White Monte Carlo for time-resolved photon migration

Alerstam, Erik; Andersson‐Engels, Stefan; Svensson, Tomas

doi:10.1117/1.2950319

Cited by 104 publications

(112 citation statements)

References 32 publications

Supporting

Mentioning

107

Contrasting

Order By: Relevance

“…Several solutions for Monte Carlo models exist, and the ability to incorporate arbitrary geometries and arbitrary internal heterogeneities with ease has improved dramatically in recent years, as developed by Fang [48]. The inversion of Monte Carlo data is still somewhat crippled by the need to run repeated forward solutions, which takes significant amounts of time unless very regular geometries are used [49,50]. However, the recent developments in graphics card processing units have made this a more realistic potential even for complex geometries [48,51].…”

Section: (B) Internal Shapes and Physical Limitations Of Light Transportmentioning

confidence: 99%

Implicit and explicit prior information in near-infrared spectral imaging: accuracy, quantification and diagnostic value

Pogue

Davis

Leblond

et al. 2011

Phil. Trans. R. Soc. A.

View full text Add to dashboard Cite

Near-infrared spectroscopy (NIRS) of tissue provides quantification of absorbers, scattering and luminescent agents in bulk tissue through the use of measurement data and assumptions. Prior knowledge can be critical about things such as (i) the tissue shape and/or structure, (ii) spectral constituents, (iii) limits on parameters, (iv) demographic or biomarker data, and (v) biophysical models of the temporal signal shapes. A general framework of NIRS imaging with prior information is presented, showing that prior information datasets could be incorporated at any step in the NIRS process, with the general workflow being: (i) data acquisition, (ii) pre-processing, (iii) forward model, (iv) inversion/reconstruction, (v) post-processing, and (vi) interpretation/diagnosis. Most of the development in NIRS has used ad hoc or empirical implementations of prior information such as pre-measured absorber or fluorophore spectra, or tissue shapes as estimated by additional imaging tools. A comprehensive analysis would examine what prior information maximizes the accuracy in recovery and value for medical diagnosis, when implemented at separate stages of the NIRS sequence. Individual applications of prior information can show increases in accuracy or improved ability to estimate biochemical features of tissue, while other approaches may not. Most beneficial inclusion of prior information has been in the inversion/reconstruction process, because it solves the mathematical intractability. However, it is not clear that this is always the most beneficial stage.

show abstract

Section: (B) Internal Shapes and Physical Limitations Of Light Transportmentioning

confidence: 99%

Implicit and explicit prior information in near-infrared spectral imaging: accuracy, quantification and diagnostic value

Pogue

Davis

Leblond

et al. 2011

Phil. Trans. R. Soc. A.

View full text Add to dashboard Cite

show abstract

“…Fully scalable Monte Carlo, also known as white Monte Carlo, uses postprocessing of temporal and spatial binning, but they currently require a homogeneous infinite or semi-infinite model. 36 Instead, we used a library approach as was previously employed by different groups. 35 Sampling μ s 0 ' from 6 to 16 cm −1 by steps of 2 cm −1 required six simulations.…”

Section: Assumption Of Homogeneous Scatteringmentioning

confidence: 99%

“…Finally, a few more complex models of the adult head have been proposed, including analytical solutions of the diffusion equation or of the radiative transfer equation for multiple layer slabs, 32,33 finite difference modeling of light propagation in the true head anatomy of a subject obtained from a magnetic resonance imaging (MRI) scan, 34 and Monte Carlo approaches 35,36 in different geometries. Monte Carlo methods provide the most accurate description of the forward problem, but because of long computation times, they have rarely been implemented in nonlinear fitting routines that require a new simulation for each iteration of the fitting process.…”

Section: Introductionmentioning

confidence: 99%

“…Truly μ s 0 -scalable or white Monte Carlo methods have been developed that enable postsimulation scaling of the scattering coefficient, but they are currently limited to very simple geometries, such as a homogeneous semi-infinite or slab medium. 36 While Monte Carlo approaches theoretically offer flexibility in terms of head geometry, they were, in practice, limited to simple homogenous geometries for which a library of Monte Carlo results could be computed ahead of time, in order to avoid the long computation time required for Monte Carlo simulations. However, with the advent of fast Monte Carlo approaches based on graphical processing unit (GPU) computation, 37,38 the use of numerical approaches to model the forward problem of light propagation has become a viable alternative to analytical solutions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of a layered slab and an atlas head model for Monte Carlo fitting of time-domain near-infrared spectroscopy data of the adult head

et al. 2014

View full text Add to dashboard Cite

Abstract. Near-infrared spectroscopy (NIRS) estimations of the adult brain baseline optical properties based on a homogeneous model of the head are known to introduce significant contamination from extracerebral layers. More complex models have been proposed and occasionally applied to in vivo data, but their performances have never been characterized on realistic head structures. Here we implement a flexible fitting routine of time-domain NIRS data using graphics processing unit based Monte Carlo simulations. We compare the results for two different geometries: a two-layer slab with variable thickness of the first layer and a template atlas head registered to the subject's head surface. We characterize the performance of the Monte Carlo approaches for fitting the optical properties from simulated time-resolved data of the adult head. We show that both geometries provide better results than the commonly used homogeneous model, and we quantify the improvement in terms of accuracy, linearity, and cross-talk from extracerebral layers.

show abstract

“…9 Furthermore, Hayakawa et al 10 have shown that MC-based data evaluation can be used to extract optical properties of tissue heterogeneities ͑i.e., solve inverse two-region problems͒. In the case of time-domain photon migration, Alerstam et al have shown a general necessity for MC-based evaluation of experimental photon time-of-flight spectroscopy 11,12 ͑TOFS͒, and Svensson et al used this approach to extract in vivo optical and physiological characteristics of human prostate tissue from TOFS data. 13 The main drawback of MC methods is the extensive computational burden.…”

mentioning

confidence: 99%

Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration

2008

Self Cite

View full text Add to dashboard Cite

White Monte Carlo for time-resolved photon migration

Cited by 104 publications

References 32 publications

Implicit and explicit prior information in near-infrared spectral imaging: accuracy, quantification and diagnostic value

Implicit and explicit prior information in near-infrared spectral imaging: accuracy, quantification and diagnostic value

Comparison of a layered slab and an atlas head model for Monte Carlo fitting of time-domain near-infrared spectroscopy data of the adult head

Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration

Contact Info

Product

Resources

About