Editor-in-Chief Brian Pogue writes about publishing credibility in the field of translational biomedical optics.

This a list of reviewers who served JBO in 2018.

Optical coherence tomography (OCT) is an emerging technology for in vivo airway and lung imaging. However, OCT lacks sensitivity to the metabolic changes caused by inflammation, which drives chronic respiratory diseases such as asthma and chronic obstructive pulmonary disorder. Redox imaging (RI) is a label-free technique that uses the autofluorescence of the metabolic coenzymes NAD(P)H and flavin adenine dinucleotide (FAD) to probe cellular metabolism and could provide complimentary information to OCT for airway and lung imaging. We demonstrate OCT and RI of respiratory ciliated epithelial function in ex vivo mouse tracheae. We applied RI to measure cellular metabolism via the redox ratio [intensity of NAD(P)H divided by FAD] and particle tracking velocimetry OCT to quantify cilia-driven fluid flow. To model mitochondrial dysfunction, a key aspect of the inflammatory process, cyanide was used to inhibit oxidative metabolism and reduce ciliary motility. Cyanide exposure over 20 min significantly increased the redox ratio and reversed cilia-driven fluid flow. We propose that RI provides complementary information to OCT to assess inflammation in the airway and lungs.

Phase wrapping is a crucial issue in Doppler optical coherence tomography (OCT) and restricts its automatic implementation for clinical applications that quantify total retinal blood flow. We propose an automated phase-unwrapping technique that takes advantage of the parabolic profile of blood flow velocity in vessels. Instead of inspecting the phase shift manually, the algorithm calculates the gradient magnitude of the phase shift on the cross-sectional image and automatically detects the presence of phase wrapping. The voxels affected by phase wrapping are corrected according to the determined flow direction adjacent to the vessel walls. We validated this technique in the rodent retina using a prototype visible-light OCT and in the human retina with a commercial infrared OCT system. We believe this signal processing method may well accelerate clinical applications of Doppler OCT in ophthalmology.

Several noninvasive imaging techniques have been developed to monitor the health of skin and enhance the diagnosis of skin diseases. Among them, skin elastography is a popular technique used to measure the elasticity of the skin. A change in the elasticity of the skin can influence its natural frequencies and mode shapes. We propose a technique to use the resonant frequencies and mode shapes of the skin to monitor its health. Our study demonstrates how the resonant frequencies and mode shapes of skin can be obtained using numerical and experimental analysis. In our study, natural frequencies and mode shapes are obtained via two methods: (1) finite element analysis: an eigensolution is performed on a finite element model of normal skin, including stratum corneum, epidermis, dermis, and subcutaneous layers and (2) digital image correlation (DIC): several in-vivo measurements have been performed using DIC. The experimental results show a correlation between the DIC and FE results suggesting a noninvasive method to obtain vibration properties of the skin. This method can be further examined to be eventually used as a method to differentiate healthy skin from diseased skin. Prevention, early diagnosis, and treatment are critical in helping to reduce the incidence, morbidity, and mortality associated with skin cancer; thus, making the current study significant and important in the field of skin biomechanics.

A software for fast rendering the visual appearance of a blood vessel located in human skin was developed based on a numerical solution of the radiative transfer equation. The user can specify geometrical properties, such as the depth and the diameter of the vessel, and physiological properties, such as the oxygen saturation of the vessel or the blood concentration in the skin. From these data, the spatially and spectrally resolved reflectance from the skin containing the blood vessel is calculated via Monte Carlo simulations, by which a two-dimensional image is generated. The short calculation time of about a second is achieved by precalculating and storing the spatially resolved reflectance for a variety of combinations of the optical and geometrical properties. This concept gives the user the opportunity to rapidly explore the influence of the physiological and geometrical properties of the investigated blood vessel on its visual appearance. The correctness of the lookup table was validated by comparison with independent Monte Carlo simulations. Rendering examples of different blood vessels in human skins are given. The current version of the software can be downloaded at https://www.ilm-ulm.de/software.

Laser speckle contrast imaging (LSCI) enables video rate imaging of blood flow. However, its relation to tissue blood perfusion is nonlinear and depends strongly on exposure time. By contrast, the perfusion estimate from the slower laser Doppler flowmetry (LDF) technique has a relationship to blood perfusion that is better understood. Multiexposure LSCI (MELSCI) enables a perfusion estimate closer to the actual perfusion than that using a single exposure time. We present and evaluate a method that utilizes contrasts from seven exposure times between 1 and 64 ms to calculate a perfusion estimate that resembles the perfusion estimate from LDF. The method is based on artificial neural networks (ANN) for fast and accurate processing of MELSCI contrasts to perfusion. The networks are trained using modeling of Doppler histograms and speckle contrasts from tissue models. The importance of accounting for noise is demonstrated. Results show that by using ANN, MELSCI data can be processed to LDF perfusion with high accuracy, with a correlation coefficient R  =  1.000 for noise-free data, R  =  0.993 when a moderate degree of noise is present, and R  =  0.995 for in vivo data from an occlusion-release experiment.

In the last decades, laparoscopic surgery has become the gold standard in patients with colorectal cancer. To overcome the drawback of reduced tactile feedback, real-time tissue classification could be of great benefit. In this ex vivo study, hyperspectral imaging (HSI) was used to distinguish tumor tissue from healthy surrounding tissue. A sample of fat, healthy colorectal wall, and tumor tissue was collected per patient and imaged using two hyperspectral cameras, covering the wavelength range from 400 to 1700 nm. The data were randomly divided into a training (75%) and test (25%) set. After feature reduction, a quadratic classifier and support vector machine were used to distinguish the three tissue types. Tissue samples of 32 patients were imaged using both hyperspectral cameras. The accuracy to distinguish the three tissue types using both hyperspectral cameras was 0.88 (STD  =  0.13) on the test dataset. When the accuracy was determined per patient, a mean accuracy of 0.93 (STD  =  0.12) was obtained on the test dataset. This study shows the potential of using HSI in colorectal cancer surgery for fast tissue classification, which could improve clinical outcome. Future research should be focused on imaging entire colon/rectum specimen and the translation of the technique to an intraoperative setting.

The objective of our study is to develop a multimodality approach by combining magnetic resonance imaging (MRI) and optical imaging methods to assess acute murine colitis at the macro- and microscopic level. In vivo MRI is used to measure the cross-sectional areas of colons at the macroscopic level. Dual-color confocal laser endomicroscopy (CLE) allows in vivo examination of the fluorescently labeled epithelial cells and microvessels in the mucosa with a spatial resolution of ∼1.4  μm during ongoing endoscopy. To further validate the structural changes of the colons in three-dimensions, ex vivo light-sheet fluorescence microscopy (LSFM) is applied for in-toto imaging of cleared colon sections. MRI, LSFM, and CLE findings are significantly correlated with histological scoring (p  <  0.01) and the inflammation-associated activity index (p  <  0.01). Our multimodality imaging technique permits visualization of mucosa in colitis at different scales, which can enhance our understanding of the pathogenesis of inflammatory bowel diseases.

We present a multicolor fluorescence microscope system, under a selective plane illumination microscopy (SPIM) configuration, using three continuous wave-lasers and a single-channel-detection camera. The laser intensities are modulated with three time-delayed pulse trains that operate synchronously at one third of the camera frame rate, allowing a sequential excitation and an image acquisition of up to three different biomarkers. The feasibility of this imaging acquisition mode is demonstrated by acquiring single-plane multicolor images of living hyphae of Neurospora crassa. This allows visualizing simultaneously the localization and dynamics of different cellular components involved in apical growth in living hyphae. The configuration presented represents a noncommercial, cost-effective alternative microscopy system for the rapid and simultaneous acquisition of multifluorescent images and can be potentially useful for three-dimensional imaging of large biological samples.

We propose an intrinsic cancer marker in fixed tissue biopsy slides, which is based on the local spatial autocorrelation length obtained from quantitative phase images. The spatial autocorrelation length in a small region of the tissue phase image is sensitive to the nanoscale cellular morphological alterations and can hence inform on carcinogenesis. Therefore, this metric can potentially be used as an intrinsic cancer marker in histopathology. Typically, these correlation length maps are calculated by computing two-dimensional Fourier transforms over image subregions—requiring long computational times. We propose a more time-efficient method of computing the correlation map and demonstrate its value for diagnosis of benign and malignant breast tissues. Our methodology is based on highly sensitive quantitative phase imaging data obtained by spatial light interference microscopy.

Sugar-rich diets and poor dental hygiene promote the formation of a biofilm (plaque) that strongly adheres to the dental enamel surface and fosters the evolution of aciduric bacteria. The acid contributes to demineralization of the exterior tooth enamel, which accelerates after the pH drops below a critical value (∼5.5) for extended time periods resulting in the need for restorative procedures. Preventative techniques to alert the dentist and caries-susceptible patients regarding vulnerability to dental decay require a clinical measure of plaque activity. Therefore, there is a need to evaluate the acid production capability of plaque deposits in the pits and fissures of occlusal and interproximal regions. A ratiometric fluorescence pH-sensing device has been developed using an FDA-approved dye and LED excitation. Fluorescein spectral profiles were collected using a spectrometer and analyzed with a spectral unmixing algorithm for calibration over the pH range of 4.5 to 7. An in vivo pilot study on human subjects was performed using a sucrose rinse to accelerate bacterial metabolism and to measure the time-dependent drop in pH. The optical system is relatively immune to confounding factors such as photobleaching, dye concentration, and variation in excitation intensity associated with earlier dye-based pH measurement techniques.

Safe and accurate placement of screws remains a critical issue in open and minimally invasive spine surgery. We propose to use diffuse reflectance (DR) spectroscopy as a sensing technology at the tip of a surgical instrument to ensure a safe path of the instrument through the cancellous bone of the vertebrae. This approach could potentially reduce the rate of cortical bone breaches, thereby resulting in fewer neural and vascular injuries during spinal fusion surgery. In our study, DR spectra in the wavelength ranges of 400 to 1600 nm were acquired from cancellous and cortical bone from three human cadavers. First, it was investigated whether these spectra can be used to distinguish between the two bone types based on fat, water, and blood content along with photon scattering. Subsequently, the penetration of the bone by an optical probe was simulated using the Monte-Carlo (MC) method, to study if the changes in fat content along the probe path would still enable distinction between the bone types. Finally, the simulation findings were validated via an experimental insertion of an optical screw probe into the vertebra aided by x-ray image guidance. The DR spectra indicate that the amount of fat, blood, and photon scattering is significantly higher in cancellous bone than in cortical bone (p  <  0.01), which allows distinction between the bone types. The MC simulations showed a change in fat content more than 1 mm before the optical probe came in contact with the cortical bone. The experimental insertion of the optical screw probe gave similar results. This study shows that spectral tissue sensing, based on DR spectroscopy at the instrument tip, is a promising technology to identify the transition zone from cancellous to cortical vertebral bone. The technology therefore has the potential to improve the safety and accuracy of spinal screw placement procedures.