Inverse Monte-Carlo Research Articles

Artificial neural networks trained on simulated multispectral data for real-time imaging of skin microcirculatory blood oxygen saturation.

Imaging blood oxygen saturation ( ) in the skin can be of clinical value when studying ischemic tissue. Emerging multispectral snapshot cameras enable real-time imaging but are limited by slow analysis when using inverse Monte Carlo (MC), the gold standard for analyzing multispectral data. Using artificial neural networks (ANNs) facilitates a significantly faster analysis but requires a large amount of high-quality training data from a wide range of tissue types for a precise estimation of . We aim to develop a framework for training ANNs that estimates in real time from multispectral data with a precision comparable to inverse MC. ANNs are trained using synthetic data from a model that includes MC simulations of light propagation in tissue and hardware characteristics. The model includes physiologically relevant variations in optical properties, unique sensor characteristics, variations in illumination spectrum, and detector noise. This approach enables a rapid way of generating high-quality training data that covers different tissue types and skin pigmentation. The ANN implementation analyzes an image in 0.11s, which is at least 10,000 times faster than inverse MC. The hardware modeling is significantly improved by an in-house calibration of the sensor spectral response. An in-vivo example shows that inverse MC and ANN give almost identical values with a mean absolute deviation of 1.3%-units. ANN can replace inverse MC and enable real-time imaging of microcirculatory in the skin if detailed and precise modeling of both tissue and hardware is used when generating training data.

Rapid extraction of skin physiological parameters from hyperspectral images using machine learning

Noninvasive assessment of skin structure using hyperspectral images has been intensively studied in recent years. Due to the high computational cost of the classical methods, such as the inverse Monte Carlo (IMC), much research has been done with the aim of using machine learning (ML) methods to reduce the time required for estimating parameters. This study aims to evaluate the accuracy and the estimation speed of the ML methods for this purpose and compare them to the traditionally used inverse adding-doubling (IAD) algorithm. We trained three models – an artificial neural network (ANN), a 1D convolutional neural network (CNN), and a random forests (RF) model – to predict seven skin parameters. The models were trained on simulated data computed using the adding-doubling algorithm. To improve predictive performance, we introduced a stacked dynamic weighting (SDW) model combining the predictions of all three individually trained models. SDW model was trained by using only a handful of real-world spectra on top of the ANN, CNN and RF models that were trained using simulated data. Models were evaluated based on the estimated parameters’ mean absolute error (MAE), considering the surface inclination angle and comparing skin spectra with spectra fitted by the IAD algorithm. On simulated data, the lowest MAE was achieved by the RF model (0.0030), while the SDW model achieved the lowest MAE on in vivo measured spectra (0.0113). The shortest time to estimate parameters for a single spectrum was 93.05 μs. Results suggest that ML algorithms can produce accurate estimates of human skin optical parameters in near real-time.

A “Local First” Approach to Glacigenic Sediment Provenance Demonstrated Using U-Pb Detrital Zircon Geochronology of the Permo-Carboniferous Wynyard Formation, Tasmanian Basin

We propose that a “local first” approach should be applied to the interpretation of provenance indicators in glacigenic sediments of all depositional ages, especially where the glacier flow path is poorly constrained and the records of potential source lithologies are incomplete. Provenance proxies, specifically U-Pb detrital zircon geochronology, of glacigenic sediments are commonly used to infer the size and distribution of past ice centers, which are in turn used to inform ancient climate reconstructions. Interpretations of these proxies often assume that similar provenance signals between glacigenic units of the same depositional age are evidence that they were deposited by the same glacier, even when those units are, not infrequently, separated by thousands of kilometers. Though glaciers are capable of transporting sediment great distances, this assumption is problematic as it does not acknowledge observations from the geologic records of Pleistocene ice sheets that show provenance proxies in glacial sediments are most likely to reflect proximal (within 100 km) sediment sources located along a specific flow path. In a “local first” approach, provenance indicators are first compared to local source lithologies. If the indicator cannot be attributed to proximal sources, only then should progressively more distal sources be investigated. Applying a local first approach to sediment provenance in ancient glacial systems may result in significant revisions to paleo ice sheet reconstructions. The effectiveness of the local first approach is demonstrated here by comparing new U-Pb detrital zircon dates from the Permo-Carboniferous glacigenic Wynyard Fm with progressively distal source lithologies along the glacier’s inferred flow path. The Wynyard Fm and source lithologies were compared using an inverse Monte-Carlo unmixing model (DZMix). All measured Wynyard Fm detrital zircon dates can be attributed to zircon sources within 33 km of the sample location along the glacier’s flow path. This interpretation of a proximal detrital zircon provenance does not conflict with the popular interpretation made from sedimentological observations that the Wynyard Fm was deposited by a large, temperate outlet glacier or ice stream that flowed south-to-north across western Tasmania. Overall, a local first approach to glacial sediment provenance, though more challenging than direct comparisons between glacigenic sedimentary deposits, has the potential to elucidate the complex histories and flow paths of glacial sedimentary systems of all depositional ages.

A variational framework for the inverse Henderson problem of statistical mechanics

The inverse Henderson problem refers to the determination of the pair potential which specifies the interactions in an ensemble of classical particles in continuous space, given the density and the equilibrium pair correlation function of these particles as data. For a canonical ensemble in a bounded domain, it has been observed that this pair potential minimizes a corresponding convex relative entropy functional, and that the Newton iteration for minimizing this functional coincides with the so-called inverse Monte Carlo (IMC) iterative scheme. In this paper, we show that in the thermodynamic limit analogous connections exist between the specific relative entropy introduced by Georgii and Zessin and a proper formulation of the IMC iteration in the full space. This provides a rigorous variational framework for the inverse Henderson problem, valid within a large class of pair potentials, including, for example, Lennard-Jones-type potentials. It is further shown that the pressure is strictly convex as a function of the pair potential and the chemical potential, and that the specific relative entropy at fixed density is a strictly convex function of the pair potential. At a given reference potential and a corresponding density in the gas phase, we determine the gradient and the Hessian of the specific relative entropy, and we prove that the Hessian extends to a symmetric positive semidefinite quadratic functional in the space of square integrable perturbations of this potential.

Phase function estimation from a diffuse optical image via deep learning

Objective. The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters. In recent years, machine learning methods were reported to estimate the parameters of the phase function of a particular form such as the Henyey–Greenstein phase function but, to our knowledge, no studies have been performed to determine the form of the phase function. Approach. Here we design a convolutional neural network (CNN) to estimate the phase function from a diffuse optical image without any explicit assumption on the form of the phase function. Specifically, we use a Gaussian mixture model (GMM) as an example to represent the phase function generally and learn the model parameters accurately. The GMM is selected because it provides the analytic expression of phase function to facilitate deflection angle sampling in MC simulation, and does not significantly increase the number of free parameters. Main Results. Our proposed method is validated on MC-simulated reflectance images of typical biological tissues using the Henyey–Greenstein phase function with different anisotropy factors. The mean squared error of the phase function is 0.01 and the relative error of the anisotropy factor is 3.28%. Significance. We propose the first data-driven CNN-based inverse MC model to estimate the form of scattering phase function. The effects of field of view and spatial resolution are analyzed and the findings provide guidelines for optimizing the experimental protocol in practical applications.

Short-range order in Fe-21.9 at.% Al

Abstract Elastic diffuse neutron scattering of an Fe-21.9 at.% Al single crystal was measured at 878, 966 and 1073 K. The separated short-range order scattering revealed the building elements of the B2 structure as the characteristic local atomic arrangement for all states investigated. Effective pair interaction parameters, obtained by the inverse Monte-Carlo method, were found to depend on temperature, thus prohibiting a determination of possible ground state structures from the high-temperature measurements. An additional X-ray study of single-crystalline Fe-18.2 at.% Al aged at 598 K, gave no indication for B32 as ground state structure.

Laser speckle contrast imaging for monitoring of acute pancreatitis at ischemia–reperfusion injury of the pancreas in rats

The influence of ischemia–reperfusion (I/R) action on pancreatic blood flow (PBF) and the development of acute pancreatitis (AP) in laboratory rats is evaluated in vivo by using the laser speckle contrast imaging (LSCI). Additionally, the optical properties in norm and under condition of AP in rats were assessed using a modified integrating sphere spectrometer and inverse Monte Carlo (IMC) software. The results of the experimental study of microcirculation of the pancreas in 82 rats in the ischemic model are presented. The data obtained confirm the fact that local ischemia and changes in the blood flow velocity of the main vessels cause and provoke acute pancreatitis.

Three-dimensional reconstruction of realistic stone-based materials with controllable stone inclusion geometries

Stone-based materials are heterogeneous construction materials that mainly consist of coarse stones and fine matrices. When using the numerical simulation tools (FEM, DEM, FDEM) to study the mechanical properties of the tone-based materials, the irregular stone-inclusion geometries cannot be neglected. The main objective of this study is to generate three-dimensional models of realistic stone-based materials with controllable stone-inclusion geometries. To achieve this goal, two major methodologies are adopted: (1) the Spherical Harmonic Transform (SHT) method and Inverse Monte-Carlo (IMC) algorithm are employed to randomly generate the 3D particle model of irregular stones with controllable geometries at three different shape scales, including form, roundness and roughness, and (2) based on the Spherical Harmonic function, an overlapping detection algorithm is proposed to facilitate the rapid allocation of the 3D SHT-based particles. The proposed algorithm can quantitatively control several geometrical features of the generated stone-based materials in an efficient and precise manner. Finally, the application of the proposed approach is demonstrated through the discrete modelling of stone-based materials with different stone contents and stone shapes. The proposed study has the significance to pave a viable pathway for stochastic modelling of stone-based materials pertaining to various construction and manufacture processes.

Investigation of Damage Evolution in Heterogeneous Rock Based on the Grain-Based Finite-Discrete Element Model.

Granite exhibits obvious meso-geometric heterogeneity. To study the influence of grain size and preferred grain orientation on the damage evolution and mechanical properties of granite, as well as to reveal the inner link between grain size‚ preferred orientation, uniaxial tensile strength (UTS) and damage evolution, a series of Brazilian splitting tests were carried out based on the combined finite-discrete element method (FDEM), grain-based model (GBM) and inverse Monte Carlo (IMC) algorithm. The main conclusions are as follows: (1) Mineral grain significantly influences the crack propagation paths, and the GBM can capture the location of fracture section more accurately than the conventional model. (2) Shear cracks occur near the loading area, while tensile and tensile-shear mixed cracks occur far from the loading area. The applied stress must overcome the tensile strength of the grain interface contacts. (3) The UTS and the ratio of the number of intergrain tensile cracks to the number of intragrain tensile cracks are negatively related to the grain size. (4) With the increase of the preferred grain orientation, the UTS presents a “V-shaped” characteristic distribution. (5) During the whole process of splitting simulation, shear microcracks play the dominant role in energy release; particularly, they occur in later stage. This novel framework, which can reveal the control mechanism of brittle rock heterogeneity on continuous-discontinuous trans-scale fracture process and microscopic rock behaviour, provides an effective technology and numerical analysis method for characterizing rock meso-structure. Accordingly, the research results can provide a useful reference for the prediction of heterogeneous rock mechanical properties and the stability control of engineering rock masses.

Study on the Effect of Amygdales on Mechanical Characteristics of Basalt Using the Combined Finite‐Discrete Element Method

Amygdaloidal basalt, as a heterogeneous rock, is widely exposed at Baihetan hydropower station, China. The geometric effect of amygdales needs further studies and quantifying the shape, orientation, and statistical distribution of amygdales plays an important role in the laboratory and numerical experiments. Therefore, digital image processing (DIP) was first utilized to build a heterogeneous model (HM) to calibrate against the laboratory test results. Then, the heterogeneous models (HMs) with prescribed geometric features were generated by the inverse Monte‐Carlo (IMC) algorithm. The uniaxial compression experiments based on HMs were conducted to study the mechanism of the crack initiation and propagation in the amygdaloidal basalt. The tensile fractures were mainly occurred in the matrix, and the shear fractures were mainly occurred in the amygdales. With the increase in the elliptic coefficient of amygdales, the uniaxial compressive strength (UCS) showed a linear growth trend. With the increase in the orientation of amygdales, the UCS exhibited a “V‐shaped” distribution characteristic. This paper provides a numerical method for studying the mechanical properties of rocks with flaws.

Recent Advances in the Laser Radiation Transport through the Head Tissues of Humans and Animals – A Review

Modern studies of the penetration of light into biological tissues is becoming very important in various medical applications. This is an important factor for determining the optical dose in many diagnostic and therapeutic procedures. The absorption and scattering properties of the tissue under study determine how deeply the light will penetrate into the tissue. However, these optical properties are highly dependent on the wavelength of the light source and tissue condition. This overview paper analyzes the transmission of light through different areas of human and animal head tissues, and the optimal laser wavelength and power density required to reach different parts of the brain are determined using lasers with different wavelengths by comparing the distribution of fluence, penetration depth and the mechanism of interaction between laser light and head tissues. The power variation in different regions of the head is presented, as estimated using Monte Carlo (MC) simulations. Data are analyzed for the absorption and scattering coefficients of the head tissue layers (scalp, skull, brain), calculated using integrating sphere measurements and inverse problem solving algorithms such as inverse MC (IMC) and adding-doubling (IAD). This study not only offered a quantitative comparison between wavelengths in terms of light transmission efficiency, but also anticipated the exciting opportunity for online, accurate and visible optimization of LLLT lighting parameters.

Effective reconstruction of bioluminescence tomography based on GPU-accelerated inverse Monte Carlo method

Diffusion equations (DEs) or simplified spherical harmonic equations are commonly used forward models in bioluminescence tomography (BLT), which are usually numerically calculated by the finite element method to construct the system matrix for reconstruction. However, the numerical solver is not accurate enough. The Monte Carlo (MC) method is regarded as the golden standard for modeling light propagation in biological tissue. In this paper, we proposed a GPU-accelerated inverse MC method for BLT reconstruction. The main feature is that the system matrix for BLT reconstruction is calculated by the MC method instead of the model-based numerical approximation. We evaluated the performance of the proposed method with both phantom-based simulation and animal-based in vivo experiment. The results show that, compared with the DE-based method, the proposed GPU-accelerated inverse MC method is more accurate and effective in BLT reconstruction.

A generalized Newton iteration for computing the solution of the inverse Henderson problem

ABSTRACT We develop a generalized Newton scheme called IHNC (inverse hypernetted-chain iteration) for the construction of effective pair potentials for systems of interacting point-like particles. The construction is realized in such a way that the distribution of the particles matches a given radial distribution function. The IHNC iteration uses the hypernetted-chain integral equation for an approximate evaluation of the inverse of the Jacobian of the forward operator. In contrast to the full Newton method realized in the Inverse Monte Carlo (IMC) scheme, the IHNC algorithm requires only a single molecular dynamics computation of the radial distribution function per iteration step and no further expensive cross-correlations. Numerical experiments are shown to demonstrate that the method is as efficient as the IMC scheme, and that it easily allows to incorporate thermodynamical constraints.

Utilizing nuclear data in delayed gamma-ray spectroscopy inverse Monte Carlo analysis

Safeguards verification of uranium and plutonium in high-radioactivity nuclear material is currently performed using destructive analysis techniques. However, the preparation method is a burden on both the safeguards inspectors and facility operators. While nondestructive assay (NDA) techniques would improve the efficiency and time, there are no passive NDA techniques available to directly verify the U and Pu content. As an alternative, the JAEA and JRC are collaboratively developing the Delayed Gamma-ray Spectroscopy (DGS) active-interrogation NDA technique to evaluate the fissile composition from the unique fission product yield distributions. To analyze the data we are developing an Inverse Monte Carlo (IMC) method that simulates the interrogation and evaluates the individual contributions from the mixed nuclear material to the composite spectrum. While the current nuclear data affects the ability to evaluate the composition, the IMC analysis method can be used to determine the systematic uncertainty contributions and has the potential to improve the nuclear data. We will present the current status of the DGS collaborative work as it relates to the development of the DGS IMC analysis.

Relative entropy indicates an ideal concentration for structure-based coarse graining of binary mixtures.

Many methodological approaches have been proposed to improve systematic or bottom-up coarse-graining techniques to enhance the representability and transferability of the derived interaction potentials. Transferability describes the ability of a coarse-grained (CG) model to be predictive, i.e., to describe a system at state points different from those chosen for parametrization. Whereas the representability characterizes the accuracy of a CG model to reproduce target properties of the underlying reference or fine-grained model at a given state point. In this article, we shift the focus away from methodological aspects and rather raise the question whether we can overcome the disadvantages of a given method in terms of representability and transferability by systematically selecting the state point at which the CG model gets parametrized. We answer this question by applying the inverse Monte Carlo (IMC) approach-a structure-based coarse-graining method-to derive effective interactions for binary mixtures of simple Lennard-Jones (LJ) particles, which are different in size. For such simple systems we indeed can identify a concentration where the derived potentials show the best performance in terms of structural representability and transferability. This specific concentration is identified by computing the relative entropy which quantifies the information loss between different IMC models and the reference LJ model at varying mixture compositions. Further, we show that an IMC model for mixtures of n-hexane and n-perfluorohexane shows the same trend in transferability as the IMC models for the LJ system. All derived models are more transferable in the direction of increasing concentration of the larger-sized compound.

Evolutions of packing properties of perfect cylinders under densification and crystallization.

Cylindrical particles are ubiquitous in nature and industry, and a cylinder is a representative shape of rod-like particles. However, the disordered packing results of cylinders in previous studies are quite inconsistent with each other. In this work, we obtain the MRJ (maximally random jammed) packings and the MDRPs (maximally dense random packings) of perfect cylinders with the aspect ratio (height/diameter) 0.2 ≤ w ≤ 6.0 using the ASC (adaptive shrinking cell) algorithm and the IMC (inverse Monte Carlo) method, respectively. The optimal aspect ratio corresponding to the maximal packing density is w = 0.9 in the MRJ state, while the value is w = 1.2 in the MDRP state. Then we investigate the evolutions of packing properties of perfect cylinders under densification and crystallization. We compare the different final packing states generated via the two methods with different compression rates and order constraints. In the densification procedure, we generate jammed and random packings of cylinders with various compression rates via the ASC and IMC method, respectively. When decreasing the compression rate, we find that the packing density increases but the optimal w remains the same in both methods. In the crystallization procedure, the order constraint in the IMC method is gradually released which means the degree of order in the packings is allowed to increase, and we find that the optimal w shifts from 1.2 to 0.9 while the packing density increases as well. Meanwhile, the random packings evolve into the jammed packings in the crystallization procedure which reflects the competition mechanism between the randomness and jamming. These results also indicate that the optimal w is solely related to the degree of order in the cylinder packings but not determined by the protocol or packing density. Furthermore, a uniform shape elongation effect on the random-packing densities of various shaped particles is found via a new proposed definition of the scaled aspect ratio. Finally, a rough linear relationship between the mean and standard deviation of the reduced Voronoi cell volumes is obtained only for the random packings. Our findings should lead to a better understanding toward the jammed and random packings and are helpful in guiding the granular material design.

Blockspin renormalization-group study of color confinement due to violation of the non-Abelian Bianchi identity

Block-spin transformation of topological defects is applied to the violation of the non-Abelian Bianchi identity (VMABI) on lattice defined as Abelian monopoles. To get rid of lattice artifacts, we introduce various techniques smoothing the vacuum. The effective action can be determined by adopting the inverse Monte-Carlo method. The coupling constants $F(i)$ of the effective action depend on the coupling of the lattice action $\beta$ and the number of the blocking step $n$. But it is found that $F(i)$ satisfy a beautiful scaling, that is, they are a function of the product $b=na(\beta)$ alone for lattice coupling constants $3.0\le\beta\le3.9$ and the steps of blocking $1\le n\le 12$. The effective action showing the scaling behavior can be regarded as an almost perfect action corresponding to the continuum limit, since $a\to 0$ as $n\to\infty$ for fixed $b$. The almost perfect action showing the scaling is found to be independent of the smooth gauges adopted here. Then we compare the results with those obtained by the analytic blocking method of topological defects from the continuum. The infrared monopole action can be transformed into that of the string model. The physical string tension and the lowest glueball mass can be evaluated \textit{analytically} by the strong-coupling expansion of the string model. We get $\sqrt{\sigma}\simeq 1.3\sqrt{\sigma_{phys}}$ for $b\ge 1.0\ \ (\sigma_{phys}^{-1/2})$, whereas the scalar glueball mass is kept to be near $M(0^{++})\sim 3.7\sqrt{\sigma_{phys}}$. Also we can almost reproduce \textit{analytically} the scaling function of the squared monopole density determined numerically for large $b$ region $b>1.2\ (\sigma_{phys}^{-1/2})$.

Addressing the temperature transferability of structure based coarse graining models.

Systematically derived coarse grained (CG) models for molecular liquids do not inherently guarantee transferability to a state point different from its reference, especially when derived on the basis of structure based CG methods like Inverse Monte Carlo (IMC). Several efforts made in the past years to improve the transferability of these models focused on including thermodynamic constraints or on the application of multistate parametrization. Das and Andersen (DA) [Das et al., J. Chem. Phys., 2010, 132, 164106.] proposed a different Ansatz. They derived a correction term added to the system's Hamiltonian to reproduce the virial pressure and the volume fluctuations of the reference system in the CG resolution which does not require further adjustment of the effective pair potential. Herein, we discuss the possibility to achieve temperature transferability with IMC models for selected alkanes following the optimization of the DA approach as proposed by Dunn and Noid (DN) [Dunn et al., J. Chem. Phys., 2015, 143, 243148.]. The work focuses on a novel approach to determine the DN correction term for different state points by linear interpolation.

Comparison of iterative inverse coarse-graining methods

Deriving potentials for coarse-grained Molecular Dynamics (MD) simulations is frequently done by solving an inverse problem. Methods like Iterative Boltzmann Inversion (IBI) or Inverse Monte Carlo (IMC) have been widely used to solve this problem. The solution obtained by application of these methods guarantees a match in the radial distribution function (RDF) between the underlying fine-grained system and the derived coarse-grained system. However, these methods often fail in reproducing thermodynamic properties. To overcome this deficiency, additional thermodynamic constraints such as pressure or Kirkwood-Buff integrals (KBI) may be added to these methods. In this communication we test the ability of these methods to converge to a known solution of the inverse problem. With this goal in mind we have studied a binary mixture of two simple Lennard-Jones (LJ) fluids, in which no actual coarse-graining is performed. We further discuss whether full convergence is actually needed to achieve thermodynamic representability.

Monitoring of tissue optical properties during thermal coagulation of ex vivo tissues.

Real-time monitoring of tissue status during thermal ablation of tumors is critical to ensure complete destruction of tumor mass, while avoiding tissue charring and excessive damage to normal tissues. Currently, magnetic resonance thermometry (MRT), along with magnetic resonance imaging (MRI), is the most commonly used technique for monitoring and assessing thermal ablation process in soft tissues. MRT/MRI is very expensive, bulky, and often subject to motion artifacts. On the other hand, light propagation within tissue is sensitive to changes in tissue microstructure and physiology which could be used to directly quantify the extent of tissue damage. Furthermore, optical monitoring can be a portable, and cost-effective alternative for monitoring a thermal ablation process. The main objective of this study, is to establish a correlation between changes in tissue optical properties and the status of tissue coagulation/damage during heating of ex vivo tissues. A portable diffuse reflectance spectroscopy system and a side-firing fiber-optic probe were developed to study the absorption (μa (λ)), and reduced scattering coefficients (μ's (λ)) of native and coagulated ex vivo porcine, and chicken breast tissues. In the first experiment, both porcine and chicken breast tissues were heated at discrete temperature points between 24 and 140°C for 2 minutes. Diffuse reflectance spectra (430-630 nm) of native and coagulated tissues were recorded prior to, and post heating. In a second experiment, porcine tissue samples were heated at 70°C and diffuse reflectance spectra were recorded continuously during heating. The μa (λ) and μ's (λ) of the tissues were extracted from the measured diffuse reflectance spectra using an inverse Monte-Carlo model of diffuse reflectance. Tissue heating was stopped when the wavelength-averaged scattering plateaued. The wavelength-averaged optical properties, <μ's (λ)> and <μa (λ)>, for native porcine tissues (n = 66) at room temperature, were 5.4 ± 0.3 cm(-1) and 0.780 ± 0.008 cm(-1) (SD), respectively. The <μ's (λ)> and <μa (λ)> for native chicken breast tissues (n = 66) at room temperature, were 2.69 ± 0.08 cm(-1) and 0.29 ± 0.01 cm(-1) (SD), respectively. In the first experiment, the <μ's (λ)> of coagulated porcine and chicken breast tissue rose to 56.4 ± 3.6 cm(-1) at 68.7 ± 1.7°C (SD), and 52.8 ± 1 cm(-1) at 57.1 ± 1.5°C (SD), respectively. Correspondingly, the <μa (λ)> of coagulated porcine (140.6°C), and chicken breast tissues (130°C) were 0.75 ± 0.05 cm(-1) and 0.263 ± 0.004 cm(-1) (SD). For both tissues, charring was observed at temperatures above 80°C. During continuous monitoring of porcine tissue (with connective tissues) heating, the <μ's (λ)> started to rise rapidly from 13.7 ± 1.5 minutes and plateaued at 19 ± 2.5 (SD) minutes. The <μ's (λ)> plateaued at 11.7 ± 3 (SD) minutes for porcine tissue devoid of connective tissue between probe and tissue surface. No charring was observed during continuous monitoring of thermal ablation process. The changes in optical absorption and scattering properties can be continuously quantified, which could be used as a diagnostic biomarker for assessing tissue coagulation/damage during thermal ablation. Lasers Surg. Med. 48:686-694, 2016. © 2016 Wiley Periodicals, Inc.