From Monte Carlo simulations to efficient estimation of optical properties for spatial frequency domain imaging

Naglič, Peter; Zelinskyi, Yevhen; Likar, Boštjan; Pernuš, Franjo; Bürmen, Miran

doi:10.1117/12.2510195

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Overall, the results of the calibration, i.e., the characterization of the geometric imaging properties, including realistic distortions and inhomogeneous intensity distributions, can be further used to establish a digital twin of the setup. A future goal is therefore to simulate the entire measurement process with forward calculations using numerical methods such as the Monte Carlo method [ 40 , 41 ]. This would make it possible, for example, to determine the mutual error contribution in the simultaneous determination of the optical properties and the 3D topography of turbid samples or to enable new possibilities for data evaluation.…”

Section: Summary and Outlookmentioning

confidence: 99%

Generic and Model-Based Calibration Method for Spatial Frequency Domain Imaging with Parameterized Frequency and Intensity Correction

Lohner,

Nothelfer,

Kienle

2023

Sensors

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Spatial frequency domain imaging (SFDI) is well established in biology and medicine for non-contact, wide-field imaging of optical properties and 3D topography. Especially for turbid media with displaced, tilted or irregularly shaped surfaces, the reliable quantitative measurement of diffuse reflectance requires efficient calibration and correction methods. In this work, we present the implementation of a generic and hardware independent calibration routine for SFDI setups based on the so-called pinhole camera model for both projection and detection. Using a two-step geometric and intensity calibration, we obtain an imaging model that efficiently and accurately determines 3D topography and diffuse reflectance for subsequently measured samples, taking into account their relative distance and orientation to the camera and projector, as well as the distortions of the optical system. Derived correction procedures for position- and orientation-dependent changes in spatial frequency and intensity allow the determination of the effective scattering coefficient μs′ and the absorption coefficient μa when measuring a spherical optical phantom at three different measurement positions and at nine wavelengths with an average error of 5% and 12%, respectively. Model-based calibration allows the characterization of the imaging properties of the entire SFDI system without prior knowledge, enabling the future development of a digital twin for synthetic data generation or more robust evaluation methods.

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Section: Summary and Outlookmentioning

confidence: 99%

Generic and Model-Based Calibration Method for Spatial Frequency Domain Imaging with Parameterized Frequency and Intensity Correction

Lohner,

Nothelfer,

Kienle

2023

Sensors

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“…We have recently shown that such a configuration is necessary for the accurate discrete transformation of the spatially resolved reflectance into the spatial frequency domain. 48 While the detectors in the second subset also include the total specular reflectance, the spatially resolved reflectance is acquired through a detector tilted by 20 deg with an acceptance cone equal to 10 deg using 8000 logarithmically spaced

-symmetric bins (infinite in the

-direction) spanning from

to 150 mm. The latter configuration more accurately represents the realistic case where the tissue is illuminated perpendicularly, and the backscattered light is acquired with a tilted camera.…”

Section: Software Package Pyxopto and Datasetsmentioning

confidence: 99%

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

2022

Self Cite

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. 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.

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“…However, the validity and accuracy of the phase function model depend not only on the characteristics of the media (Canpolat andMourant 2000, Gkioulekas et al 2013) but also the experimental setup. For example, the Henyey-Greenstein function performs well in the diffuse domain but fails in the subdiffusive domain where the source-detector distance is short (Canpolat andMourant 2000, Calabro andBigio 2014) or in the spatial frequency domain imaging mode at sufficiently high spatial frequencies (Naglič et al 2019). Due to the optically heterogeneous nature of biological tissues, the selection of the phase function and the estimation of its parameters remains a rather challenging task, especially when the tissue geometry is complex.…”

Section: Introductionmentioning

confidence: 99%

Phase function estimation from a diffuse optical image via deep learning

Liang¹,

Niu²,

Chen³

et al. 2022

Phys. Med. Biol.

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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 as an example to represent the phase function generally and learn the model parameters accurately. The Gaussian mixture model 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. 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.

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From Monte Carlo simulations to efficient estimation of optical properties for spatial frequency domain imaging

Cited by 3 publications

References 18 publications

Generic and Model-Based Calibration Method for Spatial Frequency Domain Imaging with Parameterized Frequency and Intensity Correction

Generic and Model-Based Calibration Method for Spatial Frequency Domain Imaging with Parameterized Frequency and Intensity Correction

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

Phase function estimation from a diffuse optical image via deep learning

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