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Significance: Light-field fundus photography has the potential to be a new milestone in ophthalmology. Up-to-date publications show only unsatisfactory image quality, preventing the use of depth measurements. We show that good image quality and, consequently, reliable depth measurements are possible, and we investigate the current challenges of this novel technology. Aim: We investigated whether light field (LF) imaging of the retina provides depth information, on which structures the depth is estimated, which illumination wavelength should be used, whether deeper layers are measurable, and what kinds of artifacts occur. Approach: The technical setup, a mydriatic fundus camera with an LF imager, and depth estimation were validated by an eye model and in vivo measurements of three healthy subjects and three subjects with suspected glaucoma. Comparisons between subjects and the corresponding optical coherence tomography (OCT) measurements were used for verification of the depth estimation. Results: This LF setup allowed for three-dimensional one-shot imaging and depth estimation of the optic disc with green light. In addition, a linear relationship was found between the depth estimates of the OCT and those of the setup developed here. This result is supported by the eye model study. Deeper layers were not measurable. Conclusions: If image artifacts can be handled, LF technology has the potential to help diagnose and monitor glaucoma risk at an early stage through a rapid, cost-effective one-shot technology.

On page 610 of the September/ October 1983 issue the authors (Blouke et al.) state that the MTF of the CCD (as a discrete sample imager) ideally "should exhibit a sin(f/fN)/(f/fN) behavior, where f and fN are the spatial frequency of the scene and the spatial Nyquist frequency, respectively." Application of this formula would give values of 84 percent modulation at Nyquist and 45 percent at twice Nyquist (although the latter would be of no real significance). In fact, the relationship (for a fully-active pixel-area device) is a sinc function

Significance: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are both commonly used methodologies for neuronal source reconstruction. While EEG has high temporal resolution (millisecond-scale), its spatial resolution is on the order of centimeters. On the other hand, in comparison to EEG, fNIRS, or diffuse optical tomography (DOT), when used for source reconstruction, can achieve relatively high spatial resolution (millimeter-scale), but its temporal resolution is poor because the hemodynamics that it measures evolve on the order of several seconds. This has important neuroscientific implications: e.g., if two spatially close neuronal sources are activated sequentially with only a small temporal separation, single-modal measurements using either EEG or DOT alone would fail to resolve them correctly. Aim: We attempt to address this issue by performing joint EEG and DOT neuronal source reconstruction. Approach: We propose an algorithm that utilizes DOT reconstruction as the spatial prior of EEG reconstruction, and demonstrate the improvements using simulations based on the ICBM152 brain atlas. Results: We show that neuronal sources can be reconstructed with higher spatiotemporal resolution using our algorithm than using either modality individually. Further, we study how the performance of the proposed algorithm can be affected by the locations of the neuronal sources, and how the performance can be enhanced by improving the placement of EEG electrodes and DOT optodes. Conclusions: We demonstrate using simulations that two sources separated by 2.3-3.3 cm and 50 ms can be recovered accurately using the proposed algorithm by suitably combining EEG and DOT, but not by either in isolation. We also show that the performance can be enhanced by optimizing the electrode and optode placement according to the locations of the neuronal sources.

Significance: To expand our understanding of the roles of astrocytes in neural circuits, there is a need to develop optical tools tailored specifically to capture their complex spatiotemporal Ca2+ dynamics. This interest is not limited to 2D, but to multiple depths. Aim: The focus of our work was to design and evaluate the optical performance of an enhanced version of a two-photon (2P) microscope with the addition of a deformable mirror (DM)-based axial scanning system for live mammalian brain imaging. Approach: We used a DM to manipulate the beam wavefront by applying different defocus terms to cause a controlled axial shift of the image plane. The optical design and performance were evaluated by an analysis of the optical model, followed by an experimental characterization of the implemented instrument. Results: Key questions related to this instrument were addressed, including impact of the DM curvature change on vignetting, field of view size, image plane flatness, wavefront error, and point spread function. The instrument was used for imaging several neurobiological samples at different depths, including fixed brain slices and in vivo mouse cerebral cortex. Conclusions: Our implemented instrument was capable of recording z-stacks of 53 μm in depth with a fine step size, parameters that make it useful for astrocyte biology research. Future work includes adaptive optics and intensity normalization.

The study of image evaluation is a complex subject requiring the simultaneous consideration of the object, optical system, detector, and mechanism for processing the detector's signals. Image quality for any given photo-optical system depends upon such diverse considerations as granularity, light scattering, numerical aperture, tone reproduction, the degree of correction in the image-forming optics, etc.

Significance: Transcranial photobiomodulation (PBM) is a noninvasive neuromodulation technique capable of producing changes in the mitochondrial cytochrome c-oxidase (CCO) activity of neurons. Although the application of PBM in clinical practice and as a neurophysiological tool is increasing, less is known about how different treatment time intervals may result in different outcomes. Aim: We evaluated the effects of different PBM treatment intervals on brain metabolic activity through the CCO and proto-oncogene expression (c-Fos). Approach: We studied PBM effects on brain CCO and c-Fos expression in three groups of animals: Control (CN, n = 8), long interval PBM treatment (LI, n = 5), and short interval PBM treatment (SI, n = 5). Results: Increased CCO activity in the LI group, compared to the SI and CN groups, was found in the prefrontal cortices, dorsal and ventral striatum, and hippocampus. Regarding c-Fos expression, we found a significant increase in the SI group compared to LI and CN, whereas LI showed increased c-Fos expression compared to CN in the cingulate and infralimbic cortices. Conclusions: We show the effectiveness of different PBM interval schedules in increasing brain metabolic activity or proto-oncogene expression.

Significance: Solutions for group-level analysis of connectivity from fNIRS observations exist, but groupwise explorative analysis with classical solutions is often cumbersome. Manifold-based solutions excel at data exploration, but there are infinite surfaces crossing the observations cloud of points. Aim: We aim to provide a systematic choice of surface for a manifold-based analysis of connectivity at group level with small surface interpolation error. Approach: This research introduces interpolated functional manifold (IFM). IFM builds a manifold from reconstructed changes in concentrations of oxygenated ΔcHbO2 and reduced ΔcHbR hemoglobin species by means of radial basis functions (RBF). We evaluate the root mean square error (RMSE) associated to four families of RBF. We validated our model against psychophysiological interactions (PPI) analysis using the Jaccard index (JI). We demonstrate the usability in an experimental dataset of surgical neuroergonomics. Results: Lowest interpolation RMSE was 1.26e − 4 ± 1.32e − 8 for ΔcHbO2 [A.U.] and 4.30e − 7 ± 2.50e − 13 [A.U.] for ΔcHbR. Agreement with classical group analysis was JI = 0.89 ± 0.01 for ΔcHbO2. Agreement with PPI analysis was JI = 0.83 ± 0.07 for ΔcHbO2 and JI = 0.77 ± 0.06 for ΔcHbR. IFM successfully decoded group differences [ANOVA: ΔcHbO2: F ( 2,117 ) = 3.07; p

Significance: Speech processing tasks can be used to assess the integrity and health of many functional and structural aspects of the brain. Despite the potential merits of such behavioral tests as clinical assessment tools, however, the underlying neural substrates remain relatively unclear. Aim: We aimed to obtain a more in-depth portrait of hemispheric asymmetry during dichotic listening tasks at the level of the prefrontal cortex, where prior studies have reported inconsistent results. Approach: To avoid central confounds that limited previous studies, we used diffuse correlation spectroscopy to optically monitor cerebral blood flow (CBF) in the dorsolateral prefrontal cortex during dichotic listening tasks in human subjects. Results: We found that dichotic listening tasks elicited hemispheric asymmetries in both amplitude as well as kinetics. When listening task blocks were repeated, there was an accommodative reduction in the response amplitude of the left, but not the right hemisphere. Conclusions: These heretofore unobserved trends depict a more nuanced portrait of the functional asymmetry that has been observed previously. To our knowledge, these results additionally represent the first direct measurements of CBF during a speech processing task recommended by the American Speech-Language-Hearing Association for diagnosing auditory processing disorders.

Significance: The optical properties of biological samples provide information about the structural characteristics of the tissue and any changes arising from pathological conditions. Optical coherence tomography (OCT) has proven to be capable of extracting tissue’s optical properties using a model that combines the exponential decay due to tissue scattering and the axial point spread function that arises from the confocal nature of the detection system, particularly for higher numerical aperture (NA) measurements. A weakness in estimating the optical properties is the inter-parameter cross-talk between tissue scattering and the confocal parameters defined by the Rayleigh range and the focus depth. Aim: In this study, we develop a systematic method to improve the characterization of optical properties with high-NA OCT. Approach: We developed a method that spatially parameterizes the confocal parameters in a previously established model for estimating the optical properties from the depth profiles of high-NA OCT. Results: The proposed parametrization model was first evaluated on a set of intralipid phantoms and then validated using a low-NA objective in which cross-talk from the confocal parameters is negligible. We then utilize our spatially parameterized model to characterize optical property changes introduced by a tissue index matching process using a simple immersion agent, 2,2’-thiodiethonal. Conclusions: Our approach improves the confidence of parameter estimation by reducing the degrees of freedom in the non-linear fitting model.

Significance: Dystonia is a dynamic and complex disorder. Real-time analysis of brain activity during motor tasks may increase our knowledge on its pathophysiology. Functional near-infrared spectroscopy (fNIRS) is a noninvasive method that enables the measurement of cortical hemodynamic activity in unconstrained environments. Aim: We aimed to explore the feasibility of using fNIRS for the study of task-related brain activity in dystonia. Task-related functional magnetic resonance imaging (fMRI) and resting-state functional connectivity were also analyzed. Approach: Patients with idiopathic right-upper limb dystonia and controls were assessed through nonsimultaneous fMRI and fNIRS during a finger-tapping task. Seed-based connectivity analysis of resting-state fMRI was performed in both groups. Results: The fMRI results suggest nonspecific activation of the cerebellum and occipital lobe in dystonia patients during the finger-tapping task with the affected hand. Moreover, fNIRS data show lower activation in terms of oxyhemoglobin and total hemoglobin in the frontal, ipsilateral cortex, and somatosensory areas during this task. In dystonia, both fMRI and fNIRS data resulted in hypoactivation of the frontal cortex during finger tapping with both hands simultaneously. Resting-state functional connectivity analysis suggests that the cerebellar somatomotor network in dystonia has an increased correlation with the medial prefrontal cortex and the paracingulate gyrus. Conclusions: These data suggest that unbalanced activation of the cerebellum, somatosensory, and frontal cortical areas are associated with dystonia. To our knowledge, this is the first study using fNIRS to explore the pathophysiology of dystonia. We show that fNIRS and fMRI are complementary methods and highlight the potential of fNIRS for the study of dystonia and other movement disorders as it can overcome movement restrictions, enabling experiments in more naturalistic conditions.

Significance: Cerebral blood flow is an important biomarker of brain health and function as it regulates the delivery of oxygen and substrates to tissue and the removal of metabolic waste products. Moreover, blood flow changes in specific areas of the brain are correlated with neuronal activity in those areas. Diffuse correlation spectroscopy (DCS) is a promising noninvasive optical technique for monitoring cerebral blood flow and for measuring cortex functional activation tasks. However, the current state-of-the-art DCS adoption is hindered by a trade-off between sensitivity to the cortex and signal-to-noise ratio (SNR). Aim: We aim to develop a scalable method that increases the sensitivity of DCS instruments. Approach: We report on a multispeckle DCS (mDCS) approach that is based on a 1024-pixel single-photon avalanche diode (SPAD) camera. Our approach is scalable to >100,000 independent speckle measurements since large-pixel-count SPAD cameras are becoming available, owing to the investments in LiDAR technology for automotive and augmented reality applications. Results: We demonstrated a 32-fold increase in SNR with respect to traditional single-speckle DCS. Conclusion: A mDCS system that is based on a SPAD camera serves as a scalable method toward high-sensitivity DCS measurements, thus enabling both high sensitivity to the cortex and high SNR.

Significance: Numerous optical imaging and spectroscopy techniques are used to study the tissue-optical properties; the majority of them are limited in information regarding the penetration depth. A simple, safe, easily applicable diagnostic technique is required to get deeper tissue information in a multilayer structure. Aim: A fiber-based diffuse reflectance (DR) technique is used to extract and quantify the bottom layer absorption coefficients in two-layer (2L) tissue-mimicking solid phantoms. We determine the Indian black ink concentrations in a deep-hidden layer that is sandwiched between agar and silicone-based phantom layers. Approach: A fiber-based DR experiment was performed to study the optical properties of the tissue at higher penetration depth, with different fiber core diameters and a constant numerical aperture (0.5 NA). The optimal core diameter of the fiber was chosen by measuring solid phantoms. In 2L phantoms, the thickness of the top layer was kept 5.5 mm with a constant absorption and reduced scattering coefficients (μa = 0.045 mm − 1 and μs ′ = 2.622 mm − 1), whereas the absorption coefficients of the bottom layers were varied from 0.014 to 0.037 mm − 1 keeping the μs ′ the same as the top layer. A unique crossover point (Cp) was found in the DR intensity profile against distance. We examined the slope before and after the Cp. These two slopes indicate the difference between the optical properties of the top and bottom layers. Our technique got further verification, as we successfully determined the Cp with different Indian black ink concentrations, placed at the junction between the agar and silicone-based phantom layers. Results: The DR measurements were applied to 2L phantoms. Two different slopes were found in 2L phantoms compared to the one-layer (optical properties equal to the top layer of 2L). We extracted the slopes before and after the Cp in the 2L phantoms. The calculated absorption coefficients before the Cp were 0.014 ± 0.0004, 0.022 ± 0.0003, 0.028 ± 0.0003, and 0.036 ± 0.0014 mm − 1, and the absorption coefficients after the Cp were 0.019 ± 0.0013, 0.013 ± 0.0004, 0.014 ± 0.0006, and 0.031 ± 0.0001 mm − 1, respectively. The calculated absorption coefficients before the Cp were in good agreement with the optical properties of the bottom layer. The calculated absorption coefficients after the Cp were not the same as the top layer. Our DR system successfully determines the crossover points 12.14 ± 0.11 and 11.73 ± 0.15 mm for 70% and 100% ink concentrations placed at the junction of the agar and silicone layers. Conclusions: In a 2L tissue structure, the Cp depends on the absorption coefficients of top and bottom layers and the thickness of the top layer. With the help of the Cp and the absorption coefficients, one can determine the thickness of the top layer or vice versa. The slope value before the Cp in the DR profile allowed us to determine the absorption properties of the bottom layer instead of having the average behavior of the 2L phantom in the far detection range (11.0 to 17.0 mm).

Significance: Two-photon microscopy has become the standard platform for deep-tissue fluorescence imaging. However, the use of point scanning in conventional two-photon microscopy limits the speed of volumetric image acquisition. Aim: To obtain fast and deep volumetric images, we combine two-photon light sheet fluorescence microscopy (2p-LSFM) and axicon imaging that yields an extended depth of field (DOF) in 2p-LSFM. Approach: Axicon imaging is achieved by imposing an axicon lens in the detection part of LSFM. Results: The DOF with axicon imaging is extended more than 20-fold over that of a conventional imaging lens, liberating the synchronized scanning in LSFM. We captured images of dynamic beating hearts and red blood cells in zebrafish larvae at volume acquisition rates up to 30 Hz. Conclusions: We demonstrate the fast three-dimensional imaging capability of 2p-LSFM with axicon imaging by recording the rapid dynamics of physiological processes.

Significance: Peripheral pitting edema is a clinician-administered measure for grading edema. Peripheral edema is graded 0, 1 + , 2 + , 3 + , or 4 + , but subjectivity is a major limitation of this technique. A pilot clinical study for short-wave infrared (SWIR) molecular chemical imaging (MCI) effectiveness as an objective, non-contact quantitative peripheral edema measure is underway. Aim: We explore if SWIR MCI can differentiate populations with and without peripheral edema. Further, we evaluate the technology for correctly stratifying subjects with peripheral edema. Approach: SWIR MCI of shins from healthy subjects and heart failure (HF) patients was performed. Partial least squares discriminant analysis (PLS-DA) was used to discriminate the two populations. PLS regression (PLSR) was applied to assess the ability of MCI to grade edema. Results: Average spectra from edema exhibited higher water absorption than non-edema spectra. SWIR MCI differentiated healthy volunteers from a population representing all pitting edema grades with 97.1% accuracy (N = 103 shins). Additionally, SWIR MCI correctly classified shin pitting edema levels in patients with 81.6% accuracy. Conclusions: Our study successfully achieved the two primary endpoints. Application of SWIR MCI to monitor patients while actively receiving HF treatment is necessary to validate SWIR MCI as an HF monitoring technology.

Significance: Fourier ptychography microscopy (FPM) is a computational optical imaging technology that employs angularly varying illuminations and a phase retrieval algorithm to achieve a wide field of view and high-resolution imaging simultaneously. In the FPM, LED position error will reduce the quality of the reconstructed high-resolution image. To correct the LED positions, current methods consider each of the LED positions as independent and use an optimization algorithm to get each of the positions. When the positional misalignment is large or the search position falls into a local optimal value, the current methods may lack stability and accuracy. Aim: We improve the model of the LED position and propose an accurate and stable two-step correction scheme (tcFPM) to calibrate the LED position error. Approach: The improved LED positions model combines the overall offset, which represents the relative deviation of the LED array and the optical axis, with the slight deviation of each LED’s independent position. In the tcFPM, the overall offset of the LED array is corrected at first, which obtains an approximate value of the overall offset of the LED array. Then the position of each LED is precisely adjusted, which obtains the slight offset of each LED. Results: This LED position error model is more in line with the actual situation. The simulation and experimental results show that the method has high accuracy in correcting the LED position. Furthermore, the reconstruction process of tcFPM is more stable and significantly improves the quality of the reconstruction results, which is compared with some LED position error correction methods. Conclusions: An LED position error correction technology is proposed, which has a stable iterative process and improves the reconstruction accuracy of complex amplitude.

Significance: The Cherenkov emission spectrum overlaps with that of ambient room light sources. Choice of room lighting devices dramatically affects the efficient detection of Cherenkov emission during patient treatment. Aim: To determine optimal room light sources allowing Cherenkov emission imaging in normally lit radiotherapy treatment delivery rooms. Approach: A variety of commercial light sources and long-pass (LP) filters were surveyed for spectral band separation from the red to near-infrared Cherenkov light emitted by tissue. Their effects on signal-to-noise ratio (SNR), Cherenkov to background signal ratio, and image artifacts were quantified by imaging irradiated tissue equivalent phantoms with an intensified time-gated CMOS camera. Results: Because Cherenkov emission from tissue lies largely in the near-infrared spectrum, a controlled choice of ambient light that avoids this spectral band is ideal, along with a camera that is maximally sensitive to it. An RGB LED light source produced the best SNR out of all sources that mimic room light temperature. A 675-nm LP filter on the camera input further reduced ambient light detected (optical density > 3), achieving maximal SNR for Cherenkov emission near 40. Reduction of the room light signal reduced artifacts from specular reflection on the tissue surface and also minimized spurious Cherenkov signals from non-tissue features such as bolus. Conclusions: LP filtering during image acquisition for near-infrared light in tandem with narrow band LED illuminated rooms improves image quality, trading off the loss of red wavelengths for better removal of room light in the image. This spectral filtering is also critically important to remove specular reflection in the images and allow for imaging of Cherenkov emission through clear bolus. Beyond time-gated external beam therapy systems, the spectral separation methods can be utilized for background removal for continuous treatment delivery methods including proton pencil beam scanning systems and brachytherapy.

Significance: Deranged metabolism and dysregulated growth factor signaling are closely associated with abnormal levels of proliferation, a recognized hallmark in tumorigenesis. Fluorescence lifetime imaging microscopy (FLIM) of endogenous nicotinamide adenine dinucleotide (NADH), a key metabolic coenzyme, offers a non-invasive, diagnostic indicator of disease progression, and treatment response. The model-independent phasor analysis approach leverages FLIM to rapidly evaluate cancer metabolism in response to targeted therapy. Aim: We combined lifetime and phasor FLIM analysis to evaluate the influence of human epidermal growth factor receptor 2 (HER2) inhibition, a prevalent cancer biomarker, on both nuclear and cytoplasmic NAD(P)H of two squamous cell carcinoma (SCC) cultures. While better established, the standard lifetime analysis approach is relatively slow and potentially subject to intrinsic fitting errors and model assumptions. Phasor FLIM analysis offers a rapid, model-independent alternative, but the sensitivity of the bound NAD(P)H fraction to growth factor signaling must also be firmly established. Approach: Two SCC cultures with low- and high-HER2 expression, were imaged using multiphoton-excited NAD(P)H FLIM, with and without treatment of the HER2 inhibitor AG825. Cells were challenged with mitochondrial inhibition and uncoupling to investigate AG825’s impact on the overall metabolic capacity. Phasor FLIM and lifetime fitting analyses were compared within nuclear and cytoplasmic compartments to investigate epigenetic and metabolic impacts of HER2 inhibition. Results: NAD(P)H fluorescence lifetime and bound fraction consistently decreased following HER2 inhibition in both cell lines. High-HER2 SCC74B cells displayed a more significant response than low-HER2 SCC74A in both techniques. HER2 inhibition induced greater changes in nuclear than cytoplasmic compartments, leading to an increase in NAD(P)H intensity and concentration. Conclusions: The use of both, complementary FLIM analysis techniques together with quantitative fluorescence intensity revealed consistent, quantitative changes in NAD(P)H metabolism associated with inhibition of growth factor signaling in SCC cell lines. HER2 inhibition promoted increased reliance on oxidative phosphorylation in both cell lines.

Significance: A non-destructive technique for accurately characterizing the spatial distribution of optical properties of soft tissue membranes may give improved outcomes in many tissue engineering applications. Aim: This study aimed to develop a non-destructive macroscopic imaging technique that is sensitive to optical anisotropy, typical of fibrous components in soft tissue membranes, and can address some of the difficulties caused by the complex turbid nature of these tissues. Approach: A near-infrared Mueller matrix imaging polarimeter employing logarithm decomposition was developed and used to conduct transmission measurements of all the polarization properties across the full thickness of bovine pericardium tissue. Results: The full Mueller matrix was measured across a 70 mm×70 mm sample of calf bovine pericardium and revealed significant retardance (linear and circular) and depolarization in this tissue. Regions with a uniform axis of optical anisotropy were identified. Mueller matrix imaging demonstrated that the exhibited circular retardance was sufficient to lead to possible misinterpretation of apparent fiber orientation when using conventional polarization imaging techniques for such tissues. Conclusions: Mueller matrix imaging can identify regional distributions of optical anisotropy in calf bovine pericardium. This new capability is a promising development in non-destructive imaging for tissue selection.

Significance: As linear array transducers are widely used in clinical ultrasound imaging, photoacoustic tomography (PAT) with linear arrays is similarly suitable for clinical applications. However, due to the limited-view problem, a linear array has limited performance and leads to artifacts and blurring, which has hindered its broader application. There is a need to address the limited-view problem in PAT imaging with linear arrays. Aim: We investigate potential approaches for improving PAT reconstruction from linear array, by optimizing the detection geometry and implementing iterative reconstruction. Approach: PAT imaging with a single-array, dual-probe configurations in parallel-shape and L-shape, and square-shape configuration are compared in simulations and phantom experiments. An iterative model-based algorithm based on the variance-reduced stochastic gradient descent (VR-SGD) method is implemented. The optimum configuration found in simulation is validated on phantom experiments. Results: PAT imaging with dual-probe detection and VR-SGD algorithm is found to improve the limited-view problem compared to a single probe and provide comparable performance as full-view geometry in simulation. This configuration is validated in experiments where more complete structure is obtained with reduced artifacts compared with a single array. Conclusions: PAT with dual-probe detection and iterative reconstruction is a promising solution to the limited-view problem of linear arrays.

Purpose: We propose a deep learning method for the automatic diagnosis of COVID-19 at patient presentation on chest radiography (CXR) images and investigates the role of standard and soft tissue CXR in this task. Approach: The dataset consisted of the first CXR exams of 9860 patients acquired within 2 days after their initial reverse transcription polymerase chain reaction tests for the SARS-CoV-2 virus, 1523 (15.5%) of whom tested positive and 8337 (84.5%) of whom tested negative for COVID-19. A sequential transfer learning strategy was employed to fine-tune a convolutional neural network in phases on increasingly specific and complex tasks. The COVID-19 positive/negative classification was performed on standard images, soft tissue images, and both combined via feature fusion. A U-Net variant was used to segment and crop the lung region from each image prior to performing classification. Classification performances were evaluated and compared on a held-out test set of 1972 patients using the area under the receiver operating characteristic curve (AUC) and the DeLong test. Results: Using full standard, cropped standard, cropped, soft tissue, and both types of cropped CXR yielded AUC values of 0.74 [0.70, 0.77], 0.76 [0.73, 0.79], 0.73 [0.70, 0.76], and 0.78 [0.74, 0.81], respectively. Using soft tissue images significantly underperformed standard images, and using both types of CXR failed to significantly outperform using standard images alone. Conclusions: The proposed method was able to automatically diagnose COVID-19 at patient presentation with promising performance, and the inclusion of soft tissue images did not result in a significant performance improvement.

Significance: The current gold standard for monitoring small intestinal transplant (IT) rejection is endoscopic visual assessment and biopsy of suspicious lesions; however, these lesions are only superficially visualized by endoscopy. Invasive biopsies provide a coarse sampling of tissue health without depicting the true presence and extent of any pathology. Optical coherence tomography (OCT) presents a potential alternative approach with significant advantages over traditional white-light endoscopy. Aim: The aim of our investigation was to evaluate OCT performance in distinguishing clinically relevant morphological features associated with IT graft failure. Approach: OCT was applied to evaluate the small bowel tissues of two rhesus macaques that had undergone IT of the ileum. The traditional assessment from routine histological observation was compared with OCT captured using a handheld surgical probe during the days post-transplant and subsequently was compared with histophaology. Results: The reported OCT system was capable of identifying major biological landmarks in healthy intestinal tissue. Following IT, one nonhuman primate (NHP) model suffered a severe graft ischemia, and the second NHP graft failed due to acute cellular rejection. OCT images show visual evidence of correspondence with histological signs of IT rejection. Conclusions: Results suggest that OCT imaging has significant potential to reveal morphological changes associated with IT rejection and to improve patient outcomes overall.

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.

Significance: Effective vein visualization is critically important for several clinical procedures, such as venous blood sampling and intravenous injection. Existing technologies using infrared device or ultrasound rely on professional equipment and are not suitable for daily medical care. A regression-based vein visualization method is proposed. Aim: We visualize veins from conventional RGB images to provide assistance in venipuncture procedures as well as clinical diagnosis of some venous insufficiency. Approach: The RGB images taken by digital cameras are first transformed to spectral reflectance images using Wiener estimation. Multiple regression analysis is then applied to derive the relationship between spectral reflectance and the concentrations of pigments. Monte Carlo simulation is adopted to get prior information. Finally, vein patterns are visualized from the spatial distribution of pigments. To minimize the effect of illumination on skin color, light correction and shading removal operations are performed in advance. Results: Experimental results from inner forearms of 60 subjects show the effectiveness of the regression-based method. Subjective and objective evaluations demonstrate that the clarity and completeness of vein patterns can be improved by light correction and shading removal. Conclusions: Vein patterns can be successfully visualized from RGB images without any professional equipment. The proposed method can assist in venipuncture procedures. It also shows promising potential to be used in clinical diagnosis and treatment of some venous insufficiency.

Significance: Optical imaging of Cherenkov emission during radiation therapy could be used to verify dose delivery in real-time if a more comprehensive quantitative understanding of the factors affecting emission intensity could be developed. Aim: This study aims to explore the change in diffuse Cherenkov emission intensity with x-ray beam energy from irradiated tissue, both theoretically and experimentally. Approach: Derivation of the emitted Cherenkov signal was achieved using diffusion theory, and experimental studies with 6 to 18 MV energy x-rays were performed in tissue phantoms to confirm the model predictions as related to the radiation build-up factor with depth into tissue. Results: Irradiation at lower x-ray energies results in a greater surface dose and higher build-up slope, which results in a ∼46% greater diffusely emitted Cherenkov signal per unit dose at 6 MV relative to 18 MV x-rays. However, this phenomenon competes with a decrease in signal from less Cherenkov photons being generated at lower energies, a ∼44% reduction at 6 versus 18 MV. The result is an emitted Cherenkov signal that is nearly constant with beam energy. Conclusions: This study explains why the observed Cherenkov emission from tissue is not a strong function of beam energy, despite the known strong correlation between Cherenkov intensity and particle energy in the absence of build-up and scattering effects.

Significance: Complementary absorption and fluorescence contrast could prove useful for a wide range of biomedical applications. However, current absorption-based photoacoustic microscopy systems require the ultrasound transducers to physically touch the samples, thereby increasing contamination and limiting strong optical focusing in reflection mode. Aim: We sought to develop an all-optical system for imaging cells and tissues using the three combined imaging modalities: photoacoustic remote sensing (PARS), epifluorescence, and confocal laser scanning microscopy (CLSM). Approach: A PARS subsystem with ultraviolet excitation was used to obtain label-free absorption-contrast images of nucleic acids in ex vivo tissue samples. Co-integrated epifluorescence and CLSM subsystems were used to verify the 2D and 3D nuclei distribution. Results: Complementary absorption and fluorescence contrast were demonstrated in phantom imaging experiments and subsequent cell and tissue imaging experiments. Lateral and axial resolution of ultraviolet-PARS (UV-PARS) is shown to be 0.39 and 1.6 μm, respectively, with 266-nm light. CLSM lateral and axial resolution was measured as 0.97 and 2.0 μm, respectively. This resolution is sufficient to image individual cell layers with fine optical sectioning. UV-PARS images of cell nuclei are validated in thick tissue using CLSM. Conclusions: Multimodal absorption and fluorescence contrast are obtained with a non-contact all-optical microscopy system for the first time and utilized to obtain images of cells and tissues with subcellular resolution.

Significance: Artificial skin (AS) is widely used in dermatology, pharmacology, and toxicology, and has great potential in transplant medicine, burn wound care, and chronic wound treatment. There is a great demand for high-quality AS product and a non-invasive detection method is highly desirable. Aim: To quantify the constructure parameters (i.e., thickness and surface roughness) of AS samples in the culture cycle and explore the growth regularities using optical coherent tomography (OCT). Approach: An adaptive interface detection algorithm is developed to recognize surface points in each A-scan, offering a rapid method to calculate parameters without constructing OCT B-scan pictures and further achieving realizing real-time quantification of AS thickness and surface roughness. Experiments on standard roughness plates and H&E-staining microscopy were performed as a verification. Results: As applied on the whole cycle of AS culture, our method’s results show that during the air–liquid culture, the surface roughness of the skin first decreases and then exhibits an increase, which implies coincidence with the degree of keratinization under a microscope. And normal and typical abnormal samples can be differentiated by thickness and roughness parameters during the culture cycle. Conclusions: The adaptive interface detection algorithm is suitable for high-sensitivity, fast detection, and quantification of the interface with layered characteristic tissues, and can be used for non-destructive detection of the growth regularity of AS sample thickness and roughness during the culture cycle.

Purpose: To detect and diagnose coronavirus disease 2019 (COVID-19) better and faster, separable VGG-ResNet (SVRNet) and separable VGG-DenseNet (SVDNet) models are proposed, and a detection system is designed, based on lung x-rays to diagnose whether patients are infected with COVID-19. Approach: Combining deep learning and transfer learning, 1560 lung x-ray images in the COVID-19 x-ray image database (COVID-19 Radiography Database) were used as the experimental data set, and the most representative image classification models, VGG16, ResNet50, InceptionV3, and Xception, were fine-tuned and trained. Then, two new models for lung x-ray detection, SVRNet and SVDNet, were proposed on this basis. Finally, 312 test set images (including 44 COVID-19 and 268 normal images) were used as input to evaluate the classification accuracy, sensitivity, and specificity of SVRNet and SVDNet models. Results: In the classification experiment of lung x-rays that tested positive and negative for COVID-19, the classification accuracy, sensitivity, and specificity of SVRNet and SVDNet are 99.13%, 99.14%, 99.12% and 99.37%, 99.43%, 99.31%, respectively. Compared with the VGG16 network, SVRNet and SVDNet increased by 3.07%, 2.84%, 3.31% and 3.31%, 3.13%, 3.50%, respectively. On the other hand, the parameters of SVRNet and SVDNet are 5.65 × 10 6 and 6.57 × 10 6 , respectively. These are 61.56% and 55.31% less than VGG16, respectively. Conclusions: The SVRNet and SVDNet models proposed greatly reduce the number of parameters, while improving the accuracy and increasing the operating speed, and can accurately and quickly detect lung x-rays containing COVID-19.

Coronavirus 2019 (SARS-CoV-2 or COVID-19) is an infectious disease affecting the respiratory system, which has caused a global pandemic and widespread morbidity and mortality in humans. It has mild to severe symptoms similar to those of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), resulting in multiorgan damage.1 Currently, two tests are available for detection of COVID-19 in the affected patients: diagnostic tests (current infection) and antibody tests (past infection). Diagnostic tests, such as reverse transcription polymerase chain reaction (RT-PCR) and antigen tests, are used for rapid diagnosis of COVID-19. Since false positives (FPs) are more common in antigen tests, RT-PCR is used as the gold standard for diagnosis of the disease. RT-PCR tests require an intensive lab work to acquire the results,2 and the cost of the test is a major concern in many countries that have a private health system. Although the PCR and antigen test can now provide a rapid diagnosis, the assessment of the lungs using medical imaging will provide information on disease burden. Also, faster and earlier detection of COVID-19 would help in isolating the affected patients sooner to alleviate the disease spread. Chest radiography (CXR) and computed tomography (CT) images are the conventional medical imaging modalities used in lung disease diagnosis.3,4 Though CT images are extensively used in the COVID-19 diagnosis,5–7 cost8 and radiation exposure are major concerns. CXR are preferred over CT images as they have less exposure to radiation and extensively available.9 Hence, in this study, CXR images are used for automatic diagnosis of COVID-19. Deep learning techniques are widely used in various fields, such as computer vision, machine vision, and speech recognition, among which computer vision is one of the most popular fields in which promising results to have been obtained in image classification tasks.10–12 In medical image analysis, deep learning has been widely investigated for computer-aided diagnosis and treatment.13,14 Several state-of-art methods have been proposed for the diagnosis of COVID-19 using CXR images15–19 and CT images5,20,21 based on deep learning techniques. The transfer learning approaches based on deep learning have been preferred in detection of COVID-19 due to the limited available dataset. Several studies have been implemented for COVID-19 diagnosis using the pretrained model as a feature extractor by implementing transfer learning techniques.22–24 Table 1 summarizes recent studies describing methods developed for detecting COVID-19 from CXR images; the overall datasets used for training and testing the model, accuracy, and the sensitivity are provided. Ozturk et al.18 developed the DarkCovidNet model for the detection of COVID-19 using multiclass classification. The model was developed using end-to-end architecture without adding any feature extraction techniques. Sensitivity of 85.35% was achieved by the proposed model. Wang and Wong25 proposed COVID-Net, a deep convolutional neural network (DCNN) developed by adopting machine-driven design exploration strategy. A multiclass classification was performed using CXR images in identifying COVID-19 from normal and non-COVID disease (i.e., pneumonia). Both qualitative and quantitative analysis were performed to determine the success of the model. The developed model was able to achieve a sensitivity of 91% for detecting COVID-19. Panwar et al.26 introduced nCOVnet, an algorithm developed based on a transfer learning model for faster diagnosis of COVID-19 from CXR images. The proposed model is built using VGG 16 as the base model. The model was able to achieve 97.62% in detecting COVID-19. Sethy et al.23 described a comparative study on Xception, ResNet18, ResNet50, Resnet 101, Inceptionv3, Inceptionresnetv2, GoogleNet, Densenet201, VGG16, VGG19, and AlexNet using the deep features extracted from the CNN layers and fed to the SVM classifier for classification. ResNet50 performed better compared with other classification models on a binary classification between viral pneumonia and COVID-19 with a classification accuracy of 95.38%. Jain et al.27 utilized ResNet50 model that was used in stage-I network model for distinguishing viral (including COVID-19) from bacterial pneumonia and normal cases using CXR images. Based on the result obtained, ResNet101 was used in stage-II network model to classify COVID-19 from other viral pneumonia. The model achieved an accuracy of 97% in COVID-19 detection. Narin et al.28 proposed three CNNs for the detection of COVID-19 through CXR images. The pretrained models chosen for comparative study were ResNet50, InceptionV3, and Inception-ResNetV2. A binary classification was performed between normal and COVID-19. ResNet50 model outperformed all the other models with the highest classification accuracy of 98%. Ioannis et al.22 compared VGG 19, Mobile Net, Inception, Xception, and Inception ResNetv2 pretrained models for the automatic detection of COVID-19 using CXR images. They implemented multiclass classification among COVID-19, normal, and pneumonia. MobileNetV2 achieved highest sensitivity rate of 98.6%. Asnaoui et al.29 put forward a comparative study on the pretrained DCNN models, namely VGG16, VGG19, Inception-ResNetV2, InceptionV3, ResNet50, DenseNet201, and MobileNetV2 for the classification of CXR into normal, bacteria, and coronavirus (multiclass classification). Inception-ResNetV2 model provided a sensitivity rate of 92.11% in detecting coronavirus. Hemdan et al.30 proposed COVIDX-Net a deep learning framework based on seven DCNNs namely, VGG19, Xception, ResNetV2, InceptionV3, Inception-ResNetV2, DenseNet201, and MobileNetV2 for the diagnosis of COVID-19 using x-ray images. The VGG19 and DenseNet201 achieved an accuracy of 90% compared with other models with F1-score of 0.89 for normal and 0.91 for COVID-19. Table 1 Methods described for diagnosis of COVID-19 from CXR and CT images.

SignificanceSpeckle variations induced by intracellular motion (IM) in the urothelium was observed in optical coherence tomography (OCT) images. It is feasible to use the IM as a dynamic contrast to segment the urothelium with only two sequential OCT images. This new method opens the possibility of tracking the distribution of the urothelial cells to identify the microinvasion of bladder tumors.ApproachWith fresh porcine bladder tissue, IM was analyzed by tracking speckle variations using autocorrelation function, then quantified with CONTINE algorism to identify the decorrelation time (DT) of the speckle variations. Variance analysis is conducted to show IM amplitude and distribution in the urothelium. The segmentation of the urothelium is demonstrated using tissue samples with and without significantly stretching.ResultsSignificant speckle variations induced by IM exists in the urothelium. However, the distribution of the IM is heterogeneous. The DTs are majorly distributed between 1ms to 30ms. Using the IM as a dynamic contrast, the urothelium can be accurately and exclusively segmented, even the layer structure of the bladder is invisible.ConclusionsWith fresh porcine bladders, we show that IM can be used as a dynamic contrast to exclusively segment the urothelium. This contrast may provide a new mechanism for OCT to diagnosis the invasion of urothelial cancerous cells for the better staging of bladder cancer.

Raman spectroscopy has shown very promising results in medical diagnostics by providing label-free and highly specific molecular information of pathological tissue ex vivo and in vivo. Nevertheless, the high specificity of Raman spectroscopy comes at a price, i.e., low acquisition rate, no direct access to depth information, and limited sampling areas. However, a similar case regarding advantages and disadvantages can also be made for other highly regarded optical modalities, such as optical coherence tomography, autofluorescence imaging and fluorescence spectroscopy, fluorescence lifetime microscopy, second-harmonic generation, and others. While in these modalities the acquisition speed is significantly higher, they have no or only limited molecular specificity and are only sensitive to a small group of molecules. It can be safely stated that a single modality provides only a limited view on a specific aspect of a biological specimen and cannot assess the entire complexity of a sample. To solve this issue, multimodal optical systems, which combine different optical modalities tailored to a particular need, become more and more common in translational research and will be indispensable diagnostic tools in clinical pathology in the near future. These systems can assess different and partially complementary aspects of a sample and provide a distinct set of independent biomarkers. Here, we want to give an overview on the development of multimodal systems that use RS in combination with other optical modalities to improve the diagnostic performance.

Significance: The morphological properties and hemoglobin (Hb) content of red blood cells (RBCs) are essential biomarkers to diagnose or monitor various types of hematological disorders. Label-free mass mapping approaches enable accurate Hb quantification from individual cells, serving as promising alternatives to conventional hematology analyzers. Deep ultraviolet (UV) microscopy is one such technique that allows high-resolution, molecular imaging, and absorption-based mass mapping. Aim: To compare UV absorption-based mass mapping at four UV wavelengths and understand variations across wavelengths and any assumptions necessary for accurate Hb quantification. Approach: Whole blood smears are imaged with a multispectral UV microscopy system, and the RBCs’ dry masses are computed. This approach is compared to quantitative phase imaging-based mass mapping using data from an interferometric UV imaging system. Results: Consistent Hb mass and mean corpuscular Hb values are obtained at all wavelengths, with the precision of the single-cell mass measurements being nearly identical at 220, 260, and 280 nm but slightly lower at 300 nm. Conclusions: A full hematological analysis (including white blood cell identification and characterization, and Hb quantification) may be achieved using a single UV illumination wavelength, thereby improving the speed and cost-effectiveness.

Significance: Hyperspectral and multispectral imaging (HMSI) in medical applications provides information about the physiology, morphology, and composition of tissues and organs. The use of these technologies enables the evaluation of biological objects and can potentially be applied as an objective assessment tool for medical professionals. Aim: Our study investigates HMSI systems for their usability in medical applications. Approach: Four HMSI systems (one hyperspectral pushbroom camera and three multispectral snapshot cameras) were examined and a spectrometer was used as a reference system, which was initially validated with a standardized color chart. The spectral accuracy of the cameras reproducing chemical properties of different biological objects (porcine blood, physiological porcine tissue, and pathological porcine tissue) was analyzed using the Pearson correlation coefficient. Results: All the HMSI cameras examined were able to provide the characteristic spectral properties of blood and tissues. A pushbroom camera and two snapshot systems achieve Pearson coefficients of at least 0.97 compared to the ground truth, indicating a very high positive correlation. Only one snapshot camera performs moderately to high positive correlation (0.59 to 0.85). Conclusion: The knowledge of the suitability of HMSI cameras for accurate measurement of chemical properties of biological objects offers a good opportunity for the selection of the optimal imaging tool for specific medical applications, such as organ transplantation.

Significance: Despite remarkable advances in the core modalities used in combating cancer, malignant diseases remain the second largest cause of death globally. Interstitial photodynamic therapy (IPDT) has emerged as an alternative approach for the treatment of solid tumors. Aim: The aim of our study is to outline the advancements in IPDT in recent years and provide our vision for the inclusion of IPDT in standard-of-care (SoC) treatment guidelines of specific malignant diseases. Approach: First, the SoC treatment for solid tumors is described, and the attractive properties of IPDT are presented. Second, the application of IPDT for selected types of tumors is discussed. Finally, future opportunities are considered. Results: Strong research efforts in academic, clinical, and industrial settings have led to significant improvements in the current implementation of IPDT, and these studies have demonstrated the unique advantages of this modality for the treatment of solid tumors. It is envisioned that further randomized prospective clinical trials and treatment optimization will enable a wide acceptance of IPDT in the clinical community and inclusion in SoC guidelines for well-defined clinical indications. Conclusions: The minimally invasive nature of this treatment modality combined with the relatively mild side effects makes IPDT a compelling alternative option for treatment in a number of clinical applications. The adaptability of this technique provides many opportunities to both optimize and personalize the treatment.

Significance: Simulations have the potential to be a powerful tool when planning the placement of photoacoustic imaging system components for surgical guidance. While elastic simulations (which include both compressional and shear waves) are expected to more accurately represent the physical transcranial acoustic wave propagation process, these simulations are more time-consuming and memory-intensive than the compressional-wave-only simulations that our group previously used to identify optimal acoustic windows for transcranial photoacoustic imaging. Aim: We present qualitative and quantitative comparisons of compressional and elastic wave simulations to determine which option is more suitable for preoperative surgical planning. Approach: Compressional and elastic photoacoustic k-Wave simulations were performed based on a computed tomography volume of a human cadaver head. Photoacoustic sources were placed in the locations of the internal carotid arteries and likely positions of neurosurgical instrument tips. Transducers received signals from three previously identified optimal acoustic windows (i.e., the ocular, nasal, and temporal regions). Target detectability, image-based target size estimates, and target-to-instrument distances were measured using the generalized contrast-to-noise ratio (gCNR), resolution, and relative source distances, respectively, for each simulation method. Results: The gCNR was equivalent between compressional and elastic simulations. The areas of the −6 dB contours of point spread functions utilized to measure resolution differed by 0.33 to 3.35 mm2. Target-to-instrument distance measurements were within 1.24 mm of the true distances. Conclusions: These results indicate that it is likely sufficient to utilize the less time-consuming, less memory-intensive compressional wave simulations for presurgical planning.

Significance: Seborrheic keratoses (SKs) are harmless pigmented skin lesions (PSLs) that may be confused clinically not only with other benign conditions but also with cutaneous melanoma (CM). As SKs are one of the most common neoplasms in adults, the importance of their correct diagnosis is high. Misclassifying SK as malignant is not rare and leads to a high number of unnecessary biopsies. On the other hand, misdiagnosing CM as SK may have a large impact on prognosis or therapy. Aim: In the non-invasive technique of dermatofluoroscopy, the fluorophores in melanocytes and keratinocytes are excited in vivo with nanosecond laser pulses and the resulting spectrally resolved, melanin-dominated fluorescence signals are used to differentiate between pigmented benign lesions and CM. Approach: In this single-center, non-interventional study, 33 PSLs of 20 patients were scanned with dermatofluoroscopy in vivo. For all included cases, dermatofluoroscopic signals were compared to pathology classification. Results: The characteristic spectral features of SK were identified, where the signals are dominated by keratin, NAD(P)H, and melanin. The fluorescence spectra of SKs differed substantially from those of CM: a characteristic spectrum of SK has been identified in 27 of 28 SKs. Conclusions: The high-accuracy differential diagnosis between CM and SK is possible with dermatofluoroscopy.

Significance: Chronic lung allograft dysfunction (CLAD) is the leading cause of death in transplant patients who survive past the first year post-transplant. Current diagnosis is based on sustained decline in lung function; there is a need for tools that can identify CLAD onset. Aim: Endoscopic optical coherence tomography (OCT) can visualize structural changes in the small airways, which are of interest in CLAD progression. We aim to identify OCT features in the small airways of lung allografts that correlate with CLAD status. Approach: Imaging was conducted with an endoscopic rotary pullback OCT catheter during routine bronchoscopy procedures (n = 54), collecting volumetric scans of three segmental airways per patient. Six features of interest were identified, and four blinded raters scored the dataset on the presence and intensity of each feature. Results: Airway dilation (AD) was the only feature found to significantly (p

Significance: Collagen is the most abundant protein in vertebrates and is found in tissues that regularly experience tension, compression, and shear forces. However, the underlying mechanism of collagen fibril formation and remodeling is poorly understood. Aim: We explore how a collagen monomer is visualized using fluorescence microscopy and how its spatial orientation is determined. Defining the orientation of collagen monomers is not a trivial problem, as the monomer has a weak contrast and is relatively small. It is possible to attach fluorescence tags for contrast, but the size is still a problem for detecting orientation using fluorescence microscopy. Approach: We present two methods for detecting a monomer and classifying its orientation. A modified Gabor filter set and an automatic classifier trained by convolutional neural network based on a synthetic dataset were used. Results: By evaluating the performance of these two approaches with synthetic and experimental data, our results show that it is possible to determine the location and orientation with an error of ∼37 deg of a single monomer with fluorescence microscopy. Conclusions: These findings can contribute to our understanding of collagen monomers interaction with collagen fibrils surface during fibril formation and remodeling.

Significance: The Mueller matrix decomposition method is widely used for the analysis of biological samples. However, its presumed sequential appearance of the basic optical effects (e.g., dichroism, retardance, and depolarization) limits its accuracy and application. Aim: An approach is proposed for detecting and classifying human melanoma and non-melanoma skin cancer lesions based on the characteristics of the Mueller matrix elements and a random forest (RF) algorithm. Approach: In the proposal technique, 669 data points corresponding to the 16 elements of the Mueller matrices obtained from 32 tissue samples with squamous cell carcinoma (SCC), basal cell carcinoma (BCC), melanoma, and normal features are input into an RF classifier as predictors. Results: The results show that the proposed model yields an average precision of 93%. Furthermore, the classification results show that for biological tissues, the circular polarization properties (i.e., elements m44, m34, m24, and m14 of the Mueller matrix) dominate the linear polarization properties (i.e., elements m13, m31, m22, and m41 of the Mueller matrix) in determining the classification outcome of the trained classifier. Conclusions: Overall, our study provides a simple, accurate, and cost-effective solution for developing a technique for classification and diagnosis of human skin cancer.

Significance: Diffuse light is ubiquitous in biomedical optics and imaging. Understanding the process of migration of an initial photon population entering tissue to a completely randomized, diffusely scattered population provides valuable insight to the interpretation and design of optical measurements. Aim: The goal of this perspective is to present a brief, unifying analytical framework to describe how properties of light transition from an initial state to a distributed state as light diffusion occurs. Approach: First, measurement parameters of light are introduced, and Monte Carlo simulations along with a simple analytical expression are used to explore how these individual parameters might exhibit diffusive behavior. Second, techniques to perform optical measurements are considered, highlighting how various measurement parameters can be leveraged to subsample photon populations. Results: Simulation results reinforce the fact that light undergoes a transition from a non-diffuse population to one that is first subdiffuse and then fully diffuse. Myriad experimental methods exist to isolate subpopulations of photons, which can be broadly categorized as source- and/or detector-encoded techniques, as well as methods of tagging the tissue of interest. Conclusions: Characteristic properties of light progressing to diffusion can be described by some form of Gaussian distribution that grows in space, time, angle, wavelength, polarization, and coherence. In some cases, these features can be approximated by simpler exponential behavior. Experimental methods to subsample features of the photon distribution can be achieved or theoretical methods can be used to better interpret the data with this framework.

Significance: Morphological collagen signatures are important for tissue function, particularly in the tumor microenvironment. A single algorithmic framework with quantitative, multiscale morphological collagen feature extraction may further the use of collagen signatures in understanding fundamental tumor progression. Aim: A modification of the 2D wavelet transform modulus maxima (WTMM) anisotropy method was applied to both digitally simulated collagen fibers and second-harmonic-generation imaged collagen fibers of mouse skin to calculate a multiscale anisotropy factor to detect collagen fiber organization. Approach: The modified 2D WTMM anisotropy method was initially validated on synthetic calibration images to establish the robustness and sensitivity of the multiscale fiber organization tool. Upon validation, the algorithm was applied to collagen fiber organization in normal wild-type skin, melanoma stimulated skin, and integrin α10KO skin. Results: Normal wild-type skin collagen fibers have an increased anisotropy factor at all sizes scales. Interestingly, the multiscale anisotropy differences highlight important dissimilarities between collagen fiber organization in normal wild-type skin, melanoma stimulated, and integrin α10KO skin. At small scales (∼2 to 3 μm), the integrin α10KO skin was vastly different than normal skin (p-value ∼ 10 − 8), whereas the melanoma stimulated skin was vastly different than normal at large scales (∼30 to 40 μm, p-value ∼ 10 − 15). Conclusions: This objective computational collagen fiber organization algorithm is sensitive to collagen fiber organization across multiple scales for effective exploration of collagen morphological alterations associated with melanoma and the lack of α10 integrin binding.

Significance: Idiopathic pulmonary fibrosis (IPF) patients have a poor prognosis with short lifespan following diagnosis as there are limited effective treatment options. Despite matrix stiffening being the hallmark of the disease there remains a lack of knowledge surrounding the underlying collagen alterations in the disease. Specifically, while increased collagen crosslinking has been implicated, the resulting effects on collagen macro/supramolecular changes have not been explored. Aim: We sought to determine if second-harmonic generation (SHG) microscopy could characterize differences in the collagen architecture in 3D spheroid models of IPF grown under different crosslinking modulation conditions (promotion and inhibition). Approach: We used SHG metrics based on the fiber morphology, relative SHG brightness, and macro/supramolecular structure by SHG polarization analyses to compare the structure of the IPF spheroids. Results: Comparison of the fiber morphology of the spheroids showed that the control group had the longest, straightest, and thickest fibers. The spheroids with crosslink enhancement and inhibition had the highest and lowest SHG conversion efficiencies, respectively, consistent with the resulting harmonophore density. SHG polarization analyses showed that the peptide pitch angle, alignment of collagen molecules, and overall chirality were altered upon crosslink modulation and were also consistent with reduced organization relative to the control group. Conclusions: While no single SHG signature is associated with crosslinking, we show that the suite of metrics used here is effective in delineating alterations across the collagen architecture sizescales. The results largely mirror those of human tissues and demonstrate that the combination of 3D spheroid models and SHG analysis is a powerful approach for hypothesis testing the roles of operative cellular and molecular factors in IPF.

Significance: Speckle noise limits the diagnostic capabilities of optical coherence tomography (OCT) images, causing both a reduction in contrast and a less accurate assessment of the microstructural morphology of the tissue. Aim: We present a speckle-noise reduction method for OCT volumes that exploits the advantages of adaptive-noise wavelet thresholding with a wavelet compounding method applied to several frames acquired from consecutive positions. The method takes advantage of the wavelet representation of the speckle statistics, calculated properly from a homogeneous sample or a region of the noisy volume. Approach: The proposed method was first compared quantitatively with different state-of-the-art approaches by being applied to three different clinical dermatological OCT volumes with three different OCT settings. The method was also applied to a public retinal spectral-domain OCT dataset to demonstrate its applicability to different imaging modalities. Results: The results based on four different metrics demonstrate that the proposed method achieved the best performance among the tested techniques in suppressing noise and preserving structural information. Conclusions: The proposed OCT denoising technique has the potential to adapt to different image OCT settings and noise environments and to improve image quality prior to clinical diagnosis based on visual assessment.

Significance: Coherent Raman scattering (CRS) microscopy is an optical imaging technique with capabilities that could benefit a broad range of biomedical research studies. Aim: We reflect on the birth, rapid rise, and inescapable growing pains of the technique and look back on nearly four decades of developments to examine where the CRS imaging approach might be headed in the next decade to come. Approach: We provide a brief historical account of CRS microscopy, followed by a discussion of the challenges to disseminate the technique to a larger audience. We then highlight recent progress in expanding the capabilities of the CRS microscope and assess its current appeal as a practical imaging tool. Results: New developments in Raman tagging have improved the specificity and sensitivity of the CRS technique. In addition, technical advances have led to CRS microscopes that can capture hyperspectral data cubes at practical acquisition times. These improvements have broadened the application space of the technique. Conclusion: The technical performance of the CRS microscope has improved dramatically since its inception, but these advances have not yet translated into a substantial user base beyond a strong core of enthusiasts. Nonetheless, new developments are poised to move the unique capabilities of the technique into the hands of more users.

Author(s): Tressel, John T; Abdelaziz, Marwa; Fried, Daniel | Abstract: SignificanceIt is not sufficient to detect caries lesions on tooth surfaces; it is also necessary to measure the activity of the lesions to determine if intervention is needed. Changes in the reflectivity of lesion areas during dehydration with forced air at short wavelength infrared (SWIR) wavelengths can be used to assess lesion activity since these changes represent the evaporation dynamics of water from the lesion.AimThe aim of this study is to develop new optical methods for assessing lesion activity on tooth surfaces utilizing the strong water absorption band near 1950-nm.ApproachThe time-resolved reflectivity of 20 active and arrested caries lesions on the surfaces of human extracted teeth was monitored at 1300 to 2000nnm using broadband light sources and an extended range InGaAs camera during drying with air.ResultsMultiple parameters representing the rate of change of the lesion reflectivity correlated with the presence of a highly mineralized outer surface zone indicative of lesion arrest measured with x-ray microtomography (microCT). Performance at 1950-nm was higher than for other wavelengths.ConclusionsThis study demonstrates that SWIR imaging near 1950-nm has great potential for the assessment of lesion activity.

Significance: Real-time information about oxygen delivery to the hepatic graft is important to direct care and diagnose vascular compromise in the immediate post-transplant period. Aim: The current study was designed to determine the utility of visible diffuse reflectance spectroscopy (vis-DRS) for measuring liver tissue saturation in vivo. Approach: A custom-built vis-DRS probe was calibrated using phantoms with hemoglobin (Hb) and polystyrene microspheres. Ex vivo (extracorporeal circulation) and in vivo protocols were used in a swine model (n = 15) with validation via blood gas analysis. Results:In vivo absorption and scattering measured by vis-DRS with and without biliverdin correction correlated closely between analyses. Lin’s concordance correlation coefficients are 0.991 for μa and 0.959 for μs ′ . Hb measured by blood test and vis-DRS with (R2 = 0.81) and without (R2 = 0.85) biliverdin correction were compared. Vis-DRS data obtained from the ex vivo protocol plotted against the PO2 derived from blood gas analysis showed a good fit for a Hill coefficient of 1.67 and P50 = 34 mmHg (R2 = 0.81). A conversion formula was developed to account for the systematic deviation, which resulted in a goodness-of-fit R2 = 0.76 with the expected oxygen dissociation curve. Conclusions: We show that vis-DRS allows for real-time measurement of liver tissue saturation, an indicator for liver perfusion and oxygen delivery.

Significance: Optical scattering signals obtained from tissue constituents contain a wealth of structural information. Conventional intensity features, however, are mostly dictated by the overall morphology and mean refractive index of these constituents, making it very difficult to exclusively sense internal refractive index fluctuations. Aim: We perform a systematic analysis to elucidate how changes in internal refractive index profile of cell nuclei can best be detected via optical scattering. Approach: We construct stochastically inhomogeneous nuclear models and numerically simulate their azimuth-resolved scattering patterns. We then process these two-dimensional patterns with the goal of identifying features that directly point to subnuclear structure. Results: Azimuth-dependent intensity variations over the side scattering range provide significant insights into subnuclear refractive index profile. A particular feature we refer to as contrast ratio is observed to be highly sensitive to the length scale and extent of refractive index fluctuations; further, this feature is not susceptible to changes in the overall size and mean refractive index of nuclei, thereby allowing for selective tracking of subnuclear structure that can be linked to chromatin distribution. Conclusions: Our analysis will potentially pave the way for scattering-based assessment of chromatin reorganization that is considered to be a key hallmark of precancer progression.

Significance: High density diffuse optical tomography (HD-DOT) as applied in functional near-infrared spectroscopy (fNIRS) is largely limited to continuous wave (CW) data. Using a single modulation frequency, frequency domain (FD) HD-DOT has recently demonstrated better localization of focal activation as compared to CW data. We show that combining CW and FD measurements and multiple modulation frequencies increases imaging performance in fNIRS. Aim: We evaluate the benefits of multiple modulation frequencies, combining different frequencies as well as CW data in fNIRS HD-DOT. Approach: A layered model was used, with activation occurring within a cortex layer. CW and FD measurements were simulated at 78, 141, and 203 MHz with and without noise. The localization error, full width half maximum, and effective resolution were evaluated. Results: Across the average of the three metrics, at 141 MHz, FD performed 8.4% better than CW, and the combination of CW and FD was 21.7% better than CW. FD measurements at 203 MHz performed 5% better than 78 MHz. Moreover, the three combined modulation frequencies of FD and CW performed up to 3.92% better than 141 MHz alone. Conclusions: We show that combining CW and FD measurements offers better performance than FD alone, with higher modulation frequencies increasing accuracy. Combining CW and FD measurements at multiple modulation frequencies yields the best overall performance.

Significance: Dental caries is the most common oral disease, with significant effects on healthcare systems and quality of life. Developing diagnostic methods for early caries detection is key to reducing this burden and enabling non-invasive treatment as opposed to the drill-and-fill approach. Aim: The application of a thermophotonic-based 3D imaging modality [enhanced truncated-correlation photothermal coherence tomography (eTC-PCT)] to early dental caries is investigated. To this end, the detection threshold, sensitivity, and 3D lesion reconstruction capability of eTC-PCT in imaging artificially generated caries and surface erosion are evaluated. Approach: eTC-PCT employs a diode laser with pulsed excitation, a mid-IR camera, and an in-house developed image reconstruction algorithm to produce depth-resolved 2D images and 3D reconstructions. Starting with healthy teeth, dental caries and surface erosion are simulated in vitro through application of specific demineralizing/eroding acidic solutions. Results: eTC-PCT can detect artificial caries as early as 2 days after onset of artificial demineralization and after 45 s of surface erosion, with a laser power equivalent to 64% of maximum permissible exposure. In both cases, the lesion is not visible to the eye and undetected by x-rays. eTC-PCT is capable of monitoring lesion progression in 2-day increments and generating 3D tomographic reconstructions of the advancing lesion. Conclusions: eTC-PCT shows great potential for further development as a dental imaging modality combining low detection threshold, high sensitivity to lesion progression, 3D reconstruction capability, and lack of ionizing radiation. These features enable early diagnosis and frequent monitoring, making eTC-PCT a promising technology for facilitating preventive dentistry.

Significance: Corneal diseases is a major cause of reversible blindness in the world. Monitoring the progression of human corneal edema or corneal scarring to prevent the disease entering into the end stage is crucial. Aim: We present a method for sensing human corneal composition at different depths, namely focused on the epithelium and stromal layer, using high-sensitivity terahertz (THz) broadband spectroscopy. Approach: From the proposed methodology, the THz temporal and absorption spectra of human corneas at different edema stages have been studied. THz wave signals were collected from the direct reflection and four other collection points along the THz wave propagation direction as reviewed from the simulation THz electrical field. Result: Our results show that the epithelium layer acts as a good barrier to maintain hydration level of the stroma, and the quality of the epithelium can be used to predict the level of corneal swelling in corneal edema. At the detection points near to the incident point, the THz frequency spectra demonstrated interference oscillation behavior. At the final edema observing time, results showed that the epithelium lose its barrier properties. The intactness of the epithelium can be used to predict the edema severity in the final stage. When the detection points are further away from the incident point, the THz spectra are believed to contain information from stromal layer. Stromal absorption spectra demonstrated correlation with optical coherence tomography thickness results. Conclusion: The hydration concentration from stromal layer was further quantitatively calculated. At the end of the experiment, all the corneal hydration levels reach to the same value which shows that the edema hydration has reached maximum saturation. The information of individual sublayers of the cornea is obtained by characterizing noninvasively with the use of THz spectroscopy. To our knowledge, this is the first report of using THz for noninvasive characterization of sublayers of the cornea.

Significance: Speckle noise is an inherent limitation of optical coherence tomography (OCT) images that makes clinical interpretation challenging. The recent emergence of deep learning could offer a reliable method to reduce noise in OCT images. Aim: We sought to investigate the use of deep features (VGG) to limit the effect of blurriness and increase perceptual sharpness and to evaluate its impact on the performance of OCT image denoising (DnCNN). Approach: Fifty-one macula-centered OCT pairs were used in training of the network. Another set of 20 OCT pair was used for testing. The DnCNN model was cascaded with a VGG network that acted as a perceptual loss function instead of the traditional losses of L1 and L2. The VGG network remains fixed during the training process. We focused on the individual layers of the VGG-16 network to decipher the contribution of each distinctive layer as a loss function to produce denoised OCT images that were perceptually sharp and that preserved the faint features (retinal layer boundaries) essential for interpretation. The peak signal-to-noise ratio (PSNR), edge-preserving index, and no-reference image sharpness/blurriness [perceptual sharpness index (PSI), just noticeable blur (JNB), and spectral and spatial sharpness measure (S3)] metrics were used to compare deep feature losses with the traditional losses. Results: The deep feature loss produced images with high perceptual sharpness measures at the cost of less smoothness (PSNR) in OCT images. The deep feature loss outperformed the traditional losses (L1 and L2) for all of the evaluation metrics except for PSNR. The PSI, S3, and JNB estimates of deep feature loss performance were 0.31, 0.30, and 16.53, respectively. For L1 and L2 losses performance, the PSI, S3, and JNB were 0.21 and 0.21, 0.17 and 0.16, and 14.46 and 14.34, respectively. Conclusions: We demonstrate the potential of deep feature loss in denoising OCT images. Our preliminary findings suggest research directions for further investigation.