Significance

Current white light colonoscopy suffers from many limitations that allow 22% to 32% of preneoplastic lesions to remain undetected. This high number of false negatives contributes to the appearance of interval malignancies, defined as neoplasms diagnosed between screening colonoscopies at a rate of 2% to 6%.

Aim

The shortcomings of today’s white light-based colorectal cancer screening are addressed by colonoscopic fluorescence imaging of preneoplastic lesions using targeted fluorescent agents to enhance contrast between the lesion and the surrounding normal colonic epithelium.

Approach

We describe the development of Pluronic^® nanoparticles of fluorocoxib A (FA), a fluorescent cyclooxygenase-2 (COX-2) inhibitor that enables targeted imaging of inflammation and cancer in numerous animal models, for endoscopic florescence imaging of colonic adenomas.

Results

We formulated FA, a fluorescent COX-2 inhibitor, or fluorocoxib negative control (FNC), a nontargeted fluorophore and a negative control for FA, in micellar nanoparticles of FDA approved Pluronic tri-block co-polymer using a bulk solvent evaporation method. This afforded FA-loaded micellar nanoparticles (FA-NPs) or FNC-loaded micellar nanoparticles (FNC-NPs) with the hydrodynamic diameters (D_h) of 45.7 ± 2.5 nm and 44.9 ± 3.8 nm and the zeta potentials (ζ) of −1.47 ± 0.3 mV and −1.64 ± 0.5 mV, respectively. We intravenously injected B6;129 mice bearing colonic adenomas induced by azoxymethane and dextran-sodium sulfate with FA-loaded Pluronic nanoparticles (FA-NPs). The diffusion-mediated local FA release and its binding to COX-2 enzyme allowed for clear detection of adenomas with high signal-to-noise ratios. The COX-2 targeted delivery and tumor retention were validated by negligible tumor fluorescence detected upon colonoscopic imaging of adenoma-bearing mice injected with Pluronic nanoparticles of FNC or of animals predosed with the COX-2 inhibitor, celecoxib, followed by intravenous dosing of FA-NPs.

Conclusions

These results demonstrate that the formulation of FA in Pluronic nanoparticles overcomes a significant hurdle to its clinical development for early detection of colorectal neoplasms by fluorescence endoscopy.

Significance

Spectral imaging, which includes hyperspectral and multispectral imaging, can provide images in numerous wavelength bands within and beyond the visible light spectrum. Emerging technologies that enable compact, portable spectral imaging cameras can facilitate new applications in biomedical imaging.

Aim

With this review paper, researchers will (1) understand the technological trends of upcoming spectral cameras, (2) understand new specific applications that portable spectral imaging unlocked, and (3) evaluate proper spectral imaging systems for their specific applications.

Approach

We performed a comprehensive literature review in three databases (Scopus, PubMed, and Web of Science). We included only fully realized systems with definable dimensions. To best accommodate many different definitions of “compact,” we included a table of dimensions and weights for systems that met our definition.

Results

There is a wide variety of contributions from industry, academic, and hobbyist spaces. A variety of new engineering approaches, such as Fabry–Perot interferometers, spectrally resolved detector array (mosaic array), microelectro-mechanical systems, 3D printing, light-emitting diodes, and smartphones, were used in the construction of compact spectral imaging cameras. In bioimaging applications, these compact devices were used for in vivo and ex vivo diagnosis and surgical settings.

Conclusions

Compact and ultracompact spectral imagers are the future of spectral imaging systems. Researchers in the bioimaging fields are building systems that are low-cost, fast in acquisition time, and mobile enough to be handheld.

Significance

Rapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions.

Aim

Our aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning.

Approach

Multiphoton microscopy was utilized to acquire images of collagen and elastin fibers. Morphological features, histogram, and gray-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning model through the tree-based pipeline optimization tool (TPOT).

Results

The feature importance ranking was obtained, and 28 feature subsets were acquired by incremental feature selection. The subset with the top 23 features was identified as the most accurate. Then stochastic gradient descent classifier optimized by the TPOT was generated with an accuracy of 96.15% in classifying normal, scar, and adjacent tissues. The area under curve of the classification results (scar versus normal and adjacent, normal versus scar and adjacent, and adjacent versus normal and scar) was 1.0, 1.0, and 0.99, respectively.

Conclusions

The proposed approach has great potential for future dermatological clinical diagnosis and analysis and holds promise for the development of computer-aided systems to assist dermatologists in diagnosis and treatment.

Significance

Leukocytes are mainly composed of neutrophils, basophils, eosinophils, monocytes, and lymphocytes. The number and proportion of different types of leukocytes correspond to different diseases, so an accurate segmentation of each type of leukocyte is important for the diagnosis of disease. However, the acquisition of blood cell images can be affected by external environmental factors, which can lead to variable light and darkness, complex backgrounds, and poorly characterized leukocytes.

Aim

To address the problem of complex blood cell images collected under different environments and the lack of obvious leukocyte features, a leukocyte segmentation method based on improved U-net is proposed.

Approach

First, adaptive histogram equalization-retinex correction was introduced for data enhancement to make the leukocyte features in the blood cell images clearer. Then, to address the problem of similarity between different types of leukocytes, convolutional block attention module is added to the four skip connections of U-net to focus the features from spatial and channel aspects, so that the network can quickly locate the high-value information of features in different channels and spaces. It avoids the problem of large amount of repeated computation of low-value information, prevents overfitting, and improves the training efficiency and generalization ability of the network. Finally, to solve the problem of class imbalance in blood cell images and to better segment the cytoplasm of leukocytes, a loss function combining focal loss and Dice loss is proposed.

Results

We use the BCISC public dataset to verify the effectiveness of the proposed method. The segmentation of multiple leukocytes using the method of this paper can achieve 99.53% accuracy and 91.89% mIoU.

Conclusions

The experimental results show that the method achieves good segmentation results for lymphocytes, basophils, neutrophils, eosinophils, and monocytes.

Significance

For the development and routine characterization of optical devices used in medicine, tissue-equivalent phantoms mimicking a broad spectrum of human skin properties are indispensable.

Aim

Our work aims to develop a tissue-equivalent phantom suitable for photoplethysmography applications. The phantom includes the optical and mechanical properties of the three uppermost human skin layers (dermis, epidermis, and hypodermis, each containing different types of blood vessels) plus the ability to mimic pulsation.

Approach

While the mechanical properties of the polydimethylsiloxane base material are adjusted by different mixing ratios of a base and curing agent, the optical properties are tuned by adding titanium dioxide particles, India ink, and synthetic melanin in different concentrations. The layered structure of the phantom is realized using a doctor blade technique, and blood vessels are fabricated using molding wires of different diameters. The tissue-mimicking phantom is then integrated into an artificial circulatory system employing piezo-actuated double diaphragm pumps for testing.

Results

The optical and mechanical properties of human skin were successfully replicated. The diameter of the artificial blood vessels is linearly dependent on pump actuation, and the time-dependent expansion profile of real pulse forms were mimicked.

Conclusions

A tissue equivalent phantom suitable for the ex-vivo testing of opto-medical devices was demonstrated.

Significance

Pancreatic surgery is a highly demanding and routinely applied procedure for the treatment of several pancreatic lesions. The outcome of patients with malignant entities crucially depends on the margin resection status of the tumor. Frozen section analysis for intraoperative evaluation of tissue is still time consuming and laborious.

Aim

We describe the application of fiber-based attenuated total reflection infrared (ATR IR) spectroscopy for label-free discrimination of normal pancreatic, tumorous, and pancreatitis tissue. A pilot study for the intraoperative application was performed.

Approach

The method was applied for unprocessed freshly resected tissue samples of 58 patients, and a classification model for differentiating between the distinct tissue classes was established.

Results

The developed three-class classification model for tissue spectra allows for the delineation of tumors from normal and pancreatitis tissues using a probability score for class assignment. Subsequently, the method was translated into intraoperative application. Fiber optic ATR IR spectra were obtained from freshly resected pancreatic tissue directly in the operating room.

Conclusion

Our study shows the possibility of applying fiber-based ATR IR spectroscopy in combination with a supervised classification model for rapid pancreatic tissue identification with a high potential for transfer into intraoperative surgical diagnostics.

Significance

Intravascular near-infrared fluorescence (NIRF) imaging aims to improve the inspection of vascular pathology using fluorescent agents with specificity to vascular disease biomarkers. The method has been developed to operate in tandem with an anatomical modality, such as intravascular ultrasound (IVUS), and complements anatomical readings with pathophysiological contrast, enhancing the information obtained from the hybrid examination.

Aim

However, attenuation of NIRF signals by blood challenges NIRF quantification. We propose a new method for attenuation correction in NIRF intravascular imaging based on a fluorophore-coated guidewire that is used as a reference for the fluorescence measurement and provides a real-time measurement of blood attenuation during the NIRF examination.

Approach

We examine the performance of the method in a porcine coronary artery ex vivo and phantoms using a 3.2F NIRF-IVUS catheter.

Results

We demonstrate marked improvement over uncorrected signals of up to 4.5-fold and errors of <11 % for target signals acquired at distances up to 1 mm from the catheter system employed.

Conclusions

The method offers a potential means of improving the accuracy of intravascular NIRF imaging under in vivo conditions.

Significance

In spatial frequency domain imaging (SDFI), tissue is illuminated with sinusoidal intensity patterns at different spatial frequencies. For low spatial frequencies, the reflectance is diffuse and a model derived by Cuccia et al. (doi 10.1117/1.3088140) is commonly used to extract optical properties. An improved model resulting in more accurate optical property extraction could lead to improved diagnostic algorithms.

Aim

To develop a model that improves optical property extraction for the diffuse reflectance in SFDI compared to the model of Cuccia et al.

Approach

We derive two analytical models for the diffuse reflectance, starting from the theoretical radial reflectance R ( ρ ) for a pencil-beam illumination under the partial current boundary condition (PCBC) and the extended boundary condition (EBC). We compare both models and the model of Cuccia et al. to Monte Carlo simulations.

Results

The model based on the PCBC resulted in the lowest errors, improving median relative errors compared to the model of Cuccia et al. by 45% for the reflectance, 10% for the reduced scattering coefficient and 64% for the absorption coefficient.

Conclusions

For the diffuse reflectance in SFDI, the model based on the PCBC provides more accurate results than the currently used model by Cuccia et al.

Significance

Reflectance confocal microscopy (RCM) allows for real-time in vivo visualization of the skin at the cellular level. The study of RCM images provides information on the structural properties of the epidermis. These may change in each layer of the epidermis, depending on the subject’s age and the presence of certain dermatological conditions. Studying RCM images requires manual identification of cells to derive these properties, which is time consuming and subject to human error, highlighting the need for an automated cell identification method.

Aim

We aim to design an automated pipeline for the analysis of the structure of the epidermis from RCM images of the Stratum granulosum and Stratum spinosum.

Approach

We identified the region of interest containing the epidermal cells and the individual cells in the segmented tissue area using tubeness filters to highlight membranes. We used prior biological knowledge on cell size to process the resulting detected cells, removing cells that were too small and reapplying the used filters locally on detected regions that were too big to be considered a single cell. The proposed full image analysis pipeline (FIAP) was compared with machine learning-based approaches (cell cutter, different U-Net configurations, and loss functions).

Results

All methods were evaluated both on simulated data (four images) and on manually annotated RCM data (seven images). Accuracy was measured using recall and precision metrics. Both accuracy metrics were higher in the proposed FIAP for both real (precision = 0.720 ± 0.068, recall = 0.850 ± 0.11) and synthetic images (precision = 0.835 ± 0.067, recall = 0.925 ± 0.012). The tested machine learning methods failed to identify and segment keratinocytes on RCM images with a satisfactory accuracy.

Conclusions

We showed that automatic cell segmentation can be achieved using a pipeline based on membrane detection, with an accuracy that matches expert manual cell identification. To our knowledge, this is the first method based on membrane detection to study healthy skin using RCM images evaluated against manually identified cell positions.

Significance

Current photoacoustic (PA) imaging modalities typically require either serial detection with a single-element transducer or parallel detections with an ultrasonic array, indicating a dilemma between system cost and imaging throughput. PA topography through ergodic relay (PATER) was recently developed to address this bottleneck. However, PATER requires object-specific calibration due to varied boundary condition and must be recalibrated through pointwise scanning for each object before measurements, which is time-consuming and severely limits practical application.

Aim

We aim to develop a new single-shot PA imaging technique that only requires a one-time calibration for imaging different objects using a single-element transducer.

Approach

We develop an imaging method, PA imaging through a spatiotemporal encoder (PAISE), to address the above issue. The spatial information is effectively coded into unique temporal features by the spatiotemporal encoder, which allows for compressive image reconstruction. An ultrasonic waveguide is proposed as a critical element to guide the PA waves from the object into the prism, which effectively accounts for the varied boundary condition of different objects. We further add irregular-shaped edges on the prism to introduce randomized internal reflections and further facilitate the scrambling of acoustic waves.

Results

The proposed technique is validated through comprehensive numerical simulations and experiments, and it is demonstrated that PAISE can successfully overcome the changed boundary condition and can image different samples given a single calibration.

Conclusions

The proposed PAISE technique is capable of single-shot widefield PA imaging with a single-element transducer and does not require sample-specific calibration, which successfully overcomes the major limitation of previous PATER technology.

Significance

In handheld laser speckle contrast imaging (LSCI), motion artifacts (MA) are inevitable. Suppression of MA leads to a valid and objective assessment of tissue perfusion in a wide range of medical applications including dermatology and burns. Our study shines light on the sources of these artifacts, which have not yet been explored. We propose a model based on optical Doppler effect to predict speckle contrast drop as an indication of MA.

Aim

We aim to theoretically model MA when an LSCI system measuring on static scattering media is subject to translational displacements. We validate the model using both simulation and experiments. This is the crucial first step toward creating robustness against MA.

Approach

Our model calculates optical Doppler shifts in order to predict intensity correlation function and contrast of the time-integrated intensity as functions of applied speed based on illumination and detection wavevectors. To validate the theoretical predictions, computer simulation of the dynamic speckles has been carried out. Then experiments are performed by both high-speed and low-framerate imaging. The employed samples for the experiments are a highly scattering matte surface and a Delrin plate of finite scattering level in which volume scattering occurs.

Results

An agreement has been found between theoretical prediction, simulation, and experimental results of both intensity correlation functions and speckle contrast. Coefficients in the proposed model have been linked to the physical parameters according to the experimental setups.

Conclusions

The proposed model provides a quantitative description of the influence of the types of illumination and media in the creation of MA. The accurate prediction of MA caused by translation based on Doppler shifts makes our model suitable to study the influence of rotation. Also the model can be extended for the case of dynamic media, such as live tissue.

Significance

Tissue phantoms that mimic the optical and radiologic properties of human or animal tissue play an important role in the development, characterization, and evaluation of imaging systems. Phantoms that are easily produced and stable for longitudinal studies are highly desirable.

Aim

A new type of long-lasting phantom was developed with commercially available materials and was assessed for fabrication ease, stability, and optical property control. Magnetic resonance imaging (MRI) and x-ray computed tomography (CT) contrast properties were also evaluated.

Approach

A systematic investigation of relationships between concentrations of skin-like pigments and composite optical properties was conducted to realize optical property phantoms in the red and near-infrared (NIR) wavelength range that also offered contrast for CT and MRI.

Results

Phantom fabrication time was <1 h and did not involve any heating or cooling processes. Changes in optical properties were <2 % over a 12-month period. Phantom optical and spectral features were similar to human soft tissue over the red to NIR wavelength ranges. Pigments used in the study also had CT and MRI contrasts for multimodality imaging studies.

Conclusions

The phantoms described here mimic optical properties of soft tissue and are suitable for multimodality imaging studies involving CT or MRI without adding secondary contrast agents.

Significance

Double-helix point spread function (DH-PSF) microscopy has been developed for three-dimensional (3D) localization and imaging at super-resolution but usually in environments with no or weak scattering. To date, super-resolution imaging through turbid media has not been reported.

Aim

We aim to explore the potential of DH-PSF microscopy in the imaging and localization of targets in scattering environments for improved 3D localization accuracy and imaging quality.

Approach

The conventional DH-PSF method was modified to accommodate the scanning strategy combined with a deconvolution algorithm. The localization of a fluorescent microsphere is determined by the center of the corresponding double spot, and the image is reconstructed from the scanned data by deconvoluting the DH-PSF.

Results

The resolution, i.e., the localization accuracy, was calibrated to 13 nm in the transverse plane and 51 nm in the axial direction. Penetration thickness could reach an optical thickness (OT) of 5. Proof-of-concept imaging and the 3D localization of fluorescent microspheres through an eggshell membrane and an inner epidermal membrane of an onion are presented to demonstrate the super-resolution and optical sectioning capabilities.

Conclusions

Modified DH-PSF microscopy can image and localize targets buried in scattering media using super-resolution. Combining fluorescent dyes, nanoparticles, and quantum dots, among other fluorescent probes, the proposed method may provide a simple solution for visualizing deeper and clearer in/through scattering media, making in situ super-resolution microscopy possible for various demanding applications.

Significance

In photoacoustic tomography (PAT), numerous reconstruction algorithms have been utilized to recover initial pressure rise distribution from the acquired pressure waves. In practice, most of these reconstructions are carried out on a desktop/workstation and the mobile-based reconstructions are far-flung. In recent years, mobile phones are becoming so ubiquitous, and most of them encompass a higher computing ability. Hence, realizing PAT image reconstruction on a mobile platform is intrinsic, and it will enhance the adaptability of PAT systems with point-of-care applications.

Aim

To implement PAT image reconstruction in Android-based mobile platforms.

Approach

For implementing PAT image reconstruction in Android-based mobile platforms, we proposed an Android-based application using Python to perform beamforming process in Android phones.

Results

The performance of the developed application was analyzed on different mobile platforms using both simulated and experimental datasets. The results demonstrate that the developed algorithm can accomplish the image reconstruction of in vivo small animal brain dataset in 2.4 s. Furthermore, the developed application reconstructs PAT images with comparable speed and no loss of image quality compared to that on a laptop. Employing a two-fold downsampling procedure could serve as a viable solution for reducing the time needed for beamforming while preserving image quality with minimal degradation.

Conclusions

We proposed an Android-based application that achieves image reconstruction on cheap, small, and universally available phones instead of relatively bulky expensive desktop computers/laptops/workstations. A beamforming speed of 2.4 s is achieved without hampering the quality of the reconstructed image.

Significance

A multiplexed fiber laser sensing system for cell temperature is proposed. To the best of the authors’ knowledge, this is the first multilongitudinal mode (MLM) optical fiber laser sensor array designed for cell temperature sensing.

Aim

A two-channel cell temperature sensing system with high sensitivity and real-time sensing capability is achieved. The temperature change of human hepatoellular carcinomas (HepG2) cells under the influence of exogenous chemical aflatoxin B1 (AFB1) can be monitored in real time.

Approach

A fiber laser cavity consists of a pair of fiber Bragg gratings (FBGs) with matched central wavelengths and a piece of erbium-doped fiber (EDF). The static FBG is utilized for design of fiber laser cavity and laser modes selection. In comparison, the sensing FBG is used for cell temperature sensing. The sensing FBG has a length of 10 mm and a diameter of 200 μm. Beat frequency signals (BFS) are generated by MLM lasers after optical-to-electrical conversion at a photodetector. Frequency change of a BFS is closely related to the reflected wavelength change of the sensing FBG. Through frequency division multiplexing, two fiber laser cavities are designed in the sensing system for two-channel temperature sensing. Frequency shift of a BFS that represents temperature change of cells can be automatically recorded in seconds.

Results

A two-channel cell temperature sensing system is designed with high sensitivities of 101.62 and 119.82 kHz / ° C, respectively. The temperature change of HepG2 cells under the influence of exogenous chemical AFB1 is monitored in real time.

Conclusions

The proposed system has the advantages of simple structure, high sensitivity, and two-channel sensing capability. Our study provides a simple and effective method to design a fiber laser sensor system without complex demodulation techniques and expensive optical components.