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Assessment of optical properties and Monte-Carlo based simulation of light propagation in citrus infected by Penicillium italicum

This original work investigated the optical properties and Monte-Carlo (MC) based simulation of light propagation in the flavedo of Nanfeng tangerine (NF) and Gannan navel orange (GN) infected by Penicillium italicum. The increase of absorption coefficient (μa) at around 482 nm and the decrease at around 675 nm were both observed in infected NF and GN during storage, indicating the accumulation of carotenoids and loss of chlorophyll. Particularly, the μa in NF varied more intensively than GN, but the limited differences of reduced scattering coefficient (μs') were detected while postharvest infection. Besides, MC simulation of light propagation indicated that the photon packets weight and penetration depth at 482 nm in NF were reduced more than in GN flavedo, while there were almost no changes at the relatively low absorption wavelength of 926 nm. The simulated absorption energy at 482 nm in NF and GN presented more changes than those at 675 nm during infection, thus could provide better detection of citrus diseases. Furthermore, PLS-DA models can discriminate healthy and infected citrus, with the accuracy of 95.24 % for NF and 98.67 % for GN, respectively. Consequently, these results can provide theoretical fundamentals to improve modelling prediction robustness and accuracy.

Deep learning-enabled high-speed, multi-parameter diffuse optical tomography

Significance

Frequency-domain diffuse optical tomography (FD-DOT) could enhance clinical breast tumor characterization. However, conventional diffuse optical tomography (DOT) image reconstruction algorithms require case-by-case expert tuning and are too computationally intensive to provide feedback during a scan. Deep learning (DL) algorithms front-load computational and tuning costs, enabling high-speed, high-fidelity FD-DOT.

Aim

We aim to demonstrate a simultaneous reconstruction of three-dimensional absorption and reduced scattering coefficients using DL-FD-DOT, with a view toward real-time imaging with a handheld probe.

Approach

A DL model was trained to solve the DOT inverse problem using a realistically simulated FD-DOT dataset emulating a handheld probe for human breast imaging and tested using both synthetic and experimental data.

Results

Over a test set of 300 simulated tissue phantoms for absorption and scattering reconstructions, the DL-DOT model reduced the root mean square error by 12 % ± 40 % and 23 % ± 40 % , increased the spatial similarity by 17 % ± 17 % and 9 % ± 15 % , increased the anomaly contrast accuracy by 9 % ± 9 % (μ a ), and reduced the crosstalk by 5 % ± 18 % and 7 % ± 11 % , respectively, compared with model-based tomography. The average reconstruction time was reduced from 3.8 min to 0.02 s for a single reconstruction. The model was successfully verified using two tumor-emulating optical phantoms.

Conclusions

There is clinical potential for real-time functional imaging of human breast tissue using DL and FD-DOT.

scatterBrains: an open database of human head models and companion optode locations for realistic Monte Carlo photon simulations

Significance

Monte Carlo (MC) simulations are currently the gold standard in the near-infrared and diffuse correlation spectroscopy (NIRS/DCS) communities for generating light transport paths through tissue. However, realistic and diverse models that capture complex tissue layers are not easily available to all; moreover, manually placing optodes on such models can be tedious and time consuming. Such limitations may hinder the adoption of representative models for basic simulations and the use of these models for large-scale simulations, e.g., for training machine learning algorithms.

Aim

We aim to provide the NIRS/DCS communities with an open-source, user-friendly database of morphologically and optically realistic head models, as well as a succinct software pipeline to prepare these models for mesh-based Monte Carlo simulations of light transport.

Approach

Sixteen anatomical models were created from segmented T1-weighted magnetic resonance imaging (MRI) head scans and converted to tetrahedral mesh volumes. Approximately 800 companion scalp surface locations were distributed on each model, comprising full head coverage. A pipeline was created to place custom source and optical detectors at each location, and guidance is provided on how to use these parameters to set up MC simulations.

Results

The models, head surface locations, and all associated code are freely available under the scatterBrains project on Github.

Conclusions

The NIRS/DCS community benefits from having shared resources for conducting MC simulations on realistic head geometries. We hope this will make MRI-based head models and virtual optode placement easily accessible to all. Contributions to the database are welcome and encouraged.

Effects of atlas-based anatomy on modelled light transport in the neonatal head

Objective.Diffuse optical tomography (DOT) provides a relatively convenient method for imaging haemodynamic changes related to neuronal activity on the cerebral cortex. Due to practical challenges in obtaining anatomical images of neonates, an anatomical framework is often created from an age-appropriate atlas model, which is individualized to the subject based on measurements of the head geometry. This work studies the approximation error arising from using an atlas instead of the neonate's own anatomical model.Approach.We consider numerical simulations of frequency-domain (FD) DOT using two approaches, Monte Carlo simulations and diffusion approximation via finite element method, and observe the variation in (1) the logarithm of amplitude and phase shift measurements, and (2) the corresponding inner head sensitivities (Jacobians), due to varying segmented anatomy. Varying segmentations are sampled by registering 165 atlas models from a neonatal database to the head geometry of one individual selected as the reference model. Prior to the registration, we refine the segmentation of the cerebrospinal fluid (CSF) by separating the CSF into two physiologically plausible layers.Main results.In absolute measurements, a considerable change in the grey matter or extracerebral tissue absorption coefficient was found detectable over the anatomical variation. In difference measurements, a small local 10%-increase in brain absorption was clearly detectable in the simulated measurements over the approximation error in the Jacobians, despite the wide range of brain maturation among the registered models.Significance.Individual-level atlas models could potentially be selected within several weeks in gestational age in DOT difference imaging, if an exactly age-appropriate atlas is not available. The approximation error method could potentially be implemented to improve the accuracy of atlas-based imaging. The presented CSF segmentation algorithm could be useful also in other model-based imaging modalities. The computation of FD Jacobians is now available in the widely-used Monte Carlo eXtreme software.

Analysis of relative error in perturbation Monte Carlo simulations of radiative transport

Significance

Perturbation and differential Monte Carlo (pMC/dMC) methods, used in conjunction with nonlinear optimization methods, have been successfully applied to solve inverse problems in diffuse optics. Application of pMC to systems over a large range of optical properties requires optimal "placement" of baseline conventional Monte Carlo (cMC) simulations to minimize the pMC variance. The inability to predict the growth in pMC solution uncertainty with perturbation size limits the application of pMC, especially for multispectral datasets where the variation of optical properties can be substantial.

Aim

We aim to predict the variation of pMC variance with perturbation size without explicit computation of perturbed photon weights. Our proposed method can be used to determine the range of optical properties over which pMC predictions provide sufficient accuracy. This method can be used to specify the optical properties for the reference cMC simulations that pMC utilizes to provide accurate predictions over a desired optical property range.

Approach

We utilize a conventional error propagation methodology to calculate changes in pMC relative error for Monte Carlo simulations. We demonstrate this methodology for spatially resolved diffuse reflectance measurements with ±20% scattering perturbations. We examine the performance of our method for reference simulations spanning a broad range of optical properties relevant for diffuse optical imaging of biological tissues. Our predictions are computed using the variance, covariance, and skewness of the photon weight, path length, and collision distributions generated by the reference simulation.

Results

We find that our methodology performs best when used in conjunction with reference cMC simulations that utilize Russian Roulette (RR) method. Specifically, we demonstrate that for a proximal detector placed immediately adjacent to the source, we can estimate the pMC relative error within 5% of the true value for scattering perturbations in the range of [ - 15 % , + 20 % ] . For a distal detector placed at ∼ 3 transport mean free paths relative to the source, our method provides relative error estimates within 20% for scattering perturbations in the range of [ - 8 % , + 15 % ] . Moreover, reference simulations performed at lower ( μ s ' / μ a ) values showed better performance for both proximal and distal detectors.

Conclusions

These findings indicate that reference simulations utilizing continuous absorption weighting (CAW) with the Russian Roulette method and executed using optical properties with a low ( μ s ' / μ a ) ratio spanning the desired range of μ s values, are highly advantageous for the deployment of pMC to obtain radiative transport estimates over a wide range of optical properties.

Reconstruction of light absorption changes in the human head using analytically computed photon partial pathlengths in layered media

Functional near infrared spectroscopy has been used in recent decades to sense and quantify changes in hemoglobin concentrations in the human brain. This noninvasive technique can deliver useful information concerning brain cortex activation associated with different motor/cognitive tasks or external stimuli. This is usually accomplished by considering the human head as a homogeneous medium; however, this approach does not explicitly take into account the detailed layered structure of the head, and thus, extracerebral signals can mask those arising at the cortex level. This work improves this situation by considering layered models of the human head during reconstruction of the absorption changes in layered media. To this end, analytically calculated mean partial pathlengths of photons are used, which guarantees fast and simple implementation in real-time applications. Results obtained from synthetic data generated by Monte Carlo simulations in two- and four-layered turbid media suggest that a layered description of the human head greatly outperforms typical homogeneous reconstructions, with errors, in the first case, bounded up to ∼20% maximum, while in the second case, the error is usually larger than 75%. Experimental measurements on dynamic phantoms support this conclusion.

A Monte Carlo simulation for Moving Light Source in Intracavity PDT

We developed a simulation method for modeling the light fluence delivery in intracavity Photodynamic Therapy (icav-PDT) for pleural lung cancer using a moving light source. Due to the large surface area of the pleural lung cavity, the light source needs to be moved to deliver a uniform dose around the entire cavity. While multiple fixed detectors are used for dosimetry at a few locations, an accurate simulation of light fluence and fluence rate is still needed for the rest of the cavity. We extended an existing Monte Carlo (MC) based light propagation solver to support moving light sources by densely sampling the continuous light source trajectory and assigning the proper number of photon packages launched along the way. The performance of Simphotek GPU CUDA-based implementation of the method - PEDSy-MC - has been demonstrated on a life-size lung-shaped phantom, custom printed for testing icav-PDT navigation system at the Perlman School of Medicine (PSM) - calculations completed under a minute (for some cases) and within minutes have been achieved. We demonstrate results within a 5% error of the analytic solution for multiple detectors in the phantom. PEDSy-MC is accompanied by a dose-cavity visualization tool that allows real-time inspection of dose values of the treated cavity in 2D and 3D, which will be expanded to ongoing clinical trials at PSM. PSM has developed a technology to measure 8-detectors in a pleural cavity phantom using Photofrin-mediated PDT that has been used during validation.

Excitation-based fully connected network for precise NIR-II fluorescence molecular tomography

Fluorescence molecular tomography (FMT) is a novel imaging modality to obtain fluorescence biomarkers' three-dimensional (3D) distribution. However, the simplified mathematical model and complicated inverse problem limit it to achieving precise results. In this study, the second near-infrared (NIR-II) fluorescence imaging was adopted to mitigate tissue scattering and reduce noise interference. An excitation-based fully connected network was proposed to model the inverse process of NIR-II photon propagation and directly obtain the 3D distribution of the light source. An excitation block was embedded in the network allowing it to autonomously pay more attention to neurons related to the light source. The barycenter error was added to the loss function to improve the localization accuracy of the light source. Both numerical simulation and in vivo experiments showed the superiority of the novel NIR-II FMT reconstruction strategy over the baseline methods. This strategy was expected to facilitate the application of machine learning in biomedical research.

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.

Non-invasive optical monitoring of human lungs: Monte Carlo modeling of photon migration in Visible Chinese Human and an experimental test on a human

The human lung was quantified and visualized by photon transport in this paper. A Monte Carlo (MC) simulation of voxelized media was used with the visible Chinese human (VCH). This study theoretically explored the feasibility of non-invasive optical detection of pulmonary hemodynamics, and investigated the optimal location of the light source in the lung photon migration and optimized the source-detector distance. The light fluence intensity showed that the photon penetration depth was 6-8.4 mm in the human lung. The optimal distance from the light source to the detector was 2.7-2.9 cm, but the optimal distance of the superior lobe of right lung was 3.3-3.5 cm. We then conducted experiments on diffuse light reflectance using NIRS on 14 volunteers. These measurements agree well with the simulation results. All the results demonstrated the great potential of non-invasive monitoring of pulmonary hemodynamics and contribute to the study of human lungs in the biomedical optics community.

Photodynamic therapy in a pleural cavity using monte carlo simulations with 2D/3D Graphical Visualization

Cancer therapy using Photodynamic Therapy (PDT) has been investigated for some time [1,2] and now it is a growing area of interest in clinical trials [3]. Monte Carlo (MC) simulations were used for early laboratory studies [4,5] for analysis in PDT. Various improvements in the MC method have advanced the field in recent years.

Deep-learning informed Kalman filtering for priori-free and real-time hemodynamics extraction in functional near-infrared spectroscopy

Separation of the physiological interferences and the neural hemodynamics has been a vitally important task in the realistic implementation of functional near-infrared spectroscopy (fNIRS). Although many efforts have been devoted, the established solutions to this issue additionally rely on priori information on the interferences and activation responses, such as time-frequency characteristics and spatial patterns, etc., also hindering the realization of real-time. To tackle the adversity, we herein propose a novel priori-free scheme for real-time physiological interference suppression. This method combines the robustness of deep-leaning-based interference characterization and adaptivity of Kalman filtering: a long short-term memory (LSTM) network is trained with the time-courses of the absorption perturbation baseline for interferences profiling, and successively, a Kalman filtering process is applied with reference to the noise prediction for real-time activation extraction. The proposed method is validated using both simulated dynamic data and in-vivo experiments, showing the comprehensively improved performance and promisingly appended superiority achieved in the purely data-driven way.

Special Section Guest Editorial: Introduction to the Special Section Celebrating 30 years of Open Source Monte Carlo Codes in Biomedical Optics

The editorial introduces the JBO Special Section Celebrating 30 Years of Open Source Monte Carlo Codes in Biomedical Optics for Volume 27, Issue 8.

Noninvasive hemoglobin sensing and imaging: optical tools for disease diagnosis

SIGNIFICANCE

Measurement and imaging of hemoglobin oxygenation are used extensively in the detection and diagnosis of disease; however, the applied instruments vary widely in their depth of imaging, spatiotemporal resolution, sensitivity, accuracy, complexity, physical size, and cost. The wide variation in available instrumentation can make it challenging for end users to select the appropriate tools for their application and to understand the relative limitations of different methods.

AIM

We aim to provide a systematic overview of the field of hemoglobin imaging and sensing.

APPROACH

We reviewed the sensing and imaging methods used to analyze hemoglobin oxygenation, including pulse oximetry, spectral reflectance imaging, diffuse optical imaging, spectroscopic optical coherence tomography, photoacoustic imaging, and diffuse correlation spectroscopy.

RESULTS

We compared and contrasted the ability of different methods to determine hemoglobin biomarkers such as oxygenation while considering factors that influence their practical application.

CONCLUSIONS

We highlight key limitations in the current state-of-the-art and make suggestions for routes to advance the clinical use and interpretation of hemoglobin oxygenation information.

Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

BlenderPhotonics: an integrated open-source software environment for three-dimensional meshing and photon simulations in complex tissues

SIGNIFICANCE

Rapid advances in biophotonics techniques require quantitative, model-based computational approaches to obtain functional and structural information from increasingly complex and multiscaled anatomies. The lack of efficient tools to accurately model tissue structures and subsequently perform quantitative multiphysics modeling greatly impedes the clinical translation of these modalities.

AIM

Although the mesh-based Monte Carlo (MMC) method expands our capabilities in simulating complex tissues using tetrahedral meshes, the generation of such domains often requires specialized meshing tools, such as Iso2Mesh. Creating a simplified and intuitive interface for tissue anatomical modeling and optical simulations is essential toward making these advanced modeling techniques broadly accessible to the user community.

APPROACH

We responded to the above challenge by combining the powerful, open-source three-dimensional (3D) modeling software, Blender, with state-of-the-art 3D mesh generation and MC simulation tools, utilizing the interactive graphical user interface in Blender as the front-end to allow users to create complex tissue mesh models and subsequently launch MMC light simulations.

RESULTS

Here, we present a tutorial to our Python-based Blender add-on-BlenderPhotonics-to interface with Iso2Mesh and MMC, which allows users to create, configure and refine complex simulation domains and run hardware-accelerated 3D light simulations with only a few clicks. We provide a comprehensive introduction to this tool and walk readers through five examples, ranging from simple shapes to sophisticated realistic tissue models.

CONCLUSIONS

BlenderPhotonics is user friendly and open source, and it leverages the vastly rich ecosystem of Blender. It wraps advanced modeling capabilities within an easy-to-use and interactive interface. The latest software can be downloaded at http://mcx.space/bp.

Scalable and accessible personalized photodynamic therapy optimization with FullMonte and PDT-SPACE

SIGNIFICANCE

Open-source software packages have been extensively used in the past three decades in medical imaging and diagnostics, aiming to study the feasibility of the application ex vivo. Unfortunately, most of the existing open-source tools require some software engineering background to install the prerequisite libraries, choose a suitable computational platform, and combine several software tools to address different applications.

AIM

To facilitate the use of open-source software in medical applications, enabling computational studies of treatment outcomes prior to the complex in-vivo setting.

APPROACH

FullMonteWeb, an open-source, user-friendly web-based software with a graphical user interface for interstitial photodynamic therapy (iPDT) modeling, visualization, and optimization, is introduced. The software can perform Monte Carlo simulations of light propagation in biological tissues, along with iPDT plan optimization. FullMonteWeb installs and runs the required software and libraries on Amazon Web Services (AWS), allowing scalable computing without complex set up.

RESULTS

FullMonteWeb allows simulation of large and small problems on the most appropriate compute hardware, enabling cost improvements of 10  ×   versus always running on a single platform. Case studies in optical property estimation and diffuser placement optimization highlight FullMonteWeb's versatility.

CONCLUSION

The FullMonte open source suite enables easier and more cost-effective in-silico studies for iPDT.