Imaging sub-diffuse optical properties of cancerous and normal skin tissue using machine learning-aided spatial frequency domain imaging

Impact of scattering phase function and polarization on the accuracy of diffuse and sub-diffuse spatial frequency domain imaging

Significance

Although spatial frequency domain imaging (SFDI) has been well characterized under diffuse optical conditions, tissue measurements made outside the diffuse regime can provide new diagnostic information. Before such measurements can become clinically relevant, however, the behavior of sub-diffuse SFDI and its effect on the accuracy of derived tissue parameters must be assessed.

Aim

We aim to characterize the impact that both the assumed scattering phase function (SPF) and the polarization state of the illumination light source have on the accuracy of SFDI-derived optical properties when operating under diffuse or sub-diffuse conditions, respectively.

Approach

Through the use of a set of well-characterized optical phantoms, SFDI accuracy was assessed at four wavelengths (395, 545, 625, and 850 nm) and two different spatial frequencies (0.3 and 1.0    mm - 1 ), which provided a broad range of diffuse and sub-diffuse conditions, using three different SPFs. To determine the effects of polarization, the SFDI accuracy was assessed using both unpolarized and cross-polarized illumination.

Results

It was found that the assumed SPF has a direct and significant impact on the accuracy of the SFDI-derived optical properties, with the best choice of SPF being dictated by the polarization state. As unpolarized SFDI retains the sub-diffuse portion of the signal, optical properties were found to be more accurate when using the full SPF that includes forward and backscattering components. By contrast, cross-polarized SFDI yielded accurate optical properties when using a forward-scattering SPF, matching the behavior of cross-polarization to attenuate the immediate backscattering of sub-diffuse reflectance. Using the correct pairings of SPF and polarization enabled using a reflectance standard, instead of a more subjective phantom, as the reference measurement.

Conclusions

These results provide the foundation for a more thorough understanding of SFDI and enable new applications of this technology in which sub-diffuse conditions dominate (e.g.,μ a ≮ μ s ' ) or high spatial frequencies are required.

Neural network-based optimization of sub-diffuse reflectance spectroscopy for improved parameter prediction and efficient data collection

In this study, a general and systematical investigation of sub-diffuse reflectance spectroscopy is implemented. A Gegenbauer-kernel phase function-based Monte Carlo is adopted to describe photon transport more efficiently. To improve the computational efficiency and accuracy, two neural network algorithms, namely, back propagation neural network and radial basis function neural network are utilized to predict the absorption coefficient μ a , reduced scattering coefficient μ s ' and sub-diffusive quantifier γ , simultaneously, at multiple source-detector separations (SDS). The predicted results show that the three parameters can be predicated accurately by selecting five SDSs or above. Based on the simulation results, a four wavelength (520, 650, 785 and 830 nm) measurement system using five SDSs is designed by adopting phase-lock-in technique. Furtherly, the trained neural-network models are utilized to extract optical properties from the phantom and in vivo experimental data. The results verify the feasibility and effectiveness of our proposed system and methods in mucosal disease diagnosis.

Model for the diffuse reflectance in spatial frequency domain imaging

Significance

In spatial frequency domain imaging (SDFI), tissue is illuminated with sinusoidal intensity patterns at different spatial frequencies. For low spatial frequencies, the reflectance is diffuse and a model derived by Cuccia et al. (doi 10.1117/1.3088140) is commonly used to extract optical properties. An improved model resulting in more accurate optical property extraction could lead to improved diagnostic algorithms.

Aim

To develop a model that improves optical property extraction for the diffuse reflectance in SFDI compared to the model of Cuccia et al.

Approach

We derive two analytical models for the diffuse reflectance, starting from the theoretical radial reflectance R ( ρ ) for a pencil-beam illumination under the partial current boundary condition (PCBC) and the extended boundary condition (EBC). We compare both models and the model of Cuccia et al. to Monte Carlo simulations.

Results

The model based on the PCBC resulted in the lowest errors, improving median relative errors compared to the model of Cuccia et al. by 45% for the reflectance, 10% for the reduced scattering coefficient and 64% for the absorption coefficient.

Conclusions

For the diffuse reflectance in SFDI, the model based on the PCBC provides more accurate results than the currently used model by Cuccia et al.

Improved topographic reconstruction of turbid media in the spatial frequency domain including the determination of the reduced scattering and absorption coefficients

The separation of scattering and absorption is of great importance for studying the radiative transfer in turbid media. Obtaining the corresponding coefficients for non-flat objects is difficult and needs special consideration. Building on our previous work [J. Opt. Soc. Am. A39, 1823 (2022)JOAOD60740-323210.1364/JOSAA.464007], we present an approach that takes the changing incident and detection angles relative to the surface normal of curved surfaces into account to improve the determination of the reduced scattering and absorption coefficients with measurements in the spatial frequency domain (SFD). The optical coefficients are reconstructed using a pre-calculated lookup table generated with Monte Carlo simulations on graphical processing units. With the obtained values, the error in the captured surface geometry of the object, which is due to the volume scattering, is compensated and reduced by 1 order of magnitude for measurements in the SFD. Considering the approximate surface geometry, the absorption and reduced scattering are accurately resolved for moderate object curvatures, with very low dependence on the tilt angle. In contrast to models that only correct the amplitudes of the SFD signal, our approach, in addition to the optical properties, predicts the phase values correctly, which is the reason why it can be used to correct the surface geometry.

Extracting Tissue Optical Properties and Detecting Bruised Tissue in Pears Quickly and Accurately Based on Spatial Frequency Domain Imaging and Machine Learning

Recently, Spatial Frequency Domain Imaging (SFDI) has gradually become an alternative method to extract tissue optical properties (OPs), as it provides a wide-field, no-contact acquisition. SFDI extracts OPs by least-square fitting (LSF) based on the diffuse approximation equation, but there are shortcomings in the speed and accuracy of extracting OPs. This study proposed a Long Short-term Memory Regressor (LSTMR) solution to extract tissue OPs. This method allows for fast and accurate extraction of tissue OPs. Firstly, the imaging system was developed, which is more compact and portable than conventional SFDI systems. Next, numerical simulation was performed using the Monte Carlo forward model to obtain the dataset, and then the mapping model was established using the dataset. Finally, the model was applied to detect the bruised tissue of 'crown' pears. The results show that the mean absolute errors of the absorption coefficient and the reduced scattering coefficient are no more than 0.32% and 0.21%, and the bruised tissue of 'crown' pears can be highlighted by the change of OPs. Compared with the LSF, the speed of extracting tissue OPs is improved by two orders of magnitude, and the accuracy is greatly improved. The study contributes to the rapid and accurate extraction of tissue OPs based on SFDI and has great potential in food safety assessment.

Three-dimensional representation of triple spectral line imaging data as an option for noncontact skin diagnostics

SIGNIFICANCE

Skin malformations in dermatology are mostly evaluated subjectively, based on a doctor's experience and visual perception; an option for objective quantitative skin assessment is camera-based spectrally selective diagnostics. Multispectral imaging is a technique capable to provide information about concentrations of the absorbing chromophores and their distribution over the malformation in a noncontact way. Conversion of spectral images into distribution maps of chromophores can be performed by means of the modified Beer-Lambert law. However, such distribution maps represent only single specific cases, therefore, some extensive method for data comparison is needed.

AIM

This study aims to develop a more informative approach for identification and characterization of skin malformations using three-dimensional (3D) representation of triple spectral line imaging data.

APPROACH

The 3D-representation method is experimentally tested on eight different skin pathology types, including both benign and malignant pathologies; an imaging device ensuring uniform three laser line (448, 532, and 659 nm) illumination is used. Three spectral line images are extracted from a single snapshot RGB image data, with subsequent calculation of attenuation coefficients for each working wavelength at every image pixel and represented as 3D graphs. Skin chromophore content variations in malformations are represented in a similar way.

RESULTS

Clinical measurement results for 99 skin pathologies, including basal cell carcinomas, melanoma, dermal nevi, combined nevi, junctional nevi, blue nevi, seborrheic keratosis, and hemangiomas. They are presented as 3D spectral attenuation maps exhibiting specific individual features for each group of pathologies. Along with intensity attenuation maps, 3D maps for content variations of three main skin chromophores (melanin, oxyhemoglobin, and deoxyhemoglobin), calculated in frame of a model based on modified Beer-Lambert law, are also presented. Advantages and disadvantages of the proposed data representation method are discussed.

CONCLUSIONS

The described 3D-representation method of triple spectral line imaging data shows promising potential for objective quantitative noncontact diagnosis of skin pathologies.

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.

Quantifying the confounding effect of pigmentation on measured skin tissue optical properties: a comparison of colorimetry with spatial frequency domain imaging

SIGNIFICANCE

Spatial frequency domain imaging (SFDI) is a wide-field diffuse optical imaging technique for separately quantifying tissue reduced scattering (μs  '  ) and absorption (μa) coefficients at multiple wavelengths, providing wide potential utility for clinical applications such as burn wound characterization and cancer detection. However, measured μs  '   and μa can be confounded by absorption from melanin in patients with highly pigmented skin. This issue arises because epidermal melanin is highly absorbing for visible wavelengths and standard homogeneous light-tissue interaction models do not properly account for this complexity. Tristimulus colorimetry (which quantifies pigmentation using the L  *   "lightness" parameter) can provide a point of comparison between μa, μs  '  , and skin pigmentation.

AIM

We systematically compare SFDI and colorimetry parameters to quantify confounding effects of pigmentation on measured skin μs  '   and μa. We assess the correlation between SFDI and colorimetry parameters as a function of wavelength.

APPROACH

μs  '   and μa from the palm and ventral forearm were measured for 15 healthy subjects with a wide range of skin pigmentation levels (Fitzpatrick types I to VI) using a Reflect RS® (Modulim, Inc., Irvine, California) SFDI instrument (eight wavelengths, 471 to 851 nm). L  *   was measured using a Chroma Meter CR-400 (Konica Minolta Sensing, Inc., Tokyo). Linear correlation coefficients were calculated between L  *   and μs  '   and between L  *   and μa at all wavelengths.

RESULTS

For the ventral forearm, strong linear correlations between measured L  *   and μs  '   values were observed at shorter wavelengths (R  >  0.92 at ≤659  nm), where absorption from melanin confounded the measured μs  '  . These correlations were weaker for the palm (R  <  0.59 at ≤659  nm), which has less melanin than the forearm. Similar relationships were observed between L  *   and μa.

CONCLUSIONS

We quantified the effects of epidermal melanin on skin μs  '   and μa measured with SFDI. This information may help characterize and correct pigmentation-related inaccuracies in SFDI skin measurements.