Showing papers in "Biomedical Optics Express in 2022"

Label-free plasmonic immunosensor for cortisol detection in a D-shaped optical fiber.

Abstract: Measuring cortisol levels as a stress biomarker is essential in many medical conditions associated with a high risk of metabolic syndromes such as anxiety and cardiovascular diseases, among others. One technology that has a growing interest in recent years is fiber optic biosensors that enable ultrasensitive cortisol detection. Such interest is allied with progress being achieved in basic interrogation, accuracy improvements, and novel applications. The development of improved cortisol monitoring, with a simplified manufacturing process, high reproducibility, and low cost, are challenges that these sensing mechanisms still face, and for which solutions are still needed. In this paper, a comprehensive characterization of a D-shaped fiber optic immunosensor for cortisol detection based on surface plasmon resonance (SPR) enabled by gold coating is reported. Specifically, the sensor instrumentation and fabrication processes are discussed in detail, and a simulation with its complete mathematical formalism is also presented. Moreover, experimental cortisol detection tests were performed for a detection range of 0.01 to 100 ng/mL, attaining a logarithmic sensitivity of 0.65 ± 0.02 nm/log(ng/mL) with a limit of detection (LOD) of 1.46 ng/mL. Additionally, an investigation of signal processing is also discussed, with the main issues addressed in order to highlight the best way to extract the sensing information from the spectra measured with a D-shaped sensor.

Imaging of vitreous cortex hyalocyte dynamics using non-confocal quadrant-detection adaptive optics scanning light ophthalmoscopy in human subjects.

Abstract: Vitreous cortex hyalocytes are resident macrophage cells that help maintain the transparency of the media, provide immunosurveillance, and respond to tissue injury and inflammation. In this study, we demonstrate the use of non-confocal quadrant-detection adaptive optics scanning light ophthalmoscopy (AOSLO) to non-invasively visualize the movement and morphological changes of the hyalocyte cell bodies and processes over 1-2 hour periods in the living human eye. The average velocity of the cells 0.52 ± 0.76 µm/min when sampled every 5 minutes and 0.23 ± 0.29 µm/min when sampled every 30 minutes, suggesting that the hyalocytes move in quick bursts. Understanding the behavior of these cells under normal physiological conditions may lead to their use as biomarkers or suitable targets for therapy in eye diseases such as diabetic retinopathy, preretinal fibrosis and glaucoma.

Impact of skin tone on photoacoustic oximetry and tools to minimize bias.

Abstract: The major optical absorbers in tissue are melanin and oxy/deoxy-hemoglobin, but the impact of skin tone and pigmentation on biomedical optics is still not completely understood or adequately addressed. Melanin largely governs skin tone with higher melanin concentration in subjects with darker skin tones. Recently, there has been extensive debate on the bias of pulse oximeters when used with darker subjects. Photoacoustic (PA) imaging can measure oxygen saturation similarly as pulse oximeters and could have value in studying this bias. More importantly, it can deconvolute the signal from the skin and underlying tissue. Here, we studied the impact of skin tone on PA signal generation, depth penetration, and oximetry. Our results show that subjects with darker skin tones exhibit significantly higher PA signal at the skin surface, reduced penetration depth, and lower oxygen saturation compared to subjects with lighter skin tones. We then suggest a simple way to compensate for these signal differences.

Transfontanelle photoacoustic imaging: ultrasound transducer selection analysis.

Abstract: Transfontanelle ultrasound imaging (TFUI) is the conventional approach for diagnosing brain injury in neonates. Despite being the first stage imaging modality, TFUI lacks accuracy in determining the injury at an early stage due to degraded sensitivity and specificity. Therefore, a modality like photoacoustic imaging that combines the advantages of both acoustic and optical imaging can overcome the existing TFUI limitations. Even though a variety of transducers have been used in TFUI, it is essential to identify the transducer specification that is optimal for transfontanelle imaging using the photoacoustic technique. In this study, we evaluated the performance of 6 commercially available ultrasound transducer arrays to identify the optimal characteristics for transfontanelle photoacoustic imaging. We focused on commercially available linear and phased array transducer probes with center frequencies ranging from 2.5MHz to 8.5MHz which covers the entire spectrum of the transducer arrays used for brain imaging. The probes were tested on both in vitro and ex vivo brain tissue, and their performance in terms of transducer resolution, size, penetration depth, sensitivity, signal to noise ratio, signal amplification and reconstructed image quality were evaluated. The analysis of selected transducers in these areas allowed us to determine the optimal transducer for transfontanelle imaging, based on vasculature depth and blood density in tissue using ex vivo sheep brain. The outcome of this evaluation identified the two most suitable ultrasound transducer probes for transfontanelle photoacoustic imaging.

Respiratory and heart rate monitoring using an FBG 3D-printed wearable system.

Abstract: This work proposes a 3D-printed sensor based on fiber Bragg grating (FBG) technology for respiratory rate (RR) and heart rate (HR) monitoring. Each sensor is composed of a single FBG fully encapsulated into a 3D-printable Flexible, during the printing process. Sensors with different material thicknesses and infill densities were tested. The sensor with the best metrological properties was selected and preliminary assessed in terms of capability of monitoring RR and HR on three users. Preliminary results proved that the developed sensor can be a valuable easy-to-fabricate solution, with high reproducibility and high strain sensitivity to chest wall deformations due to breathing and heart beating.

Automatic geographic atrophy segmentation using optical attenuation in OCT scans with deep learning.

Abstract: A deep learning algorithm was developed to automatically identify, segment, and quantify geographic atrophy (GA) based on optical attenuation coefficients (OACs) calculated from optical coherence tomography (OCT) datasets. Normal eyes and eyes with GA secondary to age-related macular degeneration were imaged with swept-source OCT using 6 × 6 mm scanning patterns. OACs calculated from OCT scans were used to generate customized composite en face OAC images. GA lesions were identified and measured using customized en face sub-retinal pigment epithelium (subRPE) OCT images. Two deep learning models with the same U-Net architecture were trained using OAC images and subRPE OCT images. Model performance was evaluated using DICE similarity coefficients (DSCs). The GA areas were calculated and compared with manual segmentations using Pearson's correlation and Bland-Altman plots. In total, 80 GA eyes and 60 normal eyes were included in this study, out of which, 16 GA eyes and 12 normal eyes were used to test the models. Both models identified GA with 100% sensitivity and specificity on the subject level. With the GA eyes, the model trained with OAC images achieved significantly higher DSCs, stronger correlation to manual results and smaller mean bias than the model trained with subRPE OCT images (0.940 ± 0.032 vs 0.889 ± 0.056, p = 0.03, paired t-test, r = 0.995 vs r = 0.959, mean bias = 0.011 mm vs mean bias = 0.117 mm). In summary, the proposed deep learning model using composite OAC images effectively and accurately identified, segmented, and quantified GA using OCT scans.

Complete head cerebral sensitivity mapping for diffuse correlation spectroscopy using subject-specific magnetic resonance imaging models

Abstract: We characterize cerebral sensitivity across the entire adult human head for diffuse correlation spectroscopy, an optical technique increasingly used for bedside cerebral perfusion monitoring. Sixteen subject-specific magnetic resonance imaging-derived head models were used to identify high sensitivity regions by running Monte Carlo light propagation simulations at over eight hundred uniformly distributed locations on the head. Significant spatial variations in cerebral sensitivity, consistent across subjects, were found. We also identified correlates of such differences suitable for real-time assessment. These variations can be largely attributed to changes in extracerebral thickness and should be taken into account to optimize probe placement in experimental settings.

Deep neural network classification of in vivo burn injuries with different etiologies using terahertz time-domain spectral imaging.

Abstract: Thermal injuries can occur due to direct exposure to hot objects or liquids, flames, electricity, solar energy and several other sources. If the resulting injury is a deep partial thickness burn, the accuracy of a physician's clinical assessment is as low as 50-76% in determining the healing outcome. In this study, we show that the Terahertz Portable Handheld Spectral Reflection (THz-PHASR) Scanner combined with a deep neural network classification algorithm can accurately differentiate between partial-, deep partial-, and full-thickness burns 1-hour post injury, regardless of the etiology, scanner geometry, or THz spectroscopy sampling method (ROC-AUC = 91%, 88%, and 86%, respectively). The neural network diagnostic method simplifies the classification process by directly using the pre-processed THz spectra and removing the need for any hyperspectral feature extraction. Our results show that deep learning methods based on THz time-domain spectroscopy (THz-TDS) measurements can be used to guide clinical treatment plans based on objective and accurate classification of burn injuries.

Evaluation of the robustness of cerebral oximetry to variations in skin pigmentation using a tissue-simulating phantom.

Abstract: Clinical studies have demonstrated that epidermal pigmentation level can affect cerebral oximetry measurements. To evaluate the robustness of these devices, we have developed a phantom-based test method that includes an epidermis-simulating layer with several melanin concentrations and a 3D-printed cerebrovascular module. Measurements were performed with neonatal, pediatric and adult sensors from two commercial oximeters, where neonatal probes had shorter source-detector separation distances. Referenced blood oxygenation levels ranged from 30 to 90%. Cerebral oximeter outputs exhibited a consistent decrease in saturation level with simulated melanin content; this effect was greatest at low saturation levels, producing a change of up to 15%. Dependence on pigmentation was strongest in a neonatal sensor, possibly due to its high reflectivity. Overall, our findings indicate that a modular channel-array phantom approach can provide a practical tool for assessing the impact of skin pigmentation on cerebral oximeter performance and that modifications to algorithms and/or instrumentation may be needed to mitigate pigmentation bias.

Real-time OCT image denoising using a self-fusion neural network.

Abstract: Optical coherence tomography (OCT) has become the gold standard for ophthalmic diagnostic imaging. However, clinical OCT image-quality is highly variable and limited visualization can introduce errors in the quantitative analysis of anatomic and pathologic features-of-interest. Frame-averaging is a standard method for improving image-quality, however, frame-averaging in the presence of bulk-motion can degrade lateral resolution and prolongs total acquisition time. We recently introduced a method called self-fusion, which reduces speckle noise and enhances OCT signal-to-noise ratio (SNR) by using similarity between from adjacent frames and is more robust to motion-artifacts than frame-averaging. However, since self-fusion is based on deformable registration, it is computationally expensive. In this study a convolutional neural network was implemented to offset the computational overhead of self-fusion and perform OCT denoising in real-time. The self-fusion network was pretrained to fuse 3 frames to achieve near video-rate frame-rates. Our results showed a clear gain in peak SNR in the self-fused images over both the raw and frame-averaged OCT B-scans. This approach delivers a fast and robust OCT denoising alternative to frame-averaging without the need for repeated image acquisition. Real-time self-fusion image enhancement will enable improved localization of OCT field-of-view relative to features-of-interest and improved sensitivity for anatomic features of disease.

Evaluation of resonant scanning as a high-speed imaging technique for two-photon imaging of cortical vasculature

Abstract: We demonstrate a simple, low-cost two-photon microscope design with both galvo-galvo and resonant-galvo scanning capabilities. We quantify and compare the signal-to-noise ratios and imaging speeds of the galvo-galvo and resonant-galvo scanning modes when used for murine neurovascular imaging. The two scanning modes perform as expected under shot-noise limited detection and are found to achieve comparable signal-to-noise ratios. Resonant-galvo scanning is capable of reaching desired signal-to-noise ratios using less acquisition time when higher excitation power can be used. Given equal excitation power and total pixel dwell time between the two methods, galvo-galvo scanning outperforms resonant-galvo scanning in image quality when detection deviates from being shot-noise limited.

In vivo assessment of inflammatory bowel disease in rats with ultrahigh-resolution colonoscopic OCT.

Abstract: A technology capable of high-resolution, label-free imaging of subtle pathology in vivo during colonoscopy is imperative for the early detection of disease and the performance of accurate biopsies. While colonoscopic OCT has been developed to visualize colonic microstructures beyond the mucosal surface, its clinical potential remains limited by sub-optimal resolution (∼6.5 µm in tissue), inadequate imaging contrast, and a lack of high-resolution OCT criteria for lesion detection. In this study, we developed an ultrahigh-resolution (UHR) colonoscopic OCT and evaluated its ability to volumetrically visualize and identify the pathological features of inflammatory bowel disease (IBD) in a rat model. Owing to its improved resolution (∼1.7 µm in tissue) and enhanced contrast, UHR colonoscopic OCT can accurately delineate fine colonic microstructures and identify the pathophysiological characteristics of IBD in vivo. By using a quantitative optical attenuation map, UHR colonoscopic OCT is able to differentiate diseased tissue (such as crypt distortion and microabscess) from normal colonic mucosa over a large field of view in vivo. Our results suggest the clinical potential of UHR colonoscopic OCT for in vivo assessment of IBD pathology.

Miniature structured illumination microscope for in vivo 3D imaging of brain structures with optical sectioning.

Abstract: We present a high-resolution miniature, light-weight fluorescence microscope with electrowetting lens and onboard CMOS for high resolution volumetric imaging and structured illumination for rejection of out-of-focus and scattered light. The miniature microscope (SIMscope3D) delivers structured light using a coherent fiber bundle to obtain optical sectioning with an axial resolution of 18 µm. Volumetric imaging of eGFP labeled cells in fixed mouse brain tissue at depths up to 260 µm is demonstrated. The functionality of SIMscope3D to provide background free 3D imaging is shown by recording time series of microglia dynamics in awake mice at depths up to 120 µm in the brain.

Fast whole-brain imaging of seizures in zebrafish larvae by two-photon light-sheet microscopy

Abstract: Light-sheet fluorescence microscopy (LSFM) enables real-time whole-brain functional imaging in zebrafish larvae. Conventional one-photon LSFM can however induce undesirable visual stimulation due to the use of visible excitation light. The use of two-photon (2P) excitation, employing near-infrared invisible light, provides unbiased investigation of neuronal circuit dynamics. However, due to the low efficiency of the 2P absorption process, the imaging speed of this technique is typically limited by the signal-to-noise-ratio. Here, we describe a 2P LSFM setup designed for non-invasive imaging that enables quintuplicating state-of-the-art volumetric acquisition rate of the larval zebrafish brain (5 Hz) while keeping low the laser intensity on the specimen. We applied our system to the study of pharmacologically-induced acute seizures, characterizing the spatial-temporal dynamics of pathological activity and describing for the first time the appearance of caudo-rostral ictal waves (CRIWs).

Stress management using fNIRS and binaural beats stimulation.

Abstract: In this study, we investigate the effectiveness of binaural beats stimulation (BBs) in enhancing cognitive vigilance and mitigating mental stress level at the workplace. We developed an experimental protocol under four cognitive conditions: high vigilance (HV), vigilance enhancement (VE), mental stress (MS) and stress mitigation (SM). The VE and SM conditions were achieved by listening to 16 Hz of BBs. We assessed the four cognitive conditions using salivary alpha-amylase, behavioral responses, and Functional Near-Infrared Spectroscopy (fNIRS). We quantified the vigilance and stress levels using the reaction time (RT) to stimuli, accuracy of detection, and the functional connectivity metrics of the fNIRS estimated by Phase Locking Values (PLV). We propose using the orthogonal minimum spanning tree (OMST) to determine the true connectivity network patterns of the PLV. Our results show that listening to 16-Hz BBs has significantly reduced the level of alpha amylase by 44%, reduced the RT to stimuli by 20% and increased the accuracy of target detection by 25%, (p < 0.001). The analysis of the connectivity network across the four different cognitive conditions revealed several statistically significant trends. Specifically, a significant increase in connectivity between the right and left dorsolateral prefrontal cortex (DLPFC) areas and left orbitofrontal cortex was found during the vigilance enhancement condition compared to the high vigilance. Likewise, similar patterns were found between the right and left DLPFC, orbitofrontal cortex, right ventrolateral prefrontal cortex (VLPFC) and right frontopolar PFC (prefrontal cortex) area during stress mitigation compared to mental stress. Furthermore, the connectivity network under stress condition alone showed significant connectivity increase between the VLPFC and DLPFC compared to other areas. The laterality index demonstrated left frontal laterality under high vigilance and VE conditions, and right DLPFC and left frontopolar PFC while under mental stress. Overall, our results showed that BBs can be used for vigilance enhancement and stress mitigation.

Line-scanning SD-OCT for in-vivo, non-contact, volumetric, cellular resolution imaging of the human cornea and limbus.

Abstract: In-vivo, non-contact, volumetric imaging of the cellular and sub-cellular structure of the human cornea and limbus with optical coherence tomography (OCT) is challenging due to involuntary eye motion that introduces both motion artifacts and blur in the OCT images. Here we present the design of a line-scanning (LS) spectral-domain (SD) optical coherence tomography system that combines 2 × 3 × 1.7 µm (x, y, z) resolution in biological tissue with an image acquisition rate of ∼2,500 fps, and demonstrate its ability to image in-vivo and without contact with the tissue surface, the cellular structure of the human anterior segment tissues. Volumetric LS-SD-OCT images acquired over a field-of-view (FOV) of 0.7 mm × 1.4 mm reveal fine morphological details in the healthy human cornea, such as epithelial and endothelial cells, sub-basal nerves, as well as the cellular structure of the limbal crypts, the palisades of Vogt (POVs) and the blood microvasculature of the human limbus. LS-SD-OCT is a promising technology that can assist ophthalmologists with the early diagnostics and optimal treatment planning of ocular diseases affecting the human anterior eye.

Multi-scale laser speckle contrast imaging of microcirculatory vasoreactivity.

Abstract: Laser speckle contrast imaging is a robust and versatile blood flow imaging tool in basic and clinical research for its relatively simple construction and ease of customization. One of its key features is the scalability of the imaged field of view. With minimal changes to the system or analysis, laser speckle contrast imaging allows for high-resolution blood flow imaging through cranial windows or low-resolution perfusion visualization of perfusion over large areas, e.g. in human skin. We further utilize this feature and introduce a multi-scale laser speckle contrast imaging system, which we apply to study vasoreactivity in renal microcirculation. We combine high resolution (small field of view) to segment blood flow in individual vessels with low resolution (large field of view) to monitor global blood flow changes across the renal surface. Furthermore, we compare their performance when analyzing blood flow dynamics potentially associated with a single nephron and show that the previously published approaches, based on low-zoom imaging alone, provide inaccurate results in such applications.

Illumination angle correction during image acquisition in light-sheet fluorescence microscopy using deep learning.

Abstract: Light-sheet fluorescence microscopy (LSFM) is a high-speed imaging technique that provides optical sectioning with reduced photodamage. LSFM is routinely used in life sciences for live cell imaging and for capturing large volumes of cleared tissues. LSFM has a unique configuration, in which the illumination and detection paths are separated and perpendicular to each other. As such, the image quality, especially at high resolution, largely depends on the degree of overlap between the detection focal plane and the illuminating beam. However, spatial heterogeneity within the sample, curved specimen boundaries, and mismatch of refractive index between tissues and immersion media can refract the well-aligned illumination beam. This refraction can cause extensive blur and non-uniform image quality over the imaged field-of-view. To address these issues, we tested a deep learning-based approach to estimate the angular error of the illumination beam relative to the detection focal plane. The illumination beam was then corrected using a pair of galvo scanners, and the correction significantly improved the image quality across the entire field-of-view. The angular estimation was based on calculating the defocus level on a pixel level within the image using two defocused images. Overall, our study provides a framework that can correct the angle of the light-sheet and improve the overall image quality in high-resolution LSFM 3D image acquisition.

Light-adapted flicker optoretinograms captured with a spatio-temporal optical coherence-tomography (STOC-T) system.

Abstract: For many years electroretinography (ERG) has been used for obtaining information about the retinal physiological function. More recently, a new technique called optoretinography (ORG) has been developed. In one form of this technique, the physiological response of retinal photoreceptors to visible light, resulting in a nanometric photoreceptor optical path length change, is measured by phase-sensitive optical coherence tomography (OCT). To date, a limited number of studies with phase-based ORG measured the retinal response to a flickering light stimulation. In this work, we use a spatio-temporal optical coherence tomography (STOC-T) system to capture optoretinograms with a flickering stimulus over a 1.7 × 0.85 mm2 area of a light-adapted retina located between the fovea and the optic nerve. We show that we can detect statistically-significant differences in the photoreceptor optical path length (OPL) modulation amplitudes in response to different flicker frequencies and with better signal to noise ratios (SNRs) than for a dark-adapted eye. We also demonstrate the ability to spatially map such response to a patterned stimulus with light stripes flickering at different frequencies, highlighting the prospect of characterizing the spatially-resolved temporal-frequency response of the retina with ORG.

Quantitative Jones matrix imaging using vectorial Fourier ptychography

Abstract: This paper presents a microscopic imaging technique that uses variable-angle illumination to recover the complex polarimetric properties of a specimen at high resolution and over a large field-of-view. The approach extends Fourier ptychography, which is a synthetic aperture-based imaging approach to improve resolution with phaseless measurements, to additionally account for the vectorial nature of light. After images are acquired using a standard microscope outfitted with an LED illumination array and two polarizers, our vectorial Fourier ptychography (vFP) algorithm solves for the complex 2x2 Jones matrix of the anisotropic specimen of interest at each resolved spatial location. We introduce a new sequential Gauss-Newton-based solver that additionally jointly estimates and removes polarization-dependent imaging system aberrations. We demonstrate effective vFP performance by generating large-area (29 mm2), high-resolution (1.24 μm full-pitch) reconstructions of sample absorption, phase, orientation, diattenuation, and retardance for a variety of calibration samples and biological specimens.

Retinal layer segmentation in optical coherence tomography (OCT) using a 3D deep-convolutional regression network for patients with age-related macular degeneration.

Abstract: Introduction - Retinal layer segmentation in optical coherence tomography (OCT) images is an important approach for detecting and prognosing disease. Automating segmentation using robust machine learning techniques lead to computationally efficient solutions and significantly reduces the cost of labor-intensive labeling, which is traditionally performed by trained graders at a reading center, sometimes aided by semi-automated algorithms. Although several algorithms have been proposed since the revival of deep learning, eyes with severe pathological conditions continue to challenge fully automated segmentation approaches. There remains an opportunity to leverage the underlying spatial correlations between the retinal surfaces in the segmentation approach. Methods - Some of these proposed traditional methods can be expanded to utilize the three-dimensional spatial context governing the retinal image volumes by replacing the use of 2D filters with 3D filters. Towards this purpose, we propose a spatial-context, continuity and anatomical relationship preserving semantic segmentation algorithm, which utilizes the 3D spatial context from the image volumes with the use of 3D filters. We propose a 3D deep neural network capable of learning the surface positions of the layers in the retinal volumes. Results - We utilize a dataset of OCT images from patients with Age-related Macular Degeneration (AMD) to assess performance of our model and provide both qualitative (including segmentation maps and thickness maps) and quantitative (including error metric comparisons and volumetric comparisons) results, which demonstrate that our proposed method performs favorably even for eyes with pathological changes caused by severe retinal diseases. The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for patients with a wide range of AMD severity scores (0-11) were within 0.84±0.41 and 1.33±0.73 pixels, respectively, which are significantly better than some of the other state-of-the-art algorithms. Conclusion - The results demonstrate the utility of extracting features from the entire OCT volume by treating the volume as a correlated entity and show the benefit of utilizing 3D autoencoder based regression networks for smoothing the approximated retinal layers by inducing shape based regularization constraints.

Diatom biosilica in plasmonics: applications in sensing, diagnostics and therapeutics [Invited].

Abstract: Several living organisms are able to synthesize complex nanostructures provided with peculiar physical and chemical properties by means of finely-tuned, genetically controlled biomineralization processes. Frustules, in particular, are micro- and nano-structured silica shells produced by ubiquitous diatom microalgae, whose optical properties have been recently exploited in photonics, solar energy harvesting, and biosensing. Metallization of diatom biosilica, both in the shape of intact frustules or diatomite particles, can trigger plasmonic effects that in turn can find application in high-sensitive detection platforms, allowing to obtain effective nanosensors at low cost and on a large scale. The aim of the present review article is to provide a wide, complete overview on the main metallization techniques applied to diatom biosilica and on the principal applications of diatom-based plasmonic devices mainly but not exclusively in the fields of biochemical sensing, diagnostics and therapeutics.

Polarization imaging-based radiomics approach for the staging of liver fibrosis.

Abstract: Mueller matrix imaging contains abundant biological microstructure information and has shown promising potential in clinical applications. Compared with the ordinary unpolarized light microscopy that relies on the spatial resolution to reveal detailed histological features, Mueller matrix imaging encodes rich information on the microstructures even at low-resolution and wide-field conditions. Accurate staging of liver fibrosis is essential for the therapeutic diagnosis and prognosis of chronic liver diseases. In the clinic, pathologists commonly use semiquantitative numerical scoring systems to determine the stages of liver fibrosis based on the visualization of stained characteristic morphological changes, which require skilled staining technicians and well-trained pathologists. A polarization imaging based quantitative diagnostic method can help to reduce the time-consuming multiple staining processes and provide quantitative information to facilitate the accurate staging of liver fibrosis. In this study, we report a polarization imaging based radiomics approach to provide quantitative diagnostic features for the staging of liver fibrosis. Comparisons between polarization image features under a 4× objective lens with H&E image features under 4×, 10×, 20×, and 40× objective lenses were performed to highlight the superiority of the high dimensional polarization image features in the characterization of the histological microstructures of liver fibrosis tissues at low-resolution and wide-field conditions.

Spatial resolution in optical coherence elastography of bounded media.

Abstract: Dynamic optical coherence elastography (OCE) tracks mechanical wave propagation in the subsurface region of tissue to image its shear modulus. For bulk shear waves, the lateral resolution of the reconstructed modulus map (i.e., elastographic resolution) can approach that of optical coherence tomography (OCT), typically a few tens of microns. Here we perform comprehensive numerical simulations and acoustic micro-tapping OCE experiments to show that for the typical situation of guided wave propagation in bounded media, such as cornea, the elastographic resolution cannot reach the OCT resolution and is mainly defined by the thickness of the bounded tissue layer. We considered the excitation of both broadband and quasi-harmonic guided waves in a bounded, isotropic medium. Leveraging the properties of broadband pulses, a robust method for modulus reconstruction with minimum artifacts at interfaces is demonstrated. In contrast, tissue bounding creates large instabilities in the phase of harmonic waves, leading to serious artifacts in modulus reconstructions.

In vivo assessment of corneal biomechanics under a localized cross-linking treatment using confocal air-coupled optical coherence elastography.

Abstract: The localized application of the riboflavin/UV-A collagen cross-linking (UV-CXL) corneal treatment has been proposed to concentrate the stiffening process only in the compromised regions of the cornea by limiting the epithelium removal and irradiation area. However, current clinical screening devices dedicated to measuring corneal biomechanics cannot provide maps nor spatial-dependent changes of elasticity in corneas when treated locally with UV-CXL. In this study, we leverage our previously reported confocal air-coupled ultrasonic optical coherence elastography (ACUS-OCE) probe to study local changes of corneal elasticity in three cases: untreated, half-CXL-treated, and full-CXL-treated in vivo rabbit corneas (n = 8). We found a significant increase of the shear modulus in the half-treated (>450%) and full-treated (>650%) corneal regions when compared to the non-treated cases. Therefore, the ACUS-OCE technology possesses a great potential in detecting spatially-dependent mechanical properties of the cornea at multiple meridians and generating elastography maps that are clinically relevant for patient-specific treatment planning and monitoring of UV-CXL procedures.

Fourier ptychography multi-parameunter neural network with composite physical priori optimization.

Abstract: Fourier ptychography microscopy(FPM) is a recently developed computational imaging approach for microscopic super-resolution imaging. By turning on each light-emitting-diode (LED) located on different position on the LED array sequentially and acquiring the corresponding images that contain different spatial frequency components, high spatial resolution and quantitative phase imaging can be achieved in the case of large field-of-view. Nevertheless, FPM has high requirements for the system construction and data acquisition processes, such as precise LEDs position, accurate focusing and appropriate exposure time, which brings many limitations to its practical applications. In this paper, inspired by artificial neural network, we propose a Fourier ptychography multi-parameter neural network (FPMN) with composite physical prior optimization. A hybrid parameter determination strategy combining physical imaging model and data-driven network training is proposed to recover the multi layers of the network corresponding to different physical parameters, including sample complex function, system pupil function, defocus distance, LED array position deviation and illumination intensity fluctuation, etc. Among these parameters, LED array position deviation is recovered based on the features of brightfield to darkfield transition low-resolution images while the others are recovered in the process of training of the neural network. The feasibility and effectiveness of FPMN are verified through simulations and actual experiments. Therefore FPMN can evidently reduce the requirement for practical applications of FPM.

Depth-resolved visualization and automated quantification of hyperreflective foci on OCT scans using optical attenuation coefficients.

Abstract: An automated depth-resolved algorithm using optical attenuation coefficients (OACs) was developed to visualize, localize, and quantify hyperreflective foci (HRF) seen on OCT imaging that are associated with macular hyperpigmentation and represent an increased risk of disease progression in age related macular degeneration. To achieve this, we first transformed the OCT scans to linear representation, which were then contrasted by OACs. HRF were visualized and localized within the entire scan by differentiating HRF within the retina from HRF along the retinal pigment epithelium (RPE). The total pigment burden was quantified using the en face sum projection of an OAC slab between the inner limiting membrane (ILM) to Bruch's membrane (BM). The manual total pigment burden measurements were also obtained by combining manual outlines of HRF in the B-scans with the total area of hypotransmission defects outlined on sub-RPE slabs, which was used as the reference to compare with those obtained from the automated algorithm. 6×6 mm swept-source OCT scans were collected from a total of 49 eyes from 42 patients with macular HRF. We demonstrate that the algorithm was able to automatically distinguish between HRF within the retina and HRF along the RPE. In 24 test eyes, the total pigment burden measurements by the automated algorithm were compared with measurements obtained from manual segmentations. A significant correlation was found between the total pigment area measurements from the automated and manual segmentations (P < 0.001). The proposed automated algorithm based on OACs should be useful in studying eye diseases involving HRF.

Multiplexed COVID-19 antibody quantification from human sera using label-free nanoplasmonic biosensors.

Abstract: Serological assays that can reveal immune status against COVID-19 play a critical role in informing individual and public healthcare decisions. Currently, antibody tests are performed in central clinical laboratories, limiting broad access to diverse populations. Here we report a multiplexed and label-free nanoplasmonic biosensor that can be deployed for point-of-care antibody profiling. Our optical imaging-based approach can simultaneously quantify antigen-specific antibody response against SARS-CoV-2 spike and nucleocapsid proteins from 50 µL of human sera. To enhance the dynamic range, we employed multivariate data processing and multi-color imaging and achieved a quantification range of 0.1-100 µg/mL. We measured sera from a COVID-19 acute and convalescent (N = 24) patient cohort and negative controls (N = 5) and showed highly sensitive and specific past-infection diagnosis. Our results were benchmarked against an electrochemiluminescence assay and showed good concordance (R∼0.87). Our integrated nanoplasmonic biosensor has the potential to be used in epidemiological sero-profiling and vaccine studies.

Multi-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning.

Abstract: We demonstrate a convolutional neural network (CNN) for multi-class breast tissue classification as adipose tissue, benign dense tissue, or malignant tissue, using multi-channel optical coherence tomography (OCT) and attenuation images, and a novel Matthews correlation coefficient (MCC)-based loss function that correlates more strongly with performance metrics than the commonly used cross-entropy loss. We hypothesized that using multi-channel images would increase tumor detection performance compared to using OCT alone. 5,804 images from 29 patients were used to fine-tune a pre-trained ResNet-18 network. Adding attenuation images to OCT images yields statistically significant improvements in several performance metrics, including benign dense tissue sensitivity (68.0% versus 59.6%), malignant tissue positive predictive value (PPV) (79.4% versus 75.5%), and total accuracy (85.4% versus 83.3%), indicating that the additional contrast from attenuation imaging is most beneficial for distinguishing between benign dense tissue and malignant tissue.

Image-to-image translation of label-free molecular vibrational images for a histopathological review using the UNet+/seg-cGAN model.

Abstract: Translating images generated by label-free microscopy imaging, such as Coherent Anti-Stokes Raman Scattering (CARS), into more familiar clinical presentations of histopathological images will help the adoption of real-time, spectrally resolved label-free imaging in clinical diagnosis. Generative adversarial networks (GAN) have made great progress in image generation and translation, but have been criticized for lacking precision. In particular, GAN has often misinterpreted image information and identified incorrect content categories during image translation of microscopy scans. To alleviate this problem, we developed a new Pix2pix GAN model that simultaneously learns classifying contents in the images from a segmentation dataset during the image translation training. Our model integrates UNet+ with seg-cGAN, conditional generative adversarial networks with partial regularization of segmentation. Technical innovations of the UNet+/seg-cGAN model include: (1) replacing UNet with UNet+ as the Pix2pix cGAN's generator to enhance pattern extraction and richness of the gradient, and (2) applying the partial regularization strategy to train a part of the generator network as the segmentation sub-model on a separate segmentation dataset, thus enabling the model to identify correct content categories during image translation. The quality of histopathological-like images generated based on label-free CARS images has been improved significantly.