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: Quantitative measurement of blood oxygen saturation (sO₂) with optoacoustic (OA) imaging is one of the most sought after goals of quantitative OA imaging research due to its wide range of biomedical applications.

Aim: A method for accurate and applicable real-time quantification of local sO₂ with OA imaging.

Approach: We combine multiple illumination (MI) sensing with learned spectral decoloring (LSD). We train LSD feedforward neural networks and random forests on Monte Carlo simulations of spectrally colored absorbed energy spectra, to apply the trained models to real OA measurements. We validate our combined MI-LSD method on a highly reliable, reproducible, and easily scalable phantom model, based on copper and nickel sulfate solutions.

Results: With this sulfate model, we see a consistently high estimation accuracy using MI-LSD, with median absolute estimation errors of 2.5 to 4.5 percentage points. We further find fewer outliers in MI-LSD estimates compared with LSD. Random forest regressors outperform previously reported neural network approaches.

Conclusions: Random forest-based MI-LSD is a promising method for accurate quantitative OA oximetry imaging.

Significance: Diffuse optical spectroscopic imaging (DOSI) is a versatile technology sensitive to changes in tissue composition and hemodynamics and has been used for a wide variety of clinical applications. Specific applications have prompted the development of versions of the DOSI technology to fit specific clinical needs. This work describes the development and characterization of a multi-modal DOSI (MM-DOSI) system that can acquire metabolic, compositional, and pulsatile information at multiple penetration depths in a single hardware platform. Additionally, a 3D tracking system is integrated with MM-DOSI, which enables registration of the acquired data to the physical imaging area.

Aim: We demonstrate imaging, layered compositional analysis, and metabolism tracking capabilities using a single MM-DOSI system on optical phantoms as well as in vivo human tissue.

Approach: We characterize system performance with a silicone phantom containing an embedded object. To demonstrate multi-layer sensitivity, we imaged human calf tissue with a 4.8-mm skin-adipose thickness. Human thenar tissue was also measured using a combined broadband DOSI and continuous-wave near-infrared spectroscopy method (∼15  Hz acquisition rate).

Results: High-resolution optical property maps of absorption (μ_a) and reduced scattering (μs^′) were recovered on the phantom by capturing over 1000 measurement points in under 5 minutes. On human calf tissue, we show two probing depth layers have significantly different (p  <  0.001) total-hemo/myoglobin and μs^′ composition. On thenar tissue, we calculate tissue arterial oxygen saturation, venous oxygen saturation, and tissue metabolic rate of oxygen consumption during baseline and after release of an arterial occlusion.

Conclusions: The MM-DOSI can switch between collection of broadband spectra, high-resolution images, or multi-depth hemodynamics without any hardware reconfiguration. We conclude that MM-DOSI enables acquisition of high resolution, multi-modal data consolidated in a single platform, which can provide a more comprehensive understanding of tissue hemodynamics and composition for a wide range of clinical applications.

Significance: Spatial frequency domain imaging (SFDI) is a wide-field imaging technique that provides quantitative maps of tissue optical properties. We describe a compact SFDI imager that employs a multispectral compound-eye camera. This design enables simultaneous image acquisition at multiple wavelengths. Such a device has potential for application for quantitative evaluation of superficial tissues by nonspecialists in low-resource settings.

Aim: The aim of this work was to develop a compact SFDI imager for widefield imaging of in-vivo tissue optical properties and verify its ability to measure optical properties of tissue-simulating phantoms and in a preclinical model of burn wounds.

Approach: This compound-eye imager was constructed using a CMOS sensor subdivided into multiple regions, each having a bandpass filter and objective lens. The ability of the instrument to image optical properties was compared with (1) a commercial SFDI imager and (2) a laboratory-based system. Initial validation of ability to accurately characterize optical properties was performed using a tissue-simulating optical phantom. It was then applied to an established murine model of thermal contact burn severity. In-vivo measurements of the optical properties of rat skin were performed before and after the application of burns. Histology was used to verify burn severity.

Results: Measurements of the tissue-simulating phantom optical properties made using the compound-eye imager agree with measurements made using the two comparison SFDI devices. For the murine burn model, the burns showed a decrease in the reduced scattering coefficient at all measurement wavelengths compared with preburn measurements at the same locations. This is consistent with previously reported changes in scattering that occur in full-thickness burns.

Conclusion: We demonstrate the potential for SFDI to be translated into compact form factor using a compound-eye camera that is capable of obtaining multiple wavelengths channels simultaneously.

Significance: Speckle variation induced by intracellular motion (IM) in the urothelium was observed in optical coherence tomography (OCT) images. IM can be used as a dynamic contrast to segment the urothelium by comparing two sequential OCT images. This method opens the possibility of specifically tracking the distribution of urothelial cancerous cells for identifying the microinvasion of bladder tumors.

Approach: OCT images were acquired ex vivo with fresh porcine bladder tissue. IM was analyzed by tracking speckle variation using autocorrelation function, then quantified with constrained regularization method for inverting data (CONTIN method) to identify the decorrelation time (DT) of the speckle variations. Variance analysis was also conducted to show IM amplitude and distribution in the urothelium. The segmentation of the urothelium was demonstrated with OCT images with a visible urothelial layer and OCT images with an invisible urothelial layer.

Results: Significant speckle variation induced by IM was observed in the urothelium. However, the distribution of the IM is heterogeneous. The DTs are mostly concentrated between 1 and 30 ms. With the IM as a dynamic contrast, the urothelium can be accurately and exclusively segmented, even the urothelial layer is invisible in normal OCT images.

Conclusions: IM can be used as a dynamic contrast to exclusively track urothelial cell distribution. This contrast may provide a new mechanism for OCT to image the invasion depth and pattern of urothelial cancerous cells for accurately substaging of bladder cancer.

Significance: Screening and early detection of oral potentially malignant lesions (OPMLs) are of great significance in reducing the mortality rates associated with head and neck malignancies. Intra-oral multispectral optical imaging of tissues in conjunction with cloud-based machine learning (CBML) can be used to detect oral precancers at the point-of-care (POC) and guide the clinician to the most malignant site for biopsy.

Aim: Develop a bimodal multispectral imaging system (BMIS) combining tissue autofluorescence and diffuse reflectance (DR) for mapping changes in oxygenated hemoglobin (HbO₂) absorption in the oral mucosa, quantifying tissue abnormalities, and guiding biopsies.

Approach: The hand-held widefield BMIS consisting of LEDs emitting at 405, 545, 575, and 610 nm, 5MPx monochrome camera, and proprietary Windows-based software was developed for image capture, processing, and analytics. The DR image ratio (R610/R545) was compared with pathologic classification to develop a CBML algorithm for real-time assessment of tissue status at the POC.

Results: Sensitivity of 97.5% and specificity of 92.5% were achieved for discrimination of OPML from patient normal in 40 sites, whereas 82% sensitivity and 96.6% specificity were obtained for discrimination of abnormal (OPML + SCC) in 89 sites. Site-specific algorithms derived for buccal mucosa (27 sites) showed improved sensitivity and specificity of 96.3% for discrimination of OPML from normal.

Conclusions: Assessment of oral cancer risk is possible by mapping of HbO₂ absorption in tissues, and the BMIS system developed appears to be suitable for biopsy guidance and early detection of oral cancers.

Significance: The proposed binary tomography approach was able to recover the vasculature structures accurately, which could potentially enable the utilization of binary tomography algorithm in scenarios such as therapy monitoring and hemorrhage detection in different organs.

Aim: Photoacoustic tomography (PAT) involves reconstruction of vascular networks having direct implications in cancer research, cardiovascular studies, and neuroimaging. Various methods have been proposed for recovering vascular networks in photoacoustic imaging; however, most methods are two-step (image reconstruction and image segmentation) in nature. We propose a binary PAT approach wherein direct reconstruction of vascular network from the acquired photoacoustic sinogram data is plausible.

Approach: Binary tomography approach relies on solving a dual-optimization problem to reconstruct images with every pixel resulting in a binary outcome (i.e., either background or the absorber). Further, the binary tomography approach was compared against backprojection, Tikhonov regularization, and sparse recovery-based schemes.

Results: Numerical simulations, physical phantom experiment, and in-vivo rat brain vasculature data were used to compare the performance of different algorithms. The results indicate that the binary tomography approach improved the vasculature recovery by 10% using in-silico data with respect to the Dice similarity coefficient against the other reconstruction methods.

Conclusion: The proposed algorithm demonstrates superior vasculature recovery with limited data both visually and based on quantitative image metrics.

Significance: The human vocal fold (VF) oscillates in multiple vectors and consists of distinct layers with varying viscoelastic properties that contribute to the mucosal wave. Office-based and operative laryngeal endoscopy are limited to diagnostic evaluation of the VF epithelial surface only and are restricted to axial-plane characterization of the horizontal mucosal wave. As such, understanding of the biomechanics of human VF motion remains limited.

Aim: Optical coherence tomography (OCT) is a micrometer-resolution, high-speed endoscopic imaging modality which acquires cross-sectional images of tissue. Our study aimed to leverage OCT technology and develop quantitative methods for analyzing the anatomy and kinematics of in vivo VF motion in the coronal plane.

Approach: A custom handheld laryngeal stage was used to capture OCT images with 800 A-lines at 250 Hz. Automated image postprocessing and analytical methods were developed.

Results: Novel kinematic analysis of in vivo, long-range OCT imaging of the vibrating VF in awake human subjects is reported. Cross-sectional, coronal-plane panoramic videos of the larynx during phonation are presented with three-dimensional videokymographic and space-time velocity analysis of VF motion.

Conclusions: Long-range OCT with automated computational methods allows for cross-sectional dynamic laryngeal imaging and has the potential to broaden our understanding of human VF biomechanics and sound production.

Significance: Quantitative stiffness information can be a powerful aid for tumor or fibrosis diagnosis. Currently, very promising elastography approaches developed for non-contact biomedical imaging are based on transient shear-waves imaging. Transient elastography offers quantitative stiffness information by tracking the propagation of a wave front. The most common method used to compute stiffness from the acquired propagation movie is based on shear-wave time-of-flight calculations.

Aim: We introduce an approach to transient shear-wave elastography with spatially coherent sources, able to yield full-field quantitative stiffness maps with reduced artifacts compared to typical artifacts observed in time-of-flight.

Approach: A noise-correlation algorithm developed for passive elastography is adapted to spatially coherent narrow or any band sources. This noise-correlation-inspired (NCi) method is employed in parallel with a classic time-of-flight approach. Testing is done on simulation images, experimental validation is conducted with a digital holography setup on controlled homogeneous samples, and full-field quantitative stiffness maps are presented for heterogeneous samples and ex-vivo biological tissues.

Results: The NCi approach is first validated on simulations images. Stiffness images processed by the NCi approach on simulated inclusions display significantly less artifacts than with a time-of-flight reconstruction. The adaptability of the NCi algorithm to narrow or any band shear-wave sources was tested successfully. Experimental testing on homogeneous samples demonstrates similar values for both the time-of-flight and the NCi approach. Soft inclusions in agarose sample could be resolved using the NCi method and feasibility on ex-vivo biological tissues is presented.

Conclusions: The presented NCi approach was successful in computing quantitative full-field stiffness maps with narrow and broadband source signals on simulation and experimental images from a digital holography setup. Results in heterogeneous media show that the NCi approach could provide stiffness maps with less artifacts than with time-of-flight, demonstrating that a NCi algorithm is a promising approach for shear-wave transient elastography with spatially coherent sources.

Significance: Oral cancer is a quite common global health issue. Early diagnosis of cancerous and potentially malignant disorders in the oral cavity would significantly increase the survival rate of oral cancer. Previously reported smartphone-based images detection methods for oral cancer mainly focus on demonstrating the effectiveness of their methodology, yet it still lacks systematic study on how to improve the diagnosis accuracy on oral disease using hand-held smartphone photographic images.

Aim: We present an effective smartphone-based imaging diagnosis method, powered by a deep learning algorithm, to address the challenges of automatic detection of oral diseases.

Approach: We conducted a retrospective study. First, a simple yet effective centered rule image-capturing approach was proposed for collecting oral cavity images. Then, based on this method, a medium-sized oral dataset with five categories of diseases was created, and a resampling method was presented to alleviate the effect of image variability from hand-held smartphone cameras. Finally, a recent deep learning network (HRNet) was introduced to evaluate the performance of our method for oral cancer detection.

Results: The performance of the proposed method achieved a sensitivity of 83.0%, specificity of 96.6%, precision of 84.3%, and F1 of 83.6% on 455 test images. The proposed “center positioning” method was about 8% higher than that of a simulated “random positioning” method in terms of F1 score, the resampling method had additional 6% of performance improvement, and the introduced HRNet achieved slightly better performance than VGG16, ResNet50, and DenseNet169, with respect to the metrics of sensitivity, specificity, precision, and F1.

Conclusions: Capturing oral images centered on the lesion, resampling the cases in training set, and using the HRNet can effectively improve the performance of deep learning algorithm on oral cancer detection. The smartphone-based imaging with deep learning method has good potential for primary oral cancer diagnosis.

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.