Significance: The application of near-infrared spectroscopy (NIRS) to determine the concentrations of tissue chromophores has typically relied on three alternative technological approaches: continuous-wave, frequency-domain, and time-domain. It is often the case that uncertain and variable coupling of light into and out of the skin surface renders absolute measurements unreliable, and NIRS methods are mostly used to measure changes of chromophore concentrations and of physiological parameters such as blood volume and oxygenation.

Aim: The aim has been to investigate whether an approach based on a wavelength-modulated source may enable measurements to be acquired, which are independent of surface coupling and may facilitate derivation of absolute values of tissue parameters.

Approach: An analysis is performed using the modified Beer–Lambert law.

Results: It is shown that the relative modulation in detected intensity resulting from a wavelength-modulated source could be used to estimate absolute concentrations of chromophores if unknown surface coupling losses and geometrical factors are insensitive to small changes in wavelength.

Conclusions: Wavelength modulated NIRS could be an effective tool for quantitative in vivo analysis of tissues, although it may be technically challenging.

Significance: Optical coherence tomography (OCT) has been widely used in clinical studies. However, the image quality of OCT decreases with increasing imaging depth since the light is rapidly attenuated in biological tissues.

Aim: We present a compensation approach to preserve weak high-frequency signals from deep structures and compress the dynamic range of the detected signal for superior analog-to-digital conversion and image display capability.

Approach: A homemade frequency gain compensation amplifier is designed and fabricated to amplify the electrical signal from a balanced photodetector and compensate for the signal attenuation in swept-source OCT (SSOCT).

Results: It is demonstrated in imaging various objects that this cost-efficient technique effectively enhances the contrast of the deep tissue image.

Conclusions: A frequency gain compensation amplifier is designed and used to compress the dynamic range of the electrical signal detected by the photodetector of an SSOCT system, which enables weak signals from deep structures to be acquired by the ADC and displayed with enhanced local contrast.

Significance: The necessity to use exogenous probes for optical oxygen measurements in radiotherapy poses challenges for clinical applications. Options for implantable probe biotechnology need to be improved to alleviate toxicity concerns in human use and facilitate translation to clinical trial use.

Aim: To develop an implantable oxygen sensor containing a phosphorescent oxygen probe such that the overall administered dose of the probe would be below the Federal Drug Administration (FDA)-prescribed microdose level, and the sensor would provide local high-intensity signal for longitudinal measurements of tissue pO₂.

Approach: PtG4, an oxygen quenched dendritic molecule, was mixed into an agarose matrix at 100  μM concentration, allowing for local injection into tumors at the total dose of 10 nmol per animal, forming a gel at the site of injection. Cherenkov-excited luminescence imaging (CELI) was used to acquire the phosphorescence and provide intratumoral pO₂.

Results: Although PtG4 does not form covalent bonds with agarose and gradually leaches out into the surrounding tissue, its retention time within the gel was sufficiently long to demonstrate the capability to measure intratumoral pO₂ with the implantable gel sensors. The sensor’s performance was first evaluated in vitro in tissue simulation phantoms, and then the sensor was used to measure changes in oxygen in MDA-MB-231 tumors during hypofractionated radiotherapy.

Conclusions: Our study demonstrates that implantable oxygen sensors in combination with CELI present a promising approach for quantifying oxygen changes during the course of radiation therapy and thus for evaluating the tumor response to radiation. By improving the design of the gel–probe composition in order to prevent leaching of the probe into the tissue, biosensors can be created that should allow longitudinal oxygen measurements in tumors by means of CELI while using FDA-compliant microdose levels of the probe and thus lowering toxicity concerns.

Biomedical photoacoustic tomography, which can provide high-resolution 3D soft tissue images based on optical absorption, has advanced to the stage at which translation from the laboratory to clinical settings is becoming possible. The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges. There are many classical approaches to image reconstruction, but ameliorating the effects of incomplete or imperfect data through the incorporation of accurate priors is challenging and leads to slow algorithms. Recently, the application of deep learning (DL), or deep neural networks, to this problem has received a great deal of attention. We review the literature on learned image reconstruction, summarizing the current trends and explain how these approaches fit within, and to some extent have arisen from, a framework that encompasses classical reconstruction methods. In particular, it shows how these techniques can be understood from a Bayesian perspective, providing useful insights. We also provide a concise tutorial demonstration of three prototypical approaches to learned image reconstruction. The code and data sets for these demonstrations are available to researchers. It is anticipated that it is in in vivo applications—where data may be sparse, fast imaging critical, and priors difficult to construct by hand—that DL will have the most impact. With this in mind, we conclude with some indications of possible future research directions.

Significance: Spatial frequency domain imaging (SFDI) is an imaging modality that projects spatially modulated light patterns to determine optical property maps for absorption and reduced scattering of biological tissue via a pixel-by-pixel data acquisition and analysis procedure. Compressive sensing (CS) is a signal processing methodology which aims to reproduce the original signal with a reduced number of measurements, addressing the pixel-wise nature of SFDI. These methodologies have been combined for complex heterogenous data in both the image detection and data analysis stage in a compressive sensing SFDI (cs-SFDI) approach, showing reduction in both the data acquisition and overall computational time.

Aim: Application of CS in SFDI data acquisition and image reconstruction significantly improves data collection and image recovery time without loss of quantitative accuracy.

Approach: cs-SFDI has been applied to an increased heterogenic sample from the AppSFDI data set (back of the hand), highlighting the increased number of CS measurements required as compared to simple phantoms to accurately obtain optical property maps. A novel application of CS to the parameter recovery stage of image analysis has also been developed and validated.

Results: Dimensionality reduction has been demonstrated using the increased heterogenic sample at both the acquisition and analysis stages. A data reduction of 30% for the cs-SFDI and up to 80% for the parameter recover was achieved as compared to traditional SFDI, while maintaining an error of <10  %   for the recovered optical property maps.

Conclusion: The application of data reduction through CS demonstrates additional capabilities for multi- and hyperspectral SFDI, providing advanced optical and physiological property maps.

Significance: Diffuse reflectance spectroscopy (DRS) is frequently used to assess oxygen saturation and hemoglobin concentration in living tissue. Methods solving the inverse problem may include time-consuming nonlinear optimization or artificial neural networks (ANN) determining the absorption coefficient one wavelength at a time.

Aim: To present an ANN-based method that directly outputs the oxygen saturation and the hemoglobin concentration using the shape of the measured spectra as input.

Approach: A probe-based DRS setup with dual source-detector separations in the visible wavelength range was used. ANNs were trained on spectra generated from a three-layer tissue model with oxygen saturation and hemoglobin concentration as target.

Results: Modeled evaluation data with realistic measurement noise showed an absolute root-mean-square (RMS) deviation of 5.1% units for oxygen saturation estimation. The relative RMS deviation for hemoglobin concentration was 13%. This accuracy is at least twice as good as our previous nonlinear optimization method. On blood-intralipid phantoms, the RMS deviation from the oxygen saturation derived from partial oxygen pressure measurements was 5.3% and 1.6% in two separate measurement series. Results during brachial occlusion showed expected patterns.

Conclusions: The presented method, directly assessing oxygen saturation and hemoglobin concentration, is fast, accurate, and robust to noise.

Significance: Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transparency to be widely adopted as stand-alone diagnostics.

Aim: We aimed to create a transparent machine learning technology (i.e., not deep learning) to discriminate melanomas from nevi in dermoscopy images and an interface for sensory cue integration.

Approach: Imaging biomarker cues (IBCs) fed ensemble machine learning classifier (Eclass) training while raw images fed deep learning classifier training. We compared the areas under the diagnostic receiver operator curves.

Results: Our interpretable machine learning algorithm outperformed the leading deep-learning approach 75% of the time. The user interface displayed only the diagnostic imaging biomarkers as IBCs.

Conclusions: From a translational perspective, Eclass is better than convolutional machine learning diagnosis in that physicians can embrace it faster than black box outputs. Imaging biomarkers cues may be used during sensory cue integration in clinical screening. Our method may be applied to other image-based diagnostic analyses, including pathology and radiology.

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

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

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

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

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

Significance: Gas in scattering media absorption spectroscopy (GASMAS) is a technique for gas sensing in cavities surrounded by scattering materials. GASMAS could be translated to the clinic to monitor lung function continuously and noninvasively in neonates. Accurate tissue phantoms are essential to assess the strengths and limitations of gas spectroscopy in gas-containing cavities in the human body.

Aim: The aim is to develop a detailed protocol to produce a long-lasting, multistructure tissue phantom of the thorax of a neonate. The phantom mimics the geometry and the optical properties of the main organs of the thorax and has an empty pulmonary cavity that facilitates GASMAS monitoring of gas content.

Approach: The anatomic geometry of heart, lungs, bones, muscle, fat, and skin was obtained from a neonatal computed tomography scan. Once segmented, organs were 3D printed and used to create negative rubber molds. The entire thorax was built in phantom material (silicone as matrix, black ink as absorber, and silica microspheres as scatters) by placing all phantom organs inside the muscle structure. Our phantom recipe was customized by mixing specific ratios of ink and spheres to match the optical properties of the different organs that were consider to be homogeneous.

Results: An anthropomorphic thorax phantom with the desired optical properties (μ_a and μs′) at 760 nm was built and used to obtain “transdermal” GASMAS measurements of oxygen content within the lung cavity.

Conclusion: A protocol to build a robust optical phantom of the thorax of a neonate was used to conduct benchtop studies. This recipe can be implemented to reproduce the geometry and optical properties of any human or animal tissue.

Significance: The development of a technique allowing for non-invasive measurement of retinal blood flow (RBF) in humans is needed to understand many retinal vascular diseases (pathophysiology) and evaluate treatment with potential improvement of blood flow.

Aim: We developed and validated an absolute laser Doppler velocimeter (LDV) based on an adaptive optical fundus camera that provides simultaneously high-definition images of the fundus vessels and absolute maximal red blood cells (RBCs) velocity to calculate the absolute RBF.

Approach: This new absolute LDV is combined with the adaptive optics (AO) fundus camera (rtx1, Imagine Eyes^©, Orsay, France) outside its optical wavefront correction path. A 4-s recording includes 40 images, each synchronized with two Doppler shift power spectra. Image analysis provides a vessel diameter close to the probing beam, and the velocity of the RBCs in the vessels are extracted from the Doppler spectral analysis. A combination of these values gives an average of the absolute RBF.

Results: An in vitro experiment consisting of latex microspheres flowing in water through a glass capillary to simulate a blood vessel and in vivo measurements on six healthy humans was done to assess the device. In the in vitro experiment, the calculated flow varied between 1.75 and 25.9  μL  /  min and was highly correlated (r²  =  0.995) with the flow imposed by a syringe pump. In the in vivo experiment, the error between the flow in the parent vessel and the sum of the flow in the daughter vessels was between −11  %   and 36% (mean  ±  sd, 5.7  ±  18.5  %  ). RBF in the main temporal retinal veins of healthy subjects varied between 0.9 and 13.2  μL  /  min.

Conclusions: The AO LDV prototype allows for the real-time measurement of absolute RBF derived from the retinal vessel diameter and the maximum RBCs velocity in that vessel.

Significance: Placenta is an essential organ for fetal development and successful reproduction. Placental insufficiency can lead to fetal hypoxia and, in extreme cases anoxia, leading to fetal death. Of the 145 million deliveries per year worldwide, ∼15 million neonates are small for gestational age and, therefore, at risk for antepartum and intrapartum hypoxia. Clinical methods to assess placental function largely rely on the assessment of fetal heart rate changes but do not assess placental oxygenation. Near-infrared spectroscopy (NIRS) allows non-invasive, real-time assessment of tissue oxygenation in intact organs, which can be used to assess placental oxygenation. However, tissue optical properties can affect the accuracy of methods to measure tissue oxygenation.

Aim: This study was performed to estimate the scattering coefficient of the human placenta. We have computed the scattering coefficients of the human placenta for the range of 659 to 840 nm using two methods of diffuse reflectance spectroscopy (DRS).

Approach: Measurements were performed using an in-house DRS device and a well-established frequency-domain diffuse optical spectroscopic system (DOSI). Measurements were performed in eight placentas obtained after cesarean deliveries. Placentas were perfused with normal saline to minimize the effects of absorption due to blood. Three sites per placenta were measured. Absorption and scattering coefficients were then calculated from the measured reflectance using the random walk theory for DRS and frequency-domain algorithm for DOSI.

Results: Average reduced scattering coefficient (μs^′) was 0.943  ±  0.015  mm^−  1 at 760 nm and 0.831  ±  0.009  mm^−  1 at 840 nm, and a power function μs^′  =  1.6619 (λ/500  nm)^{−  1.426} was derived for the human placental scattering coefficient.

Conclusion: We report for the first time the scattering coefficient of the human placenta. This information can be used to assess baseline scattering and improve measurements of placental oxygen saturation with NIRS.

Significance: Optical coherence tomography (OCT) is widely used as a potential diagnostic tool for a variety of diseases including various types of cancer. However, sensitivity and specificity analyses of OCT in different cancers yield results varying from 11% to 100%. Hence, there is a need for more detailed statistical analysis of blinded reader studies.

Aim: Extensive statistical analysis is performed on results from a blinded study involving OCT of breast tumor margins to assess the impact of reader variability on sensitivity and specificity.

Approach: Five readers with varying levels of experience reading OCT images assessed 50 OCT images of breast tumor margins collected using an intraoperative OCT system. Statistical modeling and analysis was performed using the R language to analyze reader experience and variability.

Results: Statistical analysis showed that the readers’ prior experience with OCT images was directly related to the probability of the readers’ assessment agreeing with histology. Additionally, results from readers with prior experience specific to OCT in breast cancer had a higher probability of agreement with histology compared to readers with experience with OCT in other (noncancer) diseases.

Conclusions: The results from this study demonstrate the potential impact of reader training and experience in the assessment of sensitivity and specificity. They also demonstrate even greater potential improvement in diagnostic performance by combining results from multiple readers. These preliminary findings suggest valuable directions for further study.

Significance: The practicality of optical methods detecting tissue optical contrast (absorption, elastic and inelastic scattering, fluorescence) for surgical guidance is limited by interferences from blood pooling and the resulting partial or complete inability to interrogate cortex and blood vessels.

Aim: A multispectral diffuse reflectance technique was developed for intraoperative brain imaging of hemodynamic activity to automatically discriminate blood vessels, cortex, and bleeding at the brain surface.

Approach: A manual segmentation of blood pooling, cortex, and vessels allowed the identification of a frequency range in hemoglobin concentration variations associated with high optical signal in blood vessels and cortex but not in bleeding. Reflectance spectra were then used to automatically segment areas with and without hemodynamic activity as well as to discriminate blood from cortical areas.

Results: The frequency range associated with low-frequency hemodynamics and respiratory rate (0.03 to 0.3 Hz) exhibits the largest differences in signal amplitudes for bleeding, blood vessels, and cortex. A segmentation technique based on simulated reflectance spectra initially allowed discrimination of blood (bleeding and vessels) from cortical tissue. Then, a threshold applied to the low-frequency components from deoxyhemoglobin allowed the segmentation of bleeding from vessels. A study on the minimum acquisition time needed to discriminate all three components determined that ∼25  s was necessary to detect changes in the low-frequency range. Other frequency ranges such as heartbeat (1 to 1.7 Hz) can be used to reduce the acquisition time to few seconds but would necessitate optimizing instrumentation to ensure larger signal-to-noise ratios are achieved.

Conclusions: A method based on multispectral reflectance signals and low-frequency hemoglobin concentration changes can be used to distinguish bleeding, blood vessels, and cortex. This could be integrated into fiber optic probes to enhance signal specificity by providing users an indication of whether measurements are corrupted by blood pooling, an important confounding factor in biomedical optics applied to surgery.

Purpose: Unlike fluorescence imaging utilizing an external excitation source, Cherenkov emissions and Cherenkov-excited luminescence occur within a medium when irradiated with high-energy x-rays. Methods to improve the understanding of the lateral spread and axial depth distribution of these emissions are needed as an initial step to improve the overall system resolution.

Methods: Monte Carlo simulations were developed to investigate the lateral spread of thin sheets of high-energy sources and compared to experimental measurements of similar sources in water. Additional simulations of a multilayer skin model were used to investigate the limits of detection using both 6- and 18-MV x-ray sources with fluorescence excitation for inclusion depths up to 1 cm.

Results: Simulations comparing the lateral spread of high-energy sources show approximately 100  ×   higher optical yield from electrons than photons, although electrons showed a larger penumbra in both the simulations and experimental measurements. Cherenkov excitation has a roughly inverse wavelength squared dependence in intensity but is largely redshifted in excitation through any distance of tissue. The calculated emission spectra in tissue were convolved with a database of luminescent compounds to produce a computational ranking of potential Cherenkov-excited luminescence molecular contrast agents.

Conclusions: Models of thin x-ray and electron sources were compared with experimental measurements, showing similar trends in energy and source type. Surface detection of Cherenkov-excited luminescence appears to be limited by the mean free path of the luminescence emission, where for the given simulation only 2% of the inclusion emissions reached the surface from a depth of 7 mm in a multilayer tissue model.

Significance: Fluorescence guidance in cancer surgery (FGS) using molecular-targeted contrast agents is accelerating, yet the influence of individual patients’ physiology on the optimal time to perform surgery post-agent-injection is not fully understood.

Aim: Develop a mathematical framework and analytical expressions to estimate patient-specific time-to-maximum contrast after imaging agent administration for single- and paired-agent (coadministration of targeted and control agents) protocols.

Approach: The framework was validated in mouse subcutaneous xenograft studies for three classes of imaging agents: peptide, antibody mimetic, and antibody. Analytical expressions estimating time-to-maximum-tumor-discrimination potential were evaluated over a range of parameters using the validated framework for human cancer parameters.

Results: Correlations were observed between simulations and matched experiments and metrics of tumor discrimination potential (p  <  0.05). Based on human cancer physiology, times-to-maximum contrast for peptide and antibody mimetic agents were <200  min, >15  h for antibodies, on average. The analytical estimates of time-to-maximum tumor discrimination performance exhibited errors of <10  %   on average, whereas patient-to-patient variance is expected to be greater than 100%.

Conclusion: We demonstrated that analytical estimates of time-to-maximum contrast in FGS carried out patient-to-patient can outperform the population average time-to-maximum contrast used currently in clinical trials. Such estimates can be made with preoperative DCE-MRI (or similar) and knowledge of the targeted agent’s binding affinity.

Significance: Mueller matrix polarimetry can provide useful information about the function and structure of the extracellular matrix. A portable and low-cost system could facilitate the clinical assessment of cervical anomalies in low-resource settings.

Aim: We introduce a low-cost snapshot Mueller matrix polarimeter that does not require external power, has no moving parts, and can acquire a full Mueller matrix in ∼1  s, to conduct a feasibility study for cervical imaging in the low-resource setting.

Approach: A snapshot system based on two sets of Savart plates, a ring illuminator with polarizing elements (generating four polarization states), and one camera is introduced. Stokes vectors are formulated to recover the polarization properties of the sample. Then, using Mueller matrix decomposition, the depolarization and retardance information is extracted.

Results: We report the results on 16 healthy individuals (out of 22 patients imaged), whose Pap smear showed no malignant findings from mobile clinics in rural region of Mysore, India. The depolarization and retardance information was in agreement with previous reports.

Conclusions: We introduce an imaging system and conducted a feasibility study on healthy individuals. This work could futurely translate into diagnostic applications to provide a quantitative platform in the clinical environment (e.g., cervical cancer screening).

Significance: Multi-exposure laser speckle contrast imaging (MELSCI) estimates microcirculatory blood perfusion more accurately than single-exposure LSCI. However, the technique has been hampered by technical limitations due to massive data throughput requirements and nonlinear inverse search algorithms, limiting it to an offline technique where data must be postprocessed.

Aim: To present an MELSCI system capable of continuous acquisition and processing of MELSCI data, enabling real-time video-rate perfusion imaging with high accuracy.

Approach: The MELSCI algorithm was implemented in programmable hardware (field programmable gate array) closely interfaced to a high-speed CMOS sensor for real-time calculation. Perfusion images were estimated in real-time from the MELSCI data using an artificial neural network trained on simulated data. The MELSCI perfusion was compared to two existing single-exposure metrics both quantitatively in a controlled phantom experiment and qualitatively in vivo.

Results: The MELSCI perfusion shows higher signal dynamics compared to both single-exposure metrics, both spatially and temporally where heartbeat-related variations are resolved in much greater detail. The MELSCI perfusion is less susceptible to measurement noise and is more linear with respect to laser Doppler perfusion in the phantom experiment (R²  =  0.992).

Conclusions: The presented MELSCI system allows for real-time acquisition and calculation of high-quality perfusion at 15.6 frames per second.

Significance: Early detection of epithelial cancers and precancers/neoplasia in the presence of benign lesions is challenging due to the lack of robust in vivo imaging and biopsy guidance techniques. Label-free nonlinear optical microscopy (NLOM) has shown promise for optical biopsy through the detection of cellular and extracellular signatures of neoplasia. Although in vivo microscopy techniques continue to be developed, the surface area imaged in microscopy is limited by the field of view. FDA-approved widefield fluorescence (WF) imaging systems that capture autofluorescence signatures of neoplasia provide molecular information at large fields of view, which may complement the cytologic and architectural information provided by NLOM.

Aim: A multimodal imaging approach with high-sensitivity WF and high-resolution NLOM was investigated to identify and distinguish image-based features of neoplasia from normal and benign lesions.

Approach: In vivo label-free WF imaging and NLOM was performed in preclinical hamster models of oral neoplasia and inflammation. Analyses of WF imaging, NLOM imaging, and dual modality (WF combined with NLOM) were performed.

Results: WF imaging showed increased red-to-green autofluorescence ratio in neoplasia compared to inflammation and normal oral mucosa (p  <  0.01). In vivo assessment of the mucosal tissue with NLOM revealed subsurface cytologic (nuclear pleomorphism) and architectural (remodeling of extracellular matrix) atypia in histologically confirmed neoplastic tissue, which were not observed in inflammation or normal mucosa. Univariate and multivariate statistical analysis of macroscopic and microscopic image-based features indicated improved performance (94% sensitivity and 97% specificity) of a multiscale approach over WF alone, even in the presence of benign lesions (inflammation), a common confounding factor in diagnostics.

Conclusions: A multimodal imaging approach integrating strengths from WF and NLOM may be beneficial in identifying oral neoplasia. Our study could guide future studies on human oral neoplasia to further evaluate merits and limitations of multimodal workflows and inform the development of multiscale clinical imaging systems.

Significance: Our work advances the development of fiber-based polarization-sensitive optical coherence tomography (PS-OCT) by stabilizing the output polarization state of the light beam when the system is under environmental disturbance. While the fiber-based PS-OCT has been demonstrated previously, it remains a challenge for the traditional fiber-based PS-OCT to obtain a stable measurement when the optic fibers are disturbed by the environment. This important issue is addressed, paving the path for clinical translation of PS-OCT, which can provide a unique perspective of the biological samples.

Aim: Polarization maintaining common-path (CP) interferometer is fabricated with the goal of providing a stable fiber-based PS-OCT imaging system that is only responsive to the polarization changes generated by the sample, immune to environmental conditions.

Approach: The system is implemented by incorporating a CP interferometer together with polarization maintaining (PM) fibers. The PM fibers are used to preserve the two orthogonal linearly polarized components of the light during propagation. By sharing the CP in the sample and reference arms, any variations in phase retardation can be eliminated between the two channels in the PM fibers. The combination of the PM fiber and the CP interferometer ensures the stability of the output polarization state.

Results: The stability of the proposed PS-OCT system is tested when a periodically stressed disturbance is applied to the fibers within the system. Stable in vivo PS-OCT images of the mouse thigh are demonstrated.

Conclusions: We have demonstrated a stable fiber-based PS-OCT system that combined the PM fiber and the CP configuration together. We have shown that the output polarization states and the system sensitivity can keep stable over time under the environmental disturbances to the system.

Significance: Photoacoustic tomography (PAT) is a promising emergent modality for the screening and staging of breast cancer. To minimize barriers to clinical translation, it is common to develop PAT systems based upon existing ultrasound hardware, which can entail significant design challenges in terms of light delivery. This often results in inherently non-uniform fluence within the tissue and should be accounted for during image reconstruction.

Aim: We aim to integrate PAT into an automated breast ultrasound scanner with minimal change to the existing system.

Approach: We designed and implemented an illuminator that directs spatially non-uniform light to the tissue near the acquisition plane of the imaging array. We developed a graphics processing unit-accelerated reconstruction method, which accounts for this illumination geometry by modeling the structure of the light in the sample. We quantified the performance of this system using a custom, modular photoacoustic phantom and graphite rods embedded in chicken breast tissue.

Results: Our illuminator provides a fluence of 2.5  mJ cm^−  2 at the tissue surface, which was sufficient to attain a signal-to-noise ratio (SNR) of 8 dB at 2 cm in chicken breast tissue and image 0.25-mm features at depths of up to 3 cm in a medium with moderate optical scattering. Our reconstruction scheme is 200  ×   faster than a CPU implementation; it provides a 25% increase in SNR at 2 cm in chicken breast tissue and lowers image error by an average of 31% at imaging depths >1.5  cm compared with a method that does not account for the inhomogeneity of the illumination or the transducer directivity.

Conclusions: A fan-shaped illumination geometry is feasible for PAT; however, it is important to account for non-uniform fluence in illumination scenarios such as this. Future work will focus on increasing fluence and further optimizing the ultrasound hardware to improve SNR and overall image quality.

Significance: Optical coherence tomography (OCT) offers high spatial resolution and contrast for imaging intraoral structures, yet few studies have investigated its clinical feasibility for dental plaque and gingiva imaging in vivo. Furthermore, the accessibility is often limited to anterior teeth due to bulky imaging systems and probes.

Aim: A custom-designed, handheld probe-based, spectral-domain OCT system with an interchangeable attachment was developed to assess dental plaque and gingival health in a clinical setting.

Approach: Healthy volunteers and subjects with gingivitis and sufficient plaque were recruited. The handheld OCT system was operated by trained dental hygienists to acquire images of dental plaque and gingiva at various locations and after one-week use of oral hygiene products.

Results: The handheld OCT can access premolars, first molars, and lingual sides of teeth to visualize the plaque distribution. OCT intensity-based texture analysis revealed lower intensity from selected sites in subjects with gingivitis. The distribution of the dental plaque after one-week use of the oral hygiene products was compared, showing the capability of OCT as a longitudinal tracking tool.

Conclusions: OCT has a strong potential to display and assess dental plaque and gingiva in a clinical setting. Meanwhile, technological challenges remain to perform systematic longitudinal tracking and comparative analyses.

Significance: High temporal stability, wavelength independency, and scalable field of view (FOV) are the primary requirements of a quantitative phase microscopy (QPM) system. The high temporal stability of the system provides accurate measurement of minute membrane fluctuations of the biological cells that can be an indicator of disease diagnosis.

Aim: The main aim of this work is to develop a high temporal stable technique that can accurately quantify the cell’s dynamics such as membrane fluctuations of human erythrocytes. Further, the technique should be capable of acquiring scalable FOV and resolution at multiple wavelengths to make it viable for various biological applications.

Approach: We developed a single-element nearly common path, wavelength-independent, and scalable resolution/FOV QPM system to obtain temporally stable holograms/interferograms of the biological specimens.

Results: With the proposed system, the temporal stability is obtained ∼15  mrad without using any vibration isolation table. The capability of the proposed system is first demonstrated on USAF resolution chart and polystyrene spheres (4.5-μm diameter). Further, the system is implemented for single shot, wavelength-independent quantitative phase imaging of human red blood cells (RBCs) with scalable resolution using color CCD camera. The membrane fluctuation of healthy human RBCs is also measured and was found to be around 47 nm.

Conclusions: Contrary to its optical counterparts, the present system offers an energy efficient, cost effective, and simple way of generating object and reference beam for the development of common-path QPM. The present system provides the flexibility to the user to acquire multi-wavelength quantitative phase images at scalable FOV and resolution.

Significance: Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase images efficiently, their applications in diagnostic testing are limited by the lack of transparency. More interpretable results such as morphological and biochemical characteristics of individual RBCs are highly desirable.

Aim: An end-to-end deep-learning model was developed to efficiently discriminate thalassemic RBCs (tRBCs) from healthy RBCs (hRBCs) in quantitative phase images and segment RBCs for single-cell characterization.

Approach: Two-dimensional quantitative phase images of hRBCs and tRBCs were acquired using digital holographic microscopy. A mask region-based convolutional neural network (Mask R-CNN) model was trained to discriminate tRBCs and segment individual RBCs. Characterization of tRBCs was achieved utilizing SHapley Additive exPlanation analysis and canonical correlation analysis on automatically segmented RBC phase images.

Results: The implemented model achieved 97.8% accuracy in detecting tRBCs. Phase-shift statistics showed the highest influence on the correct classification of tRBCs. Associations between the phase-shift features and three-dimensional morphological features were revealed.

Conclusions: The implemented Mask R-CNN model accurately identified tRBCs and segmented RBCs to provide single-RBC characterization, which has the potential to aid clinical decision-making.

Significance: Currently, tissue biopsies are sectioned into 3- to 5-μm-thick slices that are used for conventional pathology analysis. Previous work by confocal microscopy and light-sheet microscopy has shown that analyzing biopsies intact in three-dimensions (3D) is possible and may lead to a better understanding of cancer growth patterns. Although accurate, these methods require fluorescent staining of the tissue, in addition to tissue clearing. If the 3D biopsy analysis could be done sufficiently swiftly, this approach may be used for on-site assessment of the adequacy of a biopsy taken.

Aim: We aim to show that, by transmission microscopy of optically cleared tissue punches, the tissue architecture can be determined without the need for fluorescent staining.

Approach: Transmission microscopy is used by combining bright field microscopy with dark field and epifluorescent microscopy to compare samples that have also been analyzed by fluorescent confocal microscopy.

Results: With increasing distance to the focal plane, the higher-frequency part of the spatial frequency spectrum of transmitted light is attenuated increasingly. This property is exploited for tissue segmentation, detecting whether tissue is present at a certain position in the focal plane image. Using this approach, we show that a 3D rendering of the internal cavity or tubules structure of punch biopsies, which are up to 1-mm thick, can be acquired in ≈1 min scan time per imaging modality. The images of the overall tissue architecture that are obtained are similar to those from the confocal microscopy benchmark, without requiring fluorescent staining.

Conclusions: Images of the overall tissue architecture can be obtained from transmission microcopy; they are similar to those from the confocal microscopy benchmark without requiring fluorescent staining. Tissue clearing is still needed. The total scan time of the present method is significantly shorter at a fraction of the device costs.

Significance: Decreasing the oxygen consumption rate (OCR) of tumor cells is a powerful method for ameliorating tumor hypoxia. However, quantifying the change in OCR is challenging in complex experimental systems.

Aim: We present a method for quantifying the OCR of two tumor cell lines using oxygen-sensitive dual-emissive boron nanoparticles (BNPs). We hypothesize that our BNP results are equivalent to the standard Seahorse assay.

Approach: We quantified the spectral emissions of the BNP and accounted for external oxygen diffusion to quantify OCR over 24 h. The BNP-computed OCR of two breast cancer cell lines, E0771 and 4T07, were compared with their respective Seahorse assays. Both cell lines were also irradiated to quantify radiation-induced changes in the OCR.

Results: Using a Bland–Altman analysis, our BNPs OCR was equivalent to the standard Seahorse assay. Moreover, in an additional experiment in which we irradiated the cells at their 50% survival fraction, the BNPs were sensitive enough to quantify 24% reduction in OCR after irradiation.

Conclusions: Our results conclude that the BNPs are a viable alternative to the Seahorse assay for quantifying the OCR in cells. The Bland–Altman analysis showed that these two methods result in equivalent OCR measurements. Future studies will extend the OCR measurements to complex systems including 3D cultures and in vivo models, in which OCR measurements cannot currently be made.