Feasibility of spatial frequency domain imaging (SFDI) for optically characterizing a preclinical oncology model

Deep-learning-enabled spatial frequency domain imaging of the spatiotemporal dynamics of skin physiology

Significance

Spatial frequency domain imaging (SFDI) is an emerging optical imaging modality for visualizing tissue absorption and scattering properties. This approach is promising for noninvasive wide field-of-view (FOV) monitoring of biophysiological processes in vivo.

Aim

We aim to develop deep-learning-enabled spatial frequency domain imaging (SFDI-net) for real-time large FOV imaging of the optical, structural, and physiological properties and demonstrate its application for probing the spatiotemporal dynamics of skin physiology.

Approach

SFDI-net, based on mapping of a two-layer structure into an equivalent homogeneous medium for spatially modulated light and with a convolutional neural network architecture, produces two-dimensional maps of optical, structural, and physiological parameters for bilayered tissue, including cutaneous hemoglobin concentration, oxygen saturation, scattering properties (reduced scattering coefficient and scattering power), melanin content, surface roughness, and epidermal thickness, with visible spatially modulated light at the camera frame rate.

Results

Compared with traditional approaches, SFDI-net achieves a real-time inversion speed and significantly improves image quality by effectively suppressing noise while preserving tissue structure without oversmoothing. We demonstrate the application of the SFDI-net for monitoring the spatiotemporal dynamics of forearm skin physiology in reactive hyperemia and rhythmic respiration and reveal their intricate patterns in hemodynamics.

Conclusions

Deep-learning-enabled spatial frequency domain imaging and SFDI-net may offer insights into the cardiorespiratory system and have promising clinical utility for disease diagnosis, surveillance, and therapeutic assessment. Future hardware and software advancements will bring SFDI-net to clinical practice.

Monte Carlo simulation of spatial frequency domain imaging for breast tumors during compression

Significance

Near-infrared optical imaging methods have shown promise for monitoring response to neoadjuvant chemotherapy (NAC) for breast cancer, with endogenous contrast coming from oxy- and deoxyhemoglobin. Spatial frequency domain imaging (SFDI) could be used to detect this contrast in a low-cost and portable format, but it has limited imaging depth. It is possible that local tissue compression could be used to reduce the effective tumor depth.

Aim

To evaluate the potential of SFDI for therapy response prediction, we aim to predict how changes to tumor size, stiffness, and hemoglobin concentration would be reflected in contrast measured by SFDI under tissue compression.

Approach

Finite element analysis of compression on an inclusion-containing soft material is combined with Monte Carlo simulation to predict the measured optical contrast.

Results

When the effect of compression on blood volume is not considered, contrast gain from compression increases with the size and stiffness of the inclusion and decreases with the inclusion depth. With a model of reduction of blood volume from compression, compression reduces imaging contrast, an effect that is greater for larger inclusions and stiffer inclusions at shallower depths.

Conclusions

This computational modeling study represents a first step toward tracking tumor changes induced by NAC using SFDI and local compression.

Monte Carlo simulations and phantom modeling for spatial frequency domain imaging of surgical wound monitoring

Significance

Postoperative surgical wound infection is a serious problem around the globe, including in countries with advanced healthcare systems, and a method for early detection of infection is urgently required.

Aim

We explore spatial frequency domain imaging (SFDI) for distinguishing changes in surgical wound healing based on the tissue scattering properties and surgical wound width measurements.

Approach

A comprehensive numerical method is developed by applying a three-dimensional Monte Carlo simulation to a vertical heterogeneous wound model. The Monte Carlo simulation results are validated using resin phantom imaging experiments.

Results

We report on the SFDI lateral resolution with varying reduced scattering value and wound width and discuss the partial volume effect at the sharp vertical boundaries present in a surgical incision. The detection sensitivity of this method is dependent on spatial frequency, wound reduced scattering coefficient, and wound width.

Conclusions

We provide guidelines for future SFDI instrument design and explanation for the expected error in SFDI measurements.

Designing and simulating realistic spatial frequency domain imaging systems using open-source 3D rendering software

Spatial frequency domain imaging (SFDI) is a low-cost imaging technique that maps absorption and reduced scattering coefficients, offering improved contrast for important tissue structures such as tumours. Practical SFDI systems must cope with various imaging geometries including imaging planar samples ex vivo, imaging inside tubular lumen in vivo e.g. for endoscopy, and measuring tumours or polyps of varying morphology. There is a need for a design and simulation tool to accelerate design of new SFDI systems and simulate realistic performance under these scenarios. We present such a system implemented using open-source 3D design and ray-tracing software Blender that simulates media with realistic absorption and scattering in a wide range of geometries. By using Blender's Cycles ray-tracing engine, our system simulates effects such as varying lighting, refractive index changes, non-normal incidence, specular reflections and shadows, enabling realistic evaluation of new designs. We first demonstrate quantitative agreement between Monte-Carlo simulated absorption and reduced scattering coefficients with those simulated from our Blender system, achieving 16 % discrepancy in absorption coefficient and 18 % in reduced scattering coefficient. However, we then show that using an empirically derived look-up table the errors reduce to 1 % and 0.7 % respectively. Next, we simulate SFDI mapping of absorption, scattering and shape for simulated tumour spheroids, demonstrating enhanced contrast. Finally we demonstrate SFDI mapping inside a tubular lumen, which highlighted a important design insight: custom look-up tables must be generated for different longitudinal sections of the lumen. With this approach we achieved 2 % absorption error and 2 % scattering error. We anticipate our simulation system will aid in the design of novel SFDI systems for key biomedical applications.

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 (μs') 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 (R_d) 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 μs' and R_d 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.

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.

Quantitative spatial mapping of tissue water and lipid content using spatial frequency domain imaging in the 900- to 1000-nm wavelength region

Significance

Water and lipid are key participants of many biological processes, but there are few label-free, non-contact optical methods that can spatially map these components in-vivo. Shortwave infrared meso-patterned imaging (SWIR-MPI) is an emerging technique that successfully addresses this need. However, it requires a dedicated SWIR camera to probe the 900- to 1300-nm wavelength region, which hinders practical translation of the technology.

Aim

Compared with SWIR-MPI, we aim to develop a new technique that can dramatically reduce the cost in detector while maintaining high accuracy for the quantification of tissue water and lipid content.

Approach

By utilizing water and lipid absorption features in the 900- to 1000-nm wavelength region as well as optimal wavelength and spatial frequency combinations, we develop a new imaging technique based on spatial frequency domain imaging to quantitatively map tissue water and lipid content using a regular silicon-based camera.

Results

The proposed method is validated with a phantom study, which shows average error of 0.9 ± 1.2 % for water content estimation, and -0.4 ± 0.7 % for lipid content estimation, respectively. The proposed method is also demonstrated for ex vivo porcine tissue lipid mapping as well as in-vivo longitudinal water content monitoring.

Conclusions

The proposed technique enables spatial mapping of tissue water and lipid content with the cost in detector reduced by two orders of magnitude compared with SWIR-MPI while maintaining high accuracy. The experimental results highlight the potential of this technique for substantial impact in both scientific and industrial applications.

Two-layer analytical model for estimation of layer thickness and flow using Diffuse Correlation Spectroscopy

Diffuse correlation spectroscopy (DCS) has been widely explored for its ability to measure cerebral blood flow (CBF), however, mostly under the assumption that the human head is homogenous. In addition to CBF, knowledge of extracerebral layers, such as skull thickness, can be informative and crucial for patient with brain complications such as traumatic brain injuries. To bridge the gap, this study explored the feasibility of simultaneously extracting skull thickness and flow in the cortex layer using DCS. We validated a two-layer analytical model that assumed the skull as top layer with a finite thickness and the brain cortex as bottom layer with semi-infinite geometry. The model fitted for thickness of the top layer and flow of the bottom layer, while assumed other parameters as constant. The accuracy of the two-layer model was tested against the conventional single-layer model using measurements from custom made two-layer phantoms mimicking skull and brain. We found that the fitted top layer thickness at each source detector (SD) distance is correlated with the expected thickness. For the fitted bottom layer flow, the two-layer model fits relatively consistent flow across all top layer thicknesses. In comparison, the conventional one-layer model increasingly underestimates the bottom layer flow as top layer thickness increases. The overall accuracy of estimating first layer thickness and flow depends on the SD distance in relationship to first layer thickness. Lastly, we quantified the influence of uncertainties in the optical properties of each layer. We found that uncertainties in the optical properties only mildly influence the fitted thickness and flow. In this work we demonstrate the feasibility of simultaneously extracting of layer thickness and flow using a two-layer DCS model. Findings from this work may introduce a robust and cost-effective approach towards simultaneous bedside assessment of skull thickness and cerebral blood flow.

Shortwave infrared spatial frequency domain imaging for non-invasive measurement of tissue and blood optical properties

SIGNIFICANCE

The shortwave infrared (SWIR) optical window (∼900 to 2000 nm) has attracted interest for deep tissue imaging due to the lower scattering of light. SWIR spatial frequency domain imaging (SWIR SFDI) provides wide-field tissue optical property measurements in this wavelength band. Key design and performance characteristics, such as portability, wavelength selection, measurement resolution, and the effect of skin have not yet been addressed for SWIR SFDI.

AIM

To fabricate and characterize a SWIR SFDI system for clinical use.

APPROACH

The optimal choice of wavelengths was identified based on optical property uncertainty estimates and imaging depth. A compact light-emitting diode-based dual wavelength SWIR SFDI system was fabricated. A two-layer inverse model was developed to account for the layered structure of skin. Performance was validated using tissue-simulating phantoms and in-vivo measurements from three healthy subjects.

RESULTS

The SWIR SFDI system had a μs' resolution of at least 0.03  mm  -  1 at 880 nm and 0.02  mm  -  1 at 1100 nm. The two-layer inverse model reduced the error in deeper layer μs' extractions by at least 24% in the phantom study. The two-layer model also increased the contrast between superficial vessels and the surrounding tissue for in-vivo measurements.

CONCLUSION

The clinic-ready SWIR SFDI device is sensitive to small optical property alterations in diffuse media, provides enhanced accuracy in quantifying optical properties in the deeper layers in phantoms, and provided enhanced contrast of subcutaneous blood vessels.

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.

Spatial frequency domain imaging for monitoring immune-mediated chemotherapy treatment response and resistance in a murine breast cancer model

Spatial Frequency Domain Imaging (SFDI) can provide longitudinal, label-free, and widefield hemodynamic and scattering measurements of murine tumors in vivo. Our previous work has shown that the reduced scattering coefficient (μ's) at 800 nm, as well as the wavelength dependence of scattering, both have prognostic value in tracking apoptosis and proliferation during treatment with anti-cancer therapies. However, there is limited work in validating these optical biomarkers in clinically relevant tumor models that manifest specific treatment resistance mechanisms that mimic the clinical setting. It was recently demonstrated that metronomic dosing of cyclophosphamide induces a strong anti-tumor immune response and tumor volume reduction in the E0771 murine breast cancer model. This immune activation mechanism can be blocked with an IFNAR-1 antibody, leading to treatment resistance. Here we present a longitudinal study utilizing SFDI to monitor this paired responsive-resistant model for up to 30 days of drug treatment. Mice receiving the immune modulatory metronomic cyclophosphamide schedule had a significant increase in tumor optical scattering compared to mice receiving cyclophosphamide in combination with the IFNAR-1 antibody (9% increase vs 10% decrease on day 5 of treatment, p < 0.001). The magnitude of these differences increased throughout the duration of treatment. Additionally, scattering changes on day 4 of treatment could discriminate responsive versus resistant tumors with an accuracy of 78%, while tumor volume had an accuracy of only 52%. These results validate optical scattering as a promising prognostic biomarker that can discriminate between treatment responsive and resistant tumor models.

Type-I interferon signaling is essential for robust metronomic chemo-immunogenic tumor regression in murine breast cancer

Many patients with breast cancer have a poor prognosis with limited therapeutic options. Here, we investigated the potential of chemo-immunogenic therapy as an avenue of treatment. We utilized two syngeneic mouse mammary tumor models, 4T1 and E0771, to examine the chemo-immunogenic potential of cyclophosphamide and the mechanistic contributions of cyclophosphamide-activated type-I interferon (IFN) signaling to therapeutic activity. Chemically-activated cyclophosphamide induced robust IFNα/β receptor-1-dependent signaling linked to hundreds of IFN-stimulated gene responses in both cell lines. Further, in 4T1 tumors, cyclophosphamide given on a medium-dose, 6-day intermittent metronomic schedule induced strong IFN signaling but comparatively weak immune cell infiltration associated with long-term tumor growth stasis. Induction of IFN signaling was somewhat weaker in E0771 tumors but was followed by widespread downstream gene responses, robust immune cell infiltration and extensive, prolonged tumor regression. The immune dependence of these effective anti-tumor responses was established by CD8 T-cell immunodepletion, which blocked cyclophosphamide-induced E0771 tumor regression and led to tumor stasis followed by regrowth. Strikingly, IFNα/β receptor-1 antibody blockade was even more effective in preventing E0771 immune cell infiltration and blocked the major tumor regression induced by cyclophosphamide treatment. Type-I IFN signaling is thus essential for the robust chemo-immunogenic response of these tumors to cyclophosphamide administered on a metronomic schedule.

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.

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

SIGNIFICANCE

Sub-diffuse optical properties may serve as useful cancer biomarkers, and wide-field heatmaps of these properties could aid physicians in identifying cancerous tissue. Sub-diffuse spatial frequency domain imaging (sd-SFDI) can reveal such wide-field maps, but the current time cost of experimentally validated methods for rendering these heatmaps precludes this technology from potential real-time applications.

AIM

Our study renders heatmaps of sub-diffuse optical properties from experimental sd-SFDI images in real time and reports these properties for cancerous and normal skin tissue subtypes.

APPROACH

A phase function sampling method was used to simulate sd-SFDI spectra over a wide range of optical properties. A machine learning model trained on these simulations and tested on tissue phantoms was used to render sub-diffuse optical property heatmaps from sd-SFDI images of cancerous and normal skin tissue.

RESULTS

The model accurately rendered heatmaps from experimental sd-SFDI images in real time. In addition, heatmaps of a small number of tissue samples are presented to inform hypotheses on sub-diffuse optical property differences across skin tissue subtypes.

CONCLUSION

These results bring the overall process of sd-SFDI a fundamental step closer to real-time speeds and set a foundation for future real-time medical applications of sd-SFDI such as image guided surgery.

Two-layer spatial frequency domain imaging of compression-induced hemodynamic changes in breast tissue

Significance: Longitudinal tracking of hemodynamic changes in the breast has shown potential for neoadjuvant chemotherapy (NAC) outcome prediction. Spatial frequency domain imaging (SFDI) could be suitable for frequent monitoring of shallow breast tumors, but strong sensitivity to superficial absorbers presents a challenge.

Aim: We investigated the efficacy of a two-layer SFDI inverse model that accounts for varying melanin concentration in the skin to improve discrimination of optical properties of deep tissue of the breast.

Approach: Hemodynamic changes in response to localized breast compression were measured in 13 healthy volunteers using a handheld SFDI device. Epidermis optical thickness was determined based on spectral fitting of the model output and used to calculate subcutaneous optical properties.

Results: Optical properties from a homogeneous model yielded physiologically unreasonable absorption and scattering coefficients for highly pigmented volunteers. The two-layer model compensated for the effect of melanin and yielded properties in the expected range for healthy breast. Extracted epidermal optical thickness was higher for higher Fitzpatrick types. Compression induced a decrease in total hemoglobin consistent with tissue blanching.

Conclusions: The handheld SFDI device and two-layer model show potential for imaging hemodynamic responses that potentially could help predict efficacy of NAC in patients of varying skin tones.

Optical scattering as an early marker of apoptosis during chemotherapy and antiangiogenic therapy in murine models of prostate and breast cancer

Monitoring of the in vivo tumor state to track therapeutic response in real time may help to evaluate new drug candidates, maximize treatment efficacy, and reduce the burden of overtreatment. Current preclinical tumor imaging methods have largely focused on anatomic imaging (e.g., MRI, ultrasound), functional imaging (e.g., FDG-PET), and molecular imaging with exogenous contrast agents (e.g., fluorescence optical tomography). Here we utalize spatial frequency domain imaging (SFDI), a noninvasive, label-free optical technique, for the wide-field quantification of changes in tissue optical scattering in preclinical tumor models during treatment with chemotherapy and antiangiogenic agents. Optical scattering is particularly sensitive to tissue micro-architectural changes, including those that occur during apoptosis, an early indicator of response to cytotoxicity induced by chemotherapy, thermotherapy, cryotherapy, or radiation therapy. We utilized SFDI to monitor responses of PC3/2G7 prostate tumors and E0771 mammary tumors to treatment with cyclophosphamide or the antiangiogenic agent DC101 for up to 49 days. The SFDI-derived scattering amplitude was highly correlated with cleaved caspase-3, a marker of apoptosis (ρ_p = 0.75), while the exponent of the scattering wavelength-dependence correlated with the cell proliferation marker PCNA (ρ_p = 0.69). These optical parameters outperformed tumor volume and several functional parameters (e.g., oxygen saturation and hemoglobin concentration) as an early predictive biomarker of treatment response. Quantitative diffuse optical scattering is thus a promising new early marker of treatment response, which does not require radiation or exogenous contrast agents.

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.

Direct mapping from diffuse reflectance to chromophore concentrations in multi-fx spatial frequency domain imaging (SFDI) with a deep residual network (DRN)

Spatial frequency domain imaging (SFDI) is an emerging technology that enables label-free, non-contact, and wide-field mapping of tissue chromophore contents, such as oxy- and deoxy-hemoglobin concentrations. It has been shown that the use of more than two spatial frequencies (multi-fx ) can vastly improve measurement accuracy and reduce chromophore estimation uncertainties, but real-time multi-fx SFDI for chromophore monitoring has been limited in practice due to the slow speed of available chromophore inversion algorithms. Existing inversion algorithms have to first convert the multi-fx diffuse reflectance to optical absorptions, and then solve a set of linear equations to estimate chromophore concentrations. In this work, we present a deep learning framework, noted as a deep residual network (DRN), that is able to directly map from diffuse reflectance to chromophore concentrations. The proposed DRN is over 10x faster than the state-of-the-art method for chromophore inversion and enables 25x improvement on the frame rate for in vivo real-time oxygenation mapping. The proposed deep learning model will help enable real-time and highly accurate chromophore monitoring with multi-fx SFDI.

Stroke core revealed by tissue scattering using spatial frequency domain imaging

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We present OCT and SFDI as methods to measure the spatial extent of stroke in mice.

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OCT was a reliable predictor of the stroke core in a photothrombosis stroke model.

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SFDI scattering coefficient spatially overlaps with OCT attenuation after stroke.

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Scattering increases following stroke reliably predict ischemic injury.

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SFDI provides a wide-field optical approach to map the stroke core.

Ischemic stroke leads to a reduction or complete loss of blood supply causing injury to brain tissue, which ultimately leads to behavioral impairment. Optical techniques are widely used to study the structural and functional changes that result as a consequence of ischemic stroke both in the acute and chronic phases of stroke recovery. It is currently a challenge to accurately estimate the spatial extent of the infarct without the use of histological parameters however, and in order to follow recovery mechanisms longitudinally at the mesoscopic scale it is essential to know the spatial extent of the stroke core. In this paper we first establish optical coherence tomography (OCT) as a reliable indicator of the stroke core by analyzing signal attenuation and spatially correlating it with the infarct, determined by staining with triphenyl-tetrazolium chloride (TTC). We then introduce spatial frequency domain imaging (SFDI) as a mesoscopic optical technique that can be used to accurately measure the infarct spatial extent by exploiting changes in optical scattering that occur as a consequence of ischemic stroke. Additionally, we follow the progression of ischemia through the acute and sub-acute phases of stroke recovery using both OCT and SFDI and show a consistently high spatial overlap in estimating infarct location. The use of SFDI in assessing infarct location will allow longitudinal studies targeted at following functional recovery mechanisms on a mesoscopic level without having to sacrifice the mouse acutely.

Rapid tissue oxygenation mapping from snapshot structured-light images with adversarial deep learning

SIGNIFICANCE

Spatial frequency-domain imaging (SFDI) is a powerful technique for mapping tissue oxygen saturation over a wide field of view. However, current SFDI methods either require a sequence of several images with different illumination patterns or, in the case of single-snapshot optical properties (SSOP), introduce artifacts and sacrifice accuracy.

AIM

We introduce OxyGAN, a data-driven, content-aware method to estimate tissue oxygenation directly from single structured-light images.

APPROACH

OxyGAN is an end-to-end approach that uses supervised generative adversarial networks. Conventional SFDI is used to obtain ground truth tissue oxygenation maps for ex vivo human esophagi, in vivo hands and feet, and an in vivo pig colon sample under 659- and 851-nm sinusoidal illumination. We benchmark OxyGAN by comparing it with SSOP and a two-step hybrid technique that uses a previously developed deep learning model to predict optical properties followed by a physical model to calculate tissue oxygenation.

RESULTS

When tested on human feet, cross-validated OxyGAN maps tissue oxygenation with an accuracy of 96.5%. When applied to sample types not included in the training set, such as human hands and pig colon, OxyGAN achieves a 93% accuracy, demonstrating robustness to various tissue types. On average, OxyGAN outperforms SSOP and a hybrid model in estimating tissue oxygenation by 24.9% and 24.7%, respectively. Finally, we optimize OxyGAN inference so that oxygenation maps are computed ∼10 times faster than previous work, enabling video-rate, 25-Hz imaging.

CONCLUSIONS

Due to its rapid acquisition and processing speed, OxyGAN has the potential to enable real-time, high-fidelity tissue oxygenation mapping that may be useful for many clinical applications.

Shortwave-infrared meso-patterned imaging enables label-free mapping of tissue water and lipid content

Water and lipids are key participants in many biological processes, but there are few non-invasive methods that provide quantification of these components in vivo, and none that can isolate and quantify lipids in the blood. Here we develop a new imaging modality termed shortwave infrared meso-patterned imaging (SWIR-MPI) to provide label-free, non-contact, spatial mapping of water and lipid concentrations in tissue. The method utilizes patterned hyperspectral illumination to target chromophore absorption bands in the 900-1,300 nm wavelength range. We use SWIR-MPI to monitor clinically important physiological processes including edema, inflammation, and tumor lipid heterogeneity in preclinical models. We also show that SWIR-MPI can spatially map blood-lipids in humans, representing an example of non-invasive and contact-free measurements of in vivo blood lipids. Together, these results highlight the potential of SWIR-MPI to enable new capabilities in fundamental studies and clinical monitoring of major conditions including obesity, cancer, and cardiovascular disease.

Spatial frequency domain imaging for the longitudinal monitoring of vascularization during mouse femoral graft healing

Allograft is the current gold standard for treating critical-sized bone defects. However, allograft healing is usually compromised partially due to poor host-mediated vascularization. In the efforts towards developing new methods to enhance allograft healing, a non-terminal technique for monitoring the vascularization is needed in pre-clinical mouse models. In this study, we developed a non-invasive instrument based on spatial frequency domain imaging (SFDI) for longitudinal monitoring of the mouse femoral graft healing. SFDI technique provided total hemoglobin concentration (THC) and oxygen saturation (StO2) of the graft and the surrounding soft tissues. SFDI measurements were performed from 1 day before to 44 days after graft transplantation. Autograft, another type of bone graft with higher vascularization potential was also measured as a comparison to allograft. For both grafts, the overall temporal changes of the measured THC agreed with the physiological expectations of vascularization timeline during bone healing. A significantly greater increase in THC was observed in the autograft group compared to the allograft group, which agreed with the expectation that allografts have more compromised vascularization.

Wide-field optical spectroscopy system integrating reflectance and spatial frequency domain imaging to measure attenuation-corrected intrinsic tissue fluorescence in radical prostatectomy specimens

The development of a multimodal optical imaging system is presented that integrates endogenous fluorescence and diffuse reflectance spectroscopy with single-wavelength spatial frequency domain imaging (SFDI) and surface profilometry. The system images specimens at visible wavelengths with a spatial resolution of 70 µm, a field of view of 25 cm2 and a depth of field of ∼1.5 cm. The results of phantom experiments are presented demonstrating the system retrieves absorption and reduced scattering coefficient maps using SFDI with <6% reconstruction errors. A phase-shifting profilometry technique is implemented and the resulting 3-D surface used to compute a geometric correction ensuring optical properties reconstruction errors are maintained to <6% in curved media with height variations <20 mm. Combining SFDI-computed optical properties with data from diffuse reflectance spectra is shown to correct fluorescence using a model based on light transport in tissue theory. The system is used to image a human prostate, demonstrating its ability to distinguish prostatic tissue (anterior stroma, hyperplasia, peripheral zone) from extra-prostatic tissue (urethra, ejaculatory ducts, peri-prostatic tissue). These techniques could be integrated in robotic-assisted surgical systems to enhance information provided to surgeons and improve procedural accuracy by minimizing the risk of damage to extra-prostatic tissue during radical prostatectomy procedures and eventually detect residual cancer.

JP, Austin W, Tabassum SM, Aguénounon E, Tilbury K, Saager RB, Gioux S, Roblyer D. OpenSFDI: an open-source guide for constructing a spatial frequency domain imaging system

Significance: Spatial frequency domain imaging (SFDI) is a diffuse optical measurement technique that can quantify tissue optical absorption (μ_a) and reduced scattering (<inline-formula>μs'</inline-formula>) on a pixel-by-pixel basis. Measurements of μ_a at different wavelengths enable the extraction of molar concentrations of tissue chromophores over a wide field, providing a noncontact and label-free means to assess tissue viability, oxygenation, microarchitecture, and molecular content. We present here openSFDI: an open-source guide for building a low-cost, small-footprint, three-wavelength SFDI system capable of quantifying μ_a and <inline-formula>μs'</inline-formula> as well as oxyhemoglobin and deoxyhemoglobin concentrations in biological tissue. The companion website provides a complete parts list along with detailed instructions for assembling the openSFDI system.<p> Aim: We describe the design of openSFDI and report on the accuracy and precision of optical property extractions for three different systems fabricated according to the instructions on the openSFDI website.</p> <p> Approach: Accuracy was assessed by measuring nine tissue-simulating optical phantoms with a physiologically relevant range of μ_a and <inline-formula>μs'</inline-formula> with the openSFDI systems and a commercial SFDI device. Precision was assessed by repeatedly measuring the same phantom over 1 h.</p> <p> Results: The openSFDI systems had an error of 0  ±  6  %   in μ_a and -2  ±  3  %   in <inline-formula>μs'</inline-formula>, compared to a commercial SFDI system. Bland-Altman analysis revealed the limits of agreement between the two systems to be   ±  0.004  mm^-  1 for μ_a and -0.06 to 0.1  mm^-  1 for <inline-formula>μs'</inline-formula>. The openSFDI system had low drift with an average standard deviation of 0.0007  mm^-  1 and 0.05  mm^-  1 in μ_a and <inline-formula>μs'</inline-formula>, respectively.</p>,<p> Conclusion: The openSFDI provides a customizable hardware platform for research groups seeking to utilize SFDI for quantitative diffuse optical imaging.</p>

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.

Hyperspectral imaging in the spatial frequency domain with a supercontinuum source

We introduce a method for quantitative hyperspectral optical imaging in the spatial frequency domain (hs-SFDI) to image tissue absorption (μa) and reduced scattering (μs') parameters over a broad spectral range. The hs-SFDI utilizes principles of spatial scanning of the spectrally dispersed output of a supercontinuum laser that is sinusoidally projected onto the tissue using a digital micromirror device. A scientific complementary metal-oxide-semiconductor camera is used for capturing images that are demodulated and analyzed using SFDI computational models. The hs-SFDI performance is validated using tissue-simulating phantoms over a range of μa and μs' values. Quantitative hs-SFDI images are obtained from an ex-vivo beef sample to spatially resolve concentrations of oxy-, deoxy-, and met-hemoglobin, as well as water and fat fractions. Our results demonstrate that the hs-SFDI can quantitatively image tissue optical properties with 1000 spectral bins in the 580- to 950-nm range over a wide, scalable field of view. With an average accuracy of 6.7% and 12.3% in μa and μs', respectively, compared to conventional methods, hs-SFDI offers a promising approach for quantitative hyperspectral tissue optical imaging.

Optical sampling depth in the spatial frequency domain

We present a Monte Carlo (MC) method to determine depth-dependent probability distributions of photon visitation and detection for optical reflectance measurements performed in the spatial frequency domain (SFD). These distributions are formed using an MC simulation for radiative transport that utilizes a photon packet weighting procedure consistent with the two-dimensional spatial Fourier transform of the radiative transport equation. This method enables the development of quantitative metrics for SFD optical sampling depth in layered tissue and its dependence on both tissue optical properties and spatial frequency. We validate the computed depth-dependent probability distributions using SFD measurements in a layered phantom system with a highly scattering top layer of variable thickness supported by a highly absorbing base layer. We utilize our method to establish the spatial frequency-dependent optical sampling depth for a number of tissue types and also provide a general tool to determine such depths for tissues of arbitrary optical properties.

Diffuse and nonlinear imaging of multiscale vascular parameters for in vivo monitoring of preclinical mammary tumors

Diffuse optical imaging (DOI) techniques provide a wide-field or macro assessment of the functional tumor state and have shown substantial promise for monitoring treatment efficacy in cancer. Conversely, intravital microscopy provides a high-resolution view of the tumor state and has played a key role in characterizing treatment response in the preclinical setting. There has been little prior work in investigating how the macro and micro spatial scales can be combined to develop a more comprehensive and translational view of treatment response. To address this, a new multiscale preclinical imaging technique called diffuse and nonlinear imaging (DNI) was developed. DNI combines multiphoton microscopy with spatial frequency domain imaging (SFDI) to provide multiscale data sets of tumor microvascular architecture coregistered within wide-field hemodynamic maps. A novel method was developed to match the imaging depths of both modalities by utilizing informed SFDI spatial frequency selection. An in vivo DNI study of murine mammary tumors revealed multiscale relationships between tumor oxygen saturation and microvessel diameter, and tumor oxygen saturation and microvessel length (|Pearson's ρ| ≥ 0.5, P < 0.05). Going forward, DNI will be uniquely enabling for the investigation of multiscale relationships in tumors during treatment.

Spatial frequency domain imaging in 2019: principles, applications, and perspectives

Spatial frequency domain imaging (SFDI) has witnessed very rapid growth over the last decade, owing to its unique capabilities for imaging optical properties and chromophores over a large field-of-view and in a rapid manner. We provide a comprehensive review of the principles of this imaging method as of 2019, review the modeling of light propagation in this domain, describe acquisition methods, provide an understanding of the various implementations and their practical limitations, and finally review applications that have been published in the literature. Importantly, we also introduce a group effort by several key actors in the field for the dissemination of SFDI, including publications, advice in hardware and implementations, and processing code, all freely available online.

Spatial frequency domain imaging in 2019: principles, applications, and perspectives

Spatial frequency domain imaging (SFDI) has witnessed very rapid growth over the last decade, owing to its unique capabilities for imaging optical properties and chromophores over a large field-of-view and in a rapid manner. We provide a comprehensive review of the principles of this imaging method as of 2019, review the modeling of light propagation in this domain, describe acquisition methods, provide an understanding of the various implementations and their practical limitations, and finally review applications that have been published in the literature. Importantly, we also introduce a group effort by several key actors in the field for the dissemination of SFDI, including publications, advice in hardware and implementations, and processing code, all freely available online.

Microvascular imaging of the skin

Despite our understanding that the microvasculature plays a multifaceted role in the development and progression of various conditions, we know little about the extent of this involvement. A need exists for non-invasive, clinically meaningful imaging modalities capable of elucidating microvascular information to aid in our understanding of disease, and to aid in the diagnosis/monitoring of disease for more patient-specific care. In this review article, a number of imaging techniques are summarized that have been utilized to investigate the microvasculature of skin, along with their advantages, disadvantages and future perspectives in preclinical and clinical settings. These techniques include dermoscopy, capillaroscopy, Doppler sonography, laser Doppler flowmetry (LDF) and perfusion imaging, laser speckle contrast imaging (LSCI), optical coherence tomography (OCT), including its Doppler and dynamic variant and the more recently developed OCT angiography (OCTA), photoacoustic imaging, and spatial frequency domain imaging (SFDI). Attention is largely, but not exclusively, placed on optical imaging modalities that use intrinsic optical signals to contrast the microvasculature. We conclude that whilst each imaging modality has been successful in filling a particular niche, there is no one, all-encompassing modality without inherent flaws. Therefore, the future of cutaneous microvascular imaging may lie in utilizing a multi-modal approach that will counter the disadvantages of individual systems to synergistically augment our imaging capabilities.

Deep learning model for ultrafast multifrequency optical property extractions for spatial frequency domain imaging

Spatial frequency domain imaging (SFDI) is emerging as an important new method in biomedical imaging due to its ability to provide label-free, wide-field tissue optical property maps. Most prior SFDI studies have utilized two spatial frequencies (2-f_x) for optical property extractions. The use of more than two frequencies (multi-f_x) can vastly improve the accuracy and reduce uncertainties in optical property estimates for some tissue types, but it has been limited in practice due to the slow speed of available inversion algorithms. We present a deep learning solution that eliminates this bottleneck by solving the multi-f_x inverse problem 300× to 100,000× faster, with equivalent or improved accuracy compared to competing methods. The proposed deep learning inverse model will help to enable real-time and highly accurate tissue measurements with SFDI.

Low-cost compact multispectral spatial frequency domain imaging prototype for tissue characterization

We present a low-cost, compact, and multispectral spatial frequency domain imaging prototype. Illumination components, including 9 LEDs (660 nm - 950 nm) placed on a custom-designed printed circuit board, linear and rotational motors, a printed sinusoidal pattern, and collimation and projection optics as well as the detection components are incorporated in a compact custom-designed 3D-printed probe. Reconstruction of absorption and reduced scattering coefficients is evaluated via imaging tissue mimicking phantoms and potentials of the probe for biological tissue imaging are evaluated via imaging human ovarian tissue ex vivo.

Review of structured light in diffuse optical imaging

Diffuse optical imaging probes deep living tissue enabling structural, functional, metabolic, and molecular imaging. Recently, due to the availability of spatial light modulators, wide-field quantitative diffuse optical techniques have been implemented, which benefit greatly from structured light methodologies. Such implementations facilitate the quantification and characterization of depth-resolved optical and physiological properties of thick and deep tissue at fast acquisition speeds. We summarize the current state of work and applications in the three main techniques leveraging structured light: spatial frequency-domain imaging, optical tomography, and single-pixel imaging. The theory, measurement, and analysis of spatial frequency-domain imaging are described. Then, advanced theories, processing, and imaging systems are summarized. Preclinical and clinical applications on physiological measurements for guidance and diagnosis are summarized. General theory and method development of tomographic approaches as well as applications including fluorescence molecular tomography are introduced. Lastly, recent developments of single-pixel imaging methodologies and applications are reviewed.

Optical property uncertainty estimates for spatial frequency domain imaging

Spatial frequency domain imaging (SFDI) is a wide-field diffuse optical imaging modality that has attracted considerable interest in recent years. Typically, diffuse reflectance measurements of spatially modulated light are used to quantify the optical absorption and reduced scattering coefficients of tissue, and with these, chromophore concentrations are extracted. However, uncertainties in estimated absorption and reduced scattering coefficients are rarely reported, and we know of no method capable of providing these when look-up table (LUT) algorithms are used to recover the optical properties. We present a method to generate optical property uncertainty estimates from knowledge of diffuse reflectance measurement errors. By employing the Cramér-Rao bound, we can quickly and efficiently explore theoretical SFDI performance as a function of spatial frequencies and sample optical properties, allowing us to optimize spatial frequency selection for a given application. In practice, we can also obtain useful uncertainty estimates for optical properties recovered with a two-frequency LUT algorithm, as we demonstrate with tissue-simulating phantom and in vivo experiments. Finally, we illustrate how absorption coefficient uncertainties can be propagated forward to yield uncertainties for chromophore concentrations, which could significantly impact the interpretation of experimental results.

Reference-free determination of tissue absorption coefficient by modulation transfer function characterization in spatial frequency domain

Background

Spatial frequency domain (SFD) measurement allows rapid and non-contact wide-field imaging of the tissue optical properties, thus has become a potential tool for assessing physiological parameters and therapeutic responses during photodynamic therapy of skin diseases. The conventional SFD measurement requires a reference measurement within the same experimental scenario as that for a test one to calibrate mismatch between the real measurements and the model predictions. Due to the individual physical and geometrical differences among different tissues, organs and patients, an ideal reference measurement might be unavailable in clinical trials. To address this problem, we present a reference-free SFD determination of absorption coefficient that is based on the modulation transfer function (MTF) characterization.

Methods

Instead of the absolute amplitude that is used in the conventional SFD approaches, we herein employ the MTF to characterize the propagation of the modulated lights in tissues. With such a dimensionless relative quantity, the measurements can be naturally corresponded to the model predictions without calibrating the illumination intensity. By constructing a three-dimensional database that portrays the MTF as a function of the optical properties (both the absorption coefficient μ_a and the reduced scattering coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu^{\prime}_{s}$$\end{document}μs′) and the spatial frequency, a look-up table approach or a least-square curve-fitting method is readily applied to recover the absorption coefficient from a single frequency or multiple frequencies, respectively.

Results

Simulation studies have verified the feasibility of the proposed reference-free method and evaluated its accuracy in the absorption recovery. Experimental validations have been performed on homogeneous tissue-mimicking phantoms with μ_a ranging from 0.01 to 0.07 mm⁻¹ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu^{\prime}_{s}$$\end{document}μs′ = 1.0 or 2.0 mm⁻¹. The results have shown maximum errors of 4.86 and 7% for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu^{\prime}_{s}$$\end{document}μs′ = 1.0 mm⁻¹ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu^{\prime}_{s}$$\end{document}μs′ = 2.0 mm⁻¹, respectively. We have also presented quantitative ex vivo imaging of human lung cancer in a subcutaneous xenograft mouse model for further validation, and observed high absorption contrast in the tumor region.

Conclusions

The proposed method can be applied to the rapid and accurate determination of the absorption coefficient, and better yet, in a reference-free way. We believe this reference-free strategy will facilitate the clinical translation of the SFD measurement to achieve enhanced intraoperative hemodynamic monitoring and personalized treatment planning in photodynamic therapy.

Feasibility of spatial frequency-domain imaging for monitoring palpable breast lesions

In breast cancer diagnosis and therapy monitoring, there is a need for frequent, noninvasive disease progression evaluation. Breast tumors differ from healthy tissue in mechanical stiffness as well as optical properties, which allows optical methods to detect and monitor breast lesions noninvasively. Spatial frequency-domain imaging (SFDI) is a reflectance-based diffuse optical method that can yield two-dimensional images of absolute optical properties of tissue with an inexpensive and portable system, although depth penetration is limited. Since the absorption coefficient of breast tissue is relatively low and the tissue is quite flexible, there is an opportunity for compression of tissue to bring stiff, palpable breast lesions within the detection range of SFDI. Sixteen breast tissue-mimicking phantoms were fabricated containing stiffer, more highly absorbing tumor-mimicking inclusions of varying absorption contrast and depth. These phantoms were imaged with an SFDI system at five levels of compression. An increase in absorption contrast was observed with compression, and reliable detection of each inclusion was achieved when compression was sufficient to bring the inclusion center within ∼12  mm of the phantom surface. At highest compression level, contrasts achieved with this system were comparable to those measured with single source-detector near-infrared spectroscopy.

High-speed spatial frequency domain imaging with temporally modulated light

Spatial frequency domain imaging (SFDI) is a wide-field diffuse optical technique used to obtain optical properties and chromophore concentrations in highly scattering media, such as biological tissue. Here, we present a method for rapidly acquiring multispectral SFDI data by modulating each illumination wavelength at a different temporal frequency. In the remitted signal, each wavelength is temporally demodulated and processed using conventional SFDI techniques. We demonstrate a proof-of-concept system capable of acquiring wide-field maps (2048×1536  pixels, 8.5×6.4  cm) of optical properties at three wavelengths in under 2.5 s. Data acquired by this method show a good agreement with a commercial SFDI imaging system (with an average error of 13% in absorption and 8% in scattering). Additionally, we show that this strategy is insensitive to ambient lighting conditions, making it more practical for clinical translation. In the future, this technique could be expanded to tens or hundreds of wavelengths without increasing acquisition time.

Compressed single pixel imaging in the spatial frequency domain

We have developed compressed sensing single pixel spatial frequency domain imaging (cs-SFDI) to characterize tissue optical properties over a wide field of view ( 35 ?? mm × 35 ?? mm ) using multiple near-infrared (NIR) wavelengths simultaneously. Our approach takes advantage of the relatively sparse spatial content required for mapping tissue optical properties at length scales comparable to the transport scattering length in tissue ( l tr ? 1 ?? mm ) and the high bandwidth available for spectral encoding using a single-element detector. cs-SFDI recovered absorption ( ? a ) and reduced scattering ( ? s ? ) coefficients of a tissue phantom at three NIR wavelengths (660, 850, and 940 nm) within 7.6% and 4.3% of absolute values determined using camera-based SFDI, respectively. These results suggest that cs-SFDI can be developed as a multi- and hyperspectral imaging modality for quantitative, dynamic imaging of tissue optical and physiological properties.