then select various equations to specifically characterize the light transport in different categories of tissues.

RESULTS

A series of rigorous simulations were conducted to demonstrate the performance of the hybrid light transport model, and the quality of the reconstructed images was then evaluated using a digital-mouse-based gastric cancer mimicing simulation, which showed that the localization error was less than 1 mm and the quantification error was approximately 10%. Finally, the applicability of the proposed method for in vivo imaging of gastric cancer was illustrated using groups of gastric cancer-bearing mice, which demonstrated the ability of the proposed method for longitudinal and quantitative monitoring of gastric cancer.

CONCLUSION

The developed method offers improved probability and great potential in the applications of earlier detecting in situ gastric cancer and longitudinal and quantitative observation of its development.

An extended analytical approach for diffuse optical imaging

In this work, we introduce an analytical method to solve the diffusion equation in a cylindrical geometry. This method is based on an integral approach to derive the Green's function for specific boundary conditions. Using our approach, we obtain comprehensive analytical solutions with the Robin boundary condition for diffuse optical imaging in both two and three dimensions. The solutions are expressed in terms of the optical properties of tissue and the amplitude and position of the light source. Our method not only works well inside the tissue but provides very accurate results near the tissue boundaries as well. The results obtained by our method are first compared with those obtained by a conventional analytical method then validated using numerical simulations. Our new analytical method allows not only implementation of any boundary condition for a specific problem but also fast simulation of light propagation making it very suitable for iterative image reconstruction algorithms.