Optical Property Maps Research Articles

Spatial frequency domain imaging combining profile correction enables accurate real-time quantitative mapping of optical properties of apples

Spatial frequency domain imaging technology enables non-contact, wide-field quantitative analysis of optical properties in strong turbid media, thereby enhancing bruise detection in fruits based on changes in optical property values. However, precise and real-time measurement of the optical properties of curved biological materials is challenging due to the significant influence of tissue contours. This paper primarily presents a research study on real-time correction method based on the property that DC component diffuse reflectance (Rd_DC) and AC component diffuse reflectance (Rd_AC) are influenced by the contour trend of the tissue in a similar manner. The aim of this method is to eliminate the significant impact of sample contour angle on demodulation and enhance the detection of apple bruise. Sinusoidal illumination at a spatial frequency of 0.2 mm−1 was projected at 730 nm, with the experiments carried out on standard reflector, polytetrafluoroethylene sphere, and non-bruised and bruised apples. The results showed that, the relative error of the planar standard reflector decreased from the maximum value of 52.85–4.74%. The median of the Rd_AC for the polytetrafluoroethylene spherical standard, which fluctuated between 0.6 and 0.8, was corrected to the theoretical value of 1. For normal apples, the maximum fluctuation in the standard deviation of Rd_AC, after median filtering, reduced from 172.94% at 300×300 pixels to 18.69%. The boxplot of Rd_AC for bruised apples became smaller, with the lower fence being corrected from nearly 0–0.1750, which closely matched the theoretical value of 0.17. Additionally, the correlation of reduced scattering coefficient (μs′) in the ideal region of interest (100×100 pixels) remained at 0.9785 before and after correction, while other areas were corrected, making the bruising more pronounced. The correction method did not require additional experiments, with the consumed time in millisecond level.

Integration of Optical Property Mapping and Machine Learning for Real-Time Classification of Early Bruises of Apples

Integration of Optical Property Mapping and Machine Learning for Real-Time Classification of Early Bruises of Apples

Spatial frequency domain imaging for the assessment of scleroderma skin involvement.

Systemic sclerosis (SSc) is an autoimmune disease characterized by the widespread deposition of excess collagen in the skin and internal organs, as well as vascular dysfunction. The current standard of care technique used to quantify the extent of skin fibrosis in SSc patients is the modified Rodnan skin score (mRSS), which is an assessment of skin thickness based on clinical palpation. Despite being considered the gold standard, mRSS testing requires a trained physician and suffers from high inter-observer variability. In this study, we evaluated the use of spatial frequency domain imaging (SFDI) as a more quantitative and reliable method for assessing skin fibrosis in SSc patients. SFDI is a wide-field and non-contact imaging technique that utilizes spatially modulated light to generate a map of optical properties in biological tissue. The SFDI data were collected at six measurement sites (left and right forearms, hands, and fingers) of eight control subjects and ten SSc patients. mRSS were assessed by a physician, and skin biopsies were collected from subject's forearms and used to assess for markers of skin fibrosis. Our results indicate that SFDI is sensitive to skin changes even at an early stage, as we found a significant difference in the measured optical scattering () between healthy controls and SSc patients with a local mRSS score of zero (no appreciable skin fibrosis by gold standard). Furthermore, we found a strong correlation between the diffuse reflectance (Rd) at a spatial frequency of 0.2 mm-1 and the total mRSS between all subjects (Spearman correlation coefficient = -0.73, p-value < 0.0028), as well as high correlation with histology results. The healthy volunteer results show excellent inter- and intra-observer reliability (ICC > 0.8). Our results suggest that the measurement of tissue and Rd at specific spatial frequencies and wavelengths can provide an objective and quantitative assessment of skin involvement in SSc patients, which could greatly improve the accuracy and efficiency of monitoring disease progression and evaluating drug efficacy.

Multi-contrast digital histopathology of mouse organs using quantitative phase imaging and virtual staining.

Quantitative phase imaging (QPI) has emerged as a new digital histopathologic tool as it provides structural information of conventional slide without staining process. It is also capable of imaging biological tissue sections with sub-nanometer sensitivity and classifying them using light scattering properties. Here we extend its capability further by using optical scattering properties as imaging contrast in a wide-field QPI. In our first step towards validation, QPI images of 10 major organs of a wild-type mouse have been obtained followed by H&E-stained images of the corresponding tissue sections. Furthermore, we utilized deep learning model based on generative adversarial network (GAN) architecture for virtual staining of phase delay images to a H&E-equivalent brightfield (BF) image analogues. Using the structural similarity index, we demonstrate similarities between virtually stained and H&E histology images. Whereas the scattering-based maps look rather similar to QPI phase maps in the kidney, the brain images show significant improvement over QPI with clear demarcation of features across all regions. Since our technology provides not only structural information but also unique optical property maps, it could potentially become a fast and contrast-enriched histopathology technique.

Training generative adversarial networks for optical property mapping using synthetic image data.

We demonstrate the training of a generative adversarial network (GAN) for the prediction of optical property maps (scattering and absorption) using spatial frequency domain imaging (SFDI) image data sets that are generated synthetically with a free open-source 3D modelling and rendering software, Blender. The flexibility of Blender is exploited to simulate 5 models with real-life relevance to clinical SFDI of diseased tissue: flat samples containing a single material, flat samples containing 2 materials, flat samples containing 3 materials, flat samples with spheroidal tumours and cylindrical samples with spheroidal tumours. The last case is particularly relevant as it represents wide-field imaging inside a tubular organ e.g. the gastro-intestinal tract. In all 5 scenarios we show the GAN provides an accurate reconstruction of the optical properties from single SFDI images with a mean normalised error ranging from 1.0-1.2% for absorption and 1.1%-1.2% for scattering, resulting in visually improved contrast for tumour spheroid structures. This compares favourably with the ∼10% absorption error and ∼10% scattering error achieved using GANs on experimental SFDI data. Next, we perform a bi-directional cross-validation of our synthetically-trained GAN, retrained with 90% synthetic and 10% experimental data to encourage domain transfer, with a GAN trained fully on experimental data and observe visually accurate results with an error of 6.3%-10.3% for absorption and 6.6%-11.9% for scattering. Our synthetically trained GAN is therefore highly relevant to real experimental samples but provides the significant added benefits of large training datasets, perfect ground-truths and the ability to test realistic imaging geometries, e.g. inside cylinders, for which no conventional single-shot demodulation algorithms exist. In the future, we expect that the application of techniques such as domain adaptation or training on hybrid real-synthetic datasets will create a powerful tool for fast, accurate production of optical property maps for real clinical imaging systems.

Multi-band bottom index: A novel approach for coastal environmental monitoring using hyperspectral data

Hyperspectral remote sensing has potential for improving shallow-water environmental monitoring, including benthic cover mapping and estimation of bathymetry and water optical properties. Detailed spectral information provided by hyperspectral sensors is particularly required to discriminate benthic covers that have similar spectral patterns (e.g., coral and algae), which cannot be achieved by multispectral sensors with a limited number of wavebands. An important process in such applications is separation of the bottom reflection from the absorption and scattering caused by the water column. Recently, inversion of a semi-analytical radiative transfer model using airborne hyperspectral data has become popular for simultaneous estimation of benthic cover, bathymetry, and water optical properties. However, it requires non-trivial settings and ancillary information, and generally is not user friendly. A cost-effective water-column correction method based on two-band combination has also been popular for multi-band remote sensing; however, it is not suited to hyperspectral data, because of the combinatorial proliferation of many hyperspectral wave bands. To fill the gap between the two approaches, in this paper, a new cost-effective method for water-column correction optimal for hyperspectral data is proposed. The method, called the multi-band bottom index (MBI), incorporates empirical radiative transfer theory and a traditional spectral enhancement technique (i.e., logarithmic residual method). A case study was conducted for the coral reef habitats around Ishigaki Island, using free hyperspectral satellite imagery (Hyperion). The result shows that our MBI approach has more robust performance in the estimation of the water attenuation coefficient than the existing water-column correction approach. The derived MBI map successfully mitigates water-column effects and enhances the spectral pattern of benthic materials. Comparison with in-situ spectra in the literature revealed that the overall spectral pattern of the MBI is reasonable for the benthic categories of coral, seagrass, and sandy bottom. The MBI approach is also promising for application to recently launched hyperspectral satellite sensors, such as HISUI, DESIS, and PRISMA.

Ultracompact Deep Neural Network for Ultrafast Optical Property Extraction in Spatial Frequency Domain Imaging (SFDI)

Spatial frequency domain imaging (SFDI) is a powerful, label-free imaging technique capable of the wide-field quantitative mapping of tissue optical properties and, subsequently, chromophore concentrations. While SFDI hardware acquisition methods have advanced towards video-rate, the inverse problem (i.e., the mapping of acquired diffuse reflectance to optical properties) has remained a bottleneck for real-time data processing and visualization. Deep learning methods are adept at fitting nonlinear patterns, and may be ideal for rapidly solving the SFDI inverse problem. While current deep neural networks (DNN) are growing increasingly larger and more complex (e.g., with millions of parameters or more), our study shows that it can also be beneficial to move in the other direction, i.e., make DNNs that are smaller and simpler. Here, we propose an ultracompact, two-layer, fully connected DNN structure (each layer with four and two neurons, respectively) for ultrafast optical property extractions, which is 30×–600× faster than current methods with a similar or improved accuracy, allowing for an inversion time of 5.5 ms for 696 × 520 pixels. We further demonstrated the proposed inverse model in numerical simulations, and comprehensive phantom characterization, as well as offering in vivo measurements of dynamic physiological processes. We further demonstrated that the computation time could achieve another 200× improvement with a GPU device. This deep learning structure will help to enable fast and accurate real-time SFDI measurements, which are crucial for pre-clinical, clinical, and industrial applications.

Optical Property Mapping of Apples and the Relationship With Quality Properties.

This paper reports on the measurement of optical property mapping of apples at the wavelengths of 460, 527, 630, and 710 nm using spatial-frequency domain imaging (SFDI) technique, for assessing the soluble solid content (SSC), firmness, and color parameters. A laboratory-based multispectral SFDI system was developed for acquiring SFDI of 140 “Golden Delicious” apples, from which absorption coefficient (μa) and reduced scattering coefficient (μs′) mappings were quantitatively determined using the three-phase demodulation coupled with curve-fitting method. There was no noticeable spatial variation in the optical property mapping based on the resulting effect of different sizes of the region of interest (ROI) on the average optical properties. Support vector machine (SVM), multiple linear regression (MLR), and partial least square (PLS) models were developed based on μa, μs′ and their combinations (μa × μs′ and μeff) for predicting apple qualities, among which SVM outperformed the best. Better prediction results for quality parameters based on the μa were observed than those based on the μs′, and the combinations further improved the prediction performance, compared to the individual μa or μs′. The best prediction models for SSC and firmness parameters [slope, flesh firmness (FF), and maximum force (Max.F)] were achieved based on the μa × μs′, whereas those for color parameters of b* and C* were based on the μeff, with the correlation coefficients of prediction as 0.66, 0.68, 0.73, 0.79, 0.86, and 0.86, respectively.

Detection of early stage bruise in apples using optical property mapping

Apple bruises, which are mainly caused by external forces during picking, transportation and marketing, could cause significant economic losses in postharvest storage. Considering the short time window (1–2 h) between bruises occurring and apples are stored, there needs a technique that can identify the invisible early stage bruises (defined as within 1–2 h after occurring), which cannot be completed by the conventional uniform imaging techniques. This study proposed and validated a new means, i.e., optical property mapping, for detecting apple bruises at the early stage. SFDI system was firstly validated, with the results showing that accuracy for measuring μa and μs' were within 8.71 % and 4.93 %, respectively. Optical property mappings of non-bruised and early stage bruised apples were extracted by the methods of three-phase coupled with curve fitting (TP-CF), and single snapshot coupled with look-up table (SS-LUT). The extracted μs' mappings were capable of detecting early stage bruises, and the TP-CF approach resulted into better performance than SS-LUT, but at the expense of lowering efficiency about 14,000 times. Denoising algorithms significantly enhanced the visual effect of bruise detection by minimizing the influence of apple peel spots. The approach proposed in this study is promising for detecting early stage bruising for apple industry.

Halftone spatial frequency domain imaging enables kilohertz high-speed label-free non-contact quantitative mapping of optical properties for strongly turbid media

The ability to quantify optical properties (i.e., absorption and scattering) of strongly turbid media has major implications on the characterization of biological tissues, fluid fields, and many others. However, there are few methods that can provide wide-field quantification of optical properties, and none is able to perform quantitative optical property imaging with high-speed (e.g., kilohertz) capabilities. Here we develop a new imaging modality termed halftone spatial frequency domain imaging (halftone-SFDI), which is approximately two orders of magnitude faster than the state-of-the-art, and provides kilohertz high-speed, label-free, non-contact, wide-field quantification for the optical properties of strongly turbid media. This method utilizes halftone binary patterned illumination to target the spatial frequency response of turbid media, which is then mapped to optical properties using model-based analysis. We validate the halftone-SFDI on an array of phantoms with a wide range of optical properties as well as in vivo human tissue. We demonstrate with an in vivo rat brain cortex imaging study, and show that halftone-SFDI can longitudinally monitor the absolute concentration as well as spatial distribution of functional chromophores in tissue. We also show that halftone-SFDI can spatially map dual-wavelength optical properties of a highly dynamic flow field at kilohertz speed. Together, these results highlight the potential of halftone-SFDI to enable new capabilities in fundamental research and translational studies including brain science and fluid dynamics.

A scalable, multi-wavelength, broad bandwidth frequency-domain near-infrared spectroscopy platform for real-time quantitative tissue optical imaging.

Frequency-domain near-infrared spectroscopy (FD-NIRS) provides quantitative noninvasive measurements of tissue optical absorption and scattering, as well as a safe and accurate method for characterizing tissue composition and metabolism. However, the poor scalability and high complexity of most FD-NIRS systems assembled to date have contributed to its limited clinical impact. To address these shortcomings, we present a scalable, digital-based FD-NIRS platform capable of measuring optical properties and tissue chromophore concentrations in real-time. The system provides single-channel FD-NIRS amplitude/phase, optical property, and chromophore data at a maximum display rate of 36.6 kHz, 17.9 kHz, and 10.2 kHz, respectively, and can be scaled to multiple channels as well as integrated into a handheld format. The entire system is enabled by several innovations including an ultra-high-speed k-nearest neighbor lookup table method (maximum of 250,000 inversions/s for a large 2500x700 table of absorption and reduced scattering coefficients), embedded FPGA and CPU high-speed co-processing, and high-speed data transfer (due to on-board processing). We show that our 6-wavelength, broad modulation bandwidth (1-400 MHz) system can be used to perform 2D high-density spatial mapping of optical properties and high speed quantification of hemodynamics.

Quantification of volumetric morphometry and optical property in the cortex of human cerebellum at micrometer resolution

The surface of the human cerebellar cortex is much more tightly folded than the cerebral cortex. Volumetric analysis of cerebellar morphometry in magnetic resonance imaging studies suffers from insufficient resolution, and therefore has had limited impact on disease assessment. Automatic serial polarization-sensitive optical coherence tomography (as-PSOCT) is an emerging technique that offers the advantages of microscopic resolution and volumetric reconstruction of large-scale samples. In this study, we reconstructed multiple cubic centimeters of ex vivo human cerebellum tissue using as-PSOCT. The morphometric and optical properties of the cerebellar cortex across five subjects were quantified. While the molecular and granular layers exhibited similar mean thickness in the five subjects, the thickness varied greatly in the granular layer within subjects. Layer-specific optical property remained homogenous within individual subjects but showed higher cross-subject variability than layer thickness. High-resolution volumetric morphometry and optical property maps of human cerebellar cortex revealed by as-PSOCT have great potential to advance our understanding of cerebellar function and diseases.

Multi-modal diffuse optical spectroscopy for high-speed monitoring and wide-area mapping of tissue optical properties and hemodynamics.

.Significance: Diffuse optical spectroscopic imaging (DOSI) is a versatile technology sensitive to changes in tissue composition and hemodynamics and has been used for a wide variety of clinical applications. Specific applications have prompted the development of versions of the DOSI technology to fit specific clinical needs. This work describes the development and characterization of a multi-modal DOSI (MM-DOSI) system that can acquire metabolic, compositional, and pulsatile information at multiple penetration depths in a single hardware platform. Additionally, a 3D tracking system is integrated with MM-DOSI, which enables registration of the acquired data to the physical imaging area.Aim: We demonstrate imaging, layered compositional analysis, and metabolism tracking capabilities using a single MM-DOSI system on optical phantoms as well as in vivo human tissue.Approach: We characterize system performance with a silicone phantom containing an embedded object. To demonstrate multi-layer sensitivity, we imaged human calf tissue with a 4.8-mm skin-adipose thickness. Human thenar tissue was also measured using a combined broadband DOSI and continuous-wave near-infrared spectroscopy method ( acquisition rate).Results: High-resolution optical property maps of absorption () and reduced scattering () were recovered on the phantom by capturing over 1000 measurement points in under 5 minutes. On human calf tissue, we show two probing depth layers have significantly different () total-hemo/myoglobin and composition. On thenar tissue, we calculate tissue arterial oxygen saturation, venous oxygen saturation, and tissue metabolic rate of oxygen consumption during baseline and after release of an arterial occlusion.Conclusions: The MM-DOSI can switch between collection of broadband spectra, high-resolution images, or multi-depth hemodynamics without any hardware reconfiguration. We conclude that MM-DOSI enables acquisition of high resolution, multi-modal data consolidated in a single platform, which can provide a more comprehensive understanding of tissue hemodynamics and composition for a wide range of clinical applications.

Spatial frequency domain imager based on a compact multiaperture camera: testing and feasibility for noninvasive burn severity assessment.

.Significance: Spatial frequency domain imaging (SFDI) is a wide-field imaging technique that provides quantitative maps of tissue optical properties. We describe a compact SFDI imager that employs a multispectral compound-eye camera. This design enables simultaneous image acquisition at multiple wavelengths. Such a device has potential for application for quantitative evaluation of superficial tissues by nonspecialists in low-resource settings.Aim: The aim of this work was to develop a compact SFDI imager for widefield imaging of in-vivo tissue optical properties and verify its ability to measure optical properties of tissue-simulating phantoms and in a preclinical model of burn wounds.Approach: This compound-eye imager was constructed using a CMOS sensor subdivided into multiple regions, each having a bandpass filter and objective lens. The ability of the instrument to image optical properties was compared with (1) a commercial SFDI imager and (2) a laboratory-based system. Initial validation of ability to accurately characterize optical properties was performed using a tissue-simulating optical phantom. It was then applied to an established murine model of thermal contact burn severity. In-vivo measurements of the optical properties of rat skin were performed before and after the application of burns. Histology was used to verify burn severity.Results: Measurements of the tissue-simulating phantom optical properties made using the compound-eye imager agree with measurements made using the two comparison SFDI devices. For the murine burn model, the burns showed a decrease in the reduced scattering coefficient at all measurement wavelengths compared with preburn measurements at the same locations. This is consistent with previously reported changes in scattering that occur in full-thickness burns.Conclusion: We demonstrate the potential for SFDI to be translated into compact form factor using a compound-eye camera that is capable of obtaining multiple wavelengths channels simultaneously.

An artificial neural network model for accurate and efficient optical property mapping from spatial-frequency domain images

Conventional optical property mapping by spatial-frequency domain imaging (SFDI) is prone to relatively low efficiency due to the iterative nature of nonlinear curve-fitting based on light transfer model, such as diffusion approximation equation. This study aims at expediating the prediction of optical property mapping with high accuracy by developing an artificial neural network (ANN) model coupled with SFDI. A dataset was first generated using Monte Carlo simulations based on Graphic Processing Unit. The ANN training was then conducted and the model was optimized for multiple factors (learning rate, batch size and number of neurons). Experiments with simulation samples and a solid phantom were carried out to verify the performance of the ANN model for predicting optical property mapping. Results showed that the normalized mean absolute errors of absorption coefficient (μa) and reduced scattering coefficient (μ's) were 0.18 % and 0.027 %, while the root mean square errors were as low as 0.01 and 0.14. Optical properties of the solid phantom demonstrated that the proposed ANN model retained the accuracy, and was about three orders of magnitude faster than the inverse curve-fitting model. Finally, the ANN model was tested on optical property measurement of ‘Golden Delicious’ apples with three different bruising levels (non-, light and severe bruising). Results indicated that the ANN model could measure apple μa and μ's accurately and efficiently, and the measured μ's mapping was capable of detecting early bruising in apples. Contrast of bruise feature in μ's value between bruised and non-bruised tissues was significantly improved, enhancing apple bruising detection.

Speckle illumination SFDI for projector-free optical property mapping.

Spatial frequency domain imaging can map tissue scattering and absorption properties over a wide field of view, making it useful for clinical applications such as wound assessment and surgical guidance. This technique has previously required the projection of fully characterized illumination patterns. Here, we show that random and unknown speckle illumination can be used to sample the modulation transfer function of tissues at known spatial frequencies, allowing the quantitative mapping of optical properties with simple laser diode illumination. We compute low- and high-spatial frequency response parameters from the local power spectral density for each pixel and use a lookup table to accurately estimate absorption and scattering coefficients in tissue phantoms, in vivo human hand, and ex vivo swine esophagus. Because speckle patterns can be generated over a large depth of field and field of view with simple coherent illumination, this approach may enable optical property mapping in new form-factors and applications, including endoscopy.

Applications of compressive sensing in spatial frequency domain imaging.

.Significance: Spatial frequency domain imaging (SFDI) is an imaging modality that projects spatially modulated light patterns to determine optical property maps for absorption and reduced scattering of biological tissue via a pixel-by-pixel data acquisition and analysis procedure. Compressive sensing (CS) is a signal processing methodology which aims to reproduce the original signal with a reduced number of measurements, addressing the pixel-wise nature of SFDI. These methodologies have been combined for complex heterogenous data in both the image detection and data analysis stage in a compressive sensing SFDI (cs-SFDI) approach, showing reduction in both the data acquisition and overall computational time.Aim: Application of CS in SFDI data acquisition and image reconstruction significantly improves data collection and image recovery time without loss of quantitative accuracy.Approach: cs-SFDI has been applied to an increased heterogenic sample from the AppSFDI data set (back of the hand), highlighting the increased number of CS measurements required as compared to simple phantoms to accurately obtain optical property maps. A novel application of CS to the parameter recovery stage of image analysis has also been developed and validated.Results: Dimensionality reduction has been demonstrated using the increased heterogenic sample at both the acquisition and analysis stages. A data reduction of 30% for the cs-SFDI and up to 80% for the parameter recover was achieved as compared to traditional SFDI, while maintaining an error of for the recovered optical property maps.Conclusion: The application of data reduction through CS demonstrates additional capabilities for multi- and hyperspectral SFDI, providing advanced optical and physiological property maps.

First experience with spatial frequency domain imaging and red-light excitation of protoporphyrin IX fluorescence during tumor resection.

Fluorescence-guided surgery (FGS) enhances intraoperative visualization of tumors to maximize safe resection, and quantitative fluorescence imaging (qFI) of protoporphyrin IX (PpIX) has provided additional information for guidance during intracranial tumor surgery. Previous developments in fluorescence quantification have demonstrated that the depth of fluorescence signals can be estimated given known optical properties in a lab setting, and now with the work described here that these optical properties can be determined in vivo in human brain tissue in the operating room (OR) during tumor resection procedures. More specifically, we report the first depth estimation of subsurface tumor intraoperatively, achieved with the combination of spatial frequency domain imaging (SFDI) for optical property measurement and red-light excitation of PpIX. We modified a commercial surgical microscope (Zeiss) with a digital light processing module (DLI Austin, TX) to modulate light from a xenon arc lamp to illuminate the field. White-light excitation and a liquid crystal tunable filter (LCTF Verispec) were used to measure diffuse reflectance at discrete wavelengths of 670 nm and 710 nm on a sCMOS camera. An illumination-side filter wheel allowed excitation of PpIX fluorescence at 405 nm and 635 nm, and the LCTF measured fluorescence emissions at 670 nm and 710 nm. Data acquisition and processing generated wide-field images of the depth of PpIX fluorescence within 1 minute in the OR. The ability of the clinical microscope to perform optical property mapping with SFDI and convert these wide-field estimates into images of the depth of fluorescence was tested in tissue simulating phantoms and in vivo during a craniotomy for brain tumor resection. Results indicate that wide-field optical property estimates with SFDI can be combined with depth sensing algorithms to produce maps of the depth of PpIX when exposed to red-light in the OR.

GANPOP: Generative Adversarial Network Prediction of Optical Properties From Single Snapshot Wide-Field Images.

We present a deep learning framework for wide-field, content-aware estimation of absorption and scattering coefficients of tissues, called Generative Adversarial Network Prediction of Optical Properties (GANPOP). Spatial frequency domain imaging is used to obtain ground-truth optical properties at 660 nm from in vivo human hands and feet, freshly resected human esophagectomy samples, and homogeneous tissue phantoms. Images of objects with either flat-field or structured illumination are paired with registered optical property maps and are used to train conditional generative adversarial networks that estimate optical properties from a single input image. We benchmark this approach by comparing GANPOP to a single-snapshot optical property (SSOP) technique, using a normalized mean absolute error (NMAE) metric. In human gastrointestinal specimens, GANPOP with a single structured-light input image estimates the reduced scattering and absorption coefficients with 60% higher accuracy than SSOP while GANPOP with a single flat-field illumination image achieves similar accuracy to SSOP. When applied to both in vivo and ex vivo swine tissues, a GANPOP model trained solely on structured-illumination images of human specimens and phantoms estimates optical properties with approximately 46% improvement over SSOP, indicating adaptability to new, unseen tissue types. Given a training set that appropriately spans the target domain, GANPOP has the potential to enable rapid and accurate wide-field measurements of optical properties.

Wide-field optical property mapping and structured light imaging of the esophagus with spatial frequency domain imaging.

As the incidence of esophageal adenocarcinoma continues to rise, there is a need for improved imaging technologies with contrast to abnormal esophageal tissues. To inform the design of optical technologies that meet this need, we characterize the spatial distribution of the scattering and absorption properties from 471 to 851 nm of eight resected human esophagi tissues using Spatial Frequency Domain Imaging. Histopathology was used to categorize tissue types, including normal, inflammation, fibrotic, ulceration, Barrett's Esophagus and squamous cell carcinoma. Average absorption and reduced scattering coefficients of normal tissues were 0.211 ± 0.051 and 1.20 ± 0.18 mm-1 , respectively at 471 nm, and both values decreased monotonically with increasing wavelength. Fibrotic tissue exhibited at least 68% larger scattering signal across all wavelengths, while squamous cell carcinoma exhibited a 36% decrease in scattering at 471 nm. We additionally image the esophagus with high spatial frequencies up to 0.5 mm-1 and show strong reflectance contrast to tissue treated with radiation. Lastly, we observe that esophageal absorption and scattering values change by an average of 9.4% and 2.7% respectively over a 30 minute duration post-resection. These results may guide system design for the diagnosis, prevention and monitoring of esophageal pathologies.