This guest editorial introduces the Special Section on Metabolic Imaging and Spectroscopy: Britton Chance 105th Birthday Commemorative.

We created a two-channel autofluorescence test to detect oral cancer. The wavelengths 375 and 460 nm, with filters of 479 and 525 nm, were designed to excite and detect reduced-form nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) autofluorescence. Patients with oral cancer or with precancerous lesions, and a control group with healthy oral mucosae, were enrolled. The lesion in the autofluorescent image was the region of interest. The average intensity and heterogeneity of the NADH and FAD were calculated. The redox ratio [(NADH)/(NADH + FAD)] was also computed. A quadratic discriminant analysis (QDA) was used to compute boundaries based on sensitivity and specificity. We analyzed 49 oral cancer lesions, 34 precancerous lesions, and 77 healthy oral mucosae. A boundary (sensitivity: 0.974 and specificity: 0.898) between the oral cancer lesions and healthy oral mucosae was validated. Oral cancer and precancerous lesions were also differentiated from healthy oral mucosae (sensitivity: 0.919 and specificity: 0.755). The two-channel autofluorescence detection device and analyses of the intensity and heterogeneity of NADH, and of FAD, and the redox ratio combined with a QDA classifier can differentiate oral cancer and precancerous lesions from healthy oral mucosae.

Despite causing permanent hearing loss by damaging inner ear sensory cells, aminoglycosides (AGs) remain one of the most widely used classes of antibiotics in the world. Although the mechanisms of cochlear sensory cell damage are not fully known, reactive oxygen species (ROS) are clearly implicated. Mitochondrial-specific ROS formation was evaluated in acutely cultured murine cochlear explants exposed to gentamicin (GM), a representative ototoxic AG antibiotic. Superoxide (O₂^·−) and hydrogen peroxide (H₂O₂) were measured using MitoSOX Red and Dihydrorhodamine 123, respectively, in sensory and supporting cells. A 1-h GM exposure significantly increased O₂^·− formation in IHCs and increased H₂O₂ formation in all cell types. At the same time point, GM significantly increased manganese superoxide dismutase (MnSOD) levels while significantly decreasing copper/zinc superoxide dismutase (CuZnSOD) in cochlear sensory cells. This suggests (1) a rapid conversion of highly reactive O₂^·− to H₂O₂ during the acute stage of ototoxic antibiotic exposure and (2) that the endogenous antioxidant system is significantly altered by AGs. Fluorescence intensity-based measurements of reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] and mitochondrial membrane potential were measured to determine if increases in GM-induced ROS production were correlated with changes in mitochondrial metabolism. This project provides a basis for understanding the mechanisms of mitochondrial ROS production in cochlear cells exposed to ototoxic antibiotics. Understanding the nature of ototoxic antibiotic-induced changes in mitochondrial metabolism is critical for developing hearing loss treatment and prevention strategies.

Britton Chance, electronics expert when a teenager, became an enthusiastic student of biological oscillations, passing on this enthusiasm to many students and colleagues, including one of us (DL). This historical essay traces BC’s influence through the accumulated work of DL to DL’s many collaborators. The overall temporal organization of mass-energy, information, and signaling networks in yeast in self-synchronized continuous cultures represents, until now, the most characterized example of in vivo elucidation of time structure. Continuous online monitoring of dissolved gases by direct measurement (membrane-inlet mass spectrometry, together with NAD(P)H and flavin fluorescence) gives strain-specific dynamic information from timescales of minutes to hours as does two-photon imaging. The predominantly oscillatory behavior of network components becomes evident, with spontaneously synchronized cellular respiration cycles between discrete periods of increased oxygen consumption (oxidative phase) and decreased oxygen consumption (reductive phase). This temperature-compensated ultradian clock provides coordination, linking temporally partitioned functions by direct feedback loops between the energetic and redox state of the cell and its growing ultrastructure. Multioscillatory outputs in dissolved gases with 13 h, 40 min, and 4 min periods gave statistical self-similarity in power spectral and relative dispersional analyses: i.e., complex nonlinear (chaotic) behavior and a functional scale-free (fractal) network operating simultaneously over several timescales.

Cherenkov emission induced by external beam radiation therapy from a clinical linear accelerator (LINAC) can be used to excite phosphors deep in biological tissues. As with all luminescence imaging, there is a desire to minimize the spectral overlap between the excitation light and emission wavelengths, here between the Cherenkov and the phosphor. Cherenkov excited short-wavelength infrared (SWIR, 1000 to 1700 nm) fluorescence imaging has been demonstrated for the first time, using long Stokes-shift fluorophore PdSe quantum dots (QD) with nanosecond lifetime and an optimized SWIR detection. The 1  /  λ² intensity spectrum characteristic of Cherenkov emission leads to low overlap of this into the fluorescence spectrum of PdSe QDs in the SWIR range. Additionally, using a SWIR camera itself inherently ignores the stronger Cherenkov emission wavelengths dominant across the visible spectrum. The SWIR luminescence was shown to extend the depth sensitivity of Cherenkov imaging, which could be used for applications in radiotherapy sensing and imaging in human tissue with targeted molecular probes.

In the so-called surface microscopy, serial block-face imaging is combined with mechanic sectioning to obtain volumetric imaging. While mapping a resin-embedded green fluorescent protein (GFP)-labeled specimen, it has been recently reported that an alkaline buffer is used to chemically reactivate the protonated GFP molecules, and thus improve the signal-to-noise ratio. In such a procedure, the image quality is highly affected by the penetration rate of a solution. We propose a reliable penetration model to describe the penetration process of the solution into the resin. The experimental results are consistent with the parameters predicted using this model. Thus, this model provides a valuable theoretical explanation and aids in optimizing the system parameters for mapping resin-embedded GFP biological samples.

Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.

In the context of epilepsy monitoring, electroencephalography (EEG) remains the modality of choice. Functional near-infrared spectroscopy (fNIRS) is a relatively innovative modality that cannot only characterize hemodynamic profiles of seizures but also allow for long-term recordings. We employ deep learning methods to investigate the benefits of integrating fNIRS measures for seizure detection. We designed a deep recurrent neural network with long short-term memory units and subsequently validated it using the CHBMIT scalp EEG database—a compendium of 896 h of surface EEG seizure recordings. After validating our network using EEG, fNIRS, and multimodal data comprising a corpus of 89 seizures from 40 refractory epileptic patients was used as model input to evaluate the integration of fNIRS measures. Following heuristic hyperparameter optimization, multimodal EEG-fNIRS data provide superior performance metrics (sensitivity and specificity of 89.7% and 95.5%, respectively) in a seizure detection task, with low generalization errors and loss. False detection rates are generally low, with 11.8% and 5.6% for EEG and multimodal data, respectively. Employing multimodal neuroimaging, particularly EEG-fNIRS, in epileptic patients, can enhance seizure detection performance. Furthermore, the neural network model proposed and characterized herein offers a promising framework for future multimodal investigations in seizure detection and prediction.

Diffuse optical tomography has demonstrated significant potential for clinical utility in the diagnosis and prognosis of breast cancer, and its use in combination with other structural imaging modalities improves lesion localization and the quantification of functional tissue properties. Here, we introduce a hybrid diffuse optical imaging system that operates concurrently with magnetic resonance imaging (MRI) in the imaging suite, utilizing commercially available MR surface coils. The instrument acquires both continuous-wave and time-domain diffuse optical data in the parallel-plate geometry, permitting both absolute assignment of tissue optical properties and three-dimensional tomography; moreover, the instrument is designed to incorporate diffuse correlation spectroscopic measurements for probing tissue blood flow. The instrument is described in detail here. Image reconstructions of a tissue phantom are presented as an initial indicator of the system’s ability to accurately reconstruct optical properties and the concrete benefits of the spatial constraints provided by concurrent MRI. Last, we briefly discuss how various data combinations that the instrument could facilitate, including tissue perfusion, can enable more comprehensive assessment of lesion physiology.

Working memory (WM) plays a crucial role in human brain functions. The application of brain connectivity analysis helps to understand the brain network properties in WM. Combination of functional and effective connectivity can provide new insights for exploring network attributes. Nevertheless, few studies have combined these two modes in WM. Near-infrared spectroscopy was used to investigate the connectivity properties in the prefrontal cortex (PFC) during n-back (0-back and 2-back) tasks by combining functional and effective connectivity analysis. Our results demonstrated that the PFC network showed small-world properties in both WM tasks. The characteristic path length was significantly longer in the 2-back task than in the 0-back task, while there was no obvious difference in the clustering coefficient between two tasks. Regarding the effective connectivity, the Granger causality (GC) was higher for right PFC→left PFC than for left PFC→right PFC in the 2-back task. Compared with the 0-back task, GC of right PFC→left PFC was higher in the 2-back task. Our findings show that, along with memory load increase, long range connections in PFC are enhanced and this enhancement might be associated with the stronger information flow from right PFC to left PFC.

Timing of the intervention for intracranial hematomas is critical for its success, specifically since expansion of the hemorrhage can result in debilitating and sometimes fatal outcomes. Led by Britton Chance, we and an extended team from University of Pennsylvania, Baylor and Drexel universities developed a handheld brain hematoma detector for early triage and diagnosis of head trauma victims. After obtaining de novo Food and Drug Administration clearance, over 200 systems are deployed in all Marine battalion aid stations around the world. Infrascanner, a handheld brain hematoma detection system, is based on the differential near-infrared light absorption of the injured versus the noninjured part of brain. About 12 independent studies have been conducted in the USA, Canada, Spain, Italy, the Netherlands, Germany, Russia, Poland, Afghanistan, India, China, and Turkey. Here, we outline the background and design of the device as well as clinical studies with a total of 1293 patients and 203 hematomas. Infrascanner demonstrates high sensitivity (adults: 92.5% and children: 93%) and specificity (adults: 82.9% and children: 86.5%) in detecting intracranial hematomas >3.5  mL in volume and <2.5  cm from the surface of the brain. Infrascanner is a clinically effective screening solution for head trauma patients in prehospital settings where timely triage is critical.

Biological systems with intrinsic luminescent properties serve as powerful and noninvasive bioreporters for real-time and label-free monitoring of cell physiology. This study employs the bioluminescent marine bacterium Vibrio fischeri to investigate the effects of separated microwave electric (E) and magnetic (H) fields. Using a cylindrical TM₀₁₀ mode aluminum resonant cavity, designed to spatially separate E and H fields of a pulsed microwave (2.45 GHz) input, we sampled at 100-ms intervals the 490-nm emission of bioluminescence from suspensions of the V. fischeri. E-field exposure (at 4.24 and 13.4 kV/m) results in rapid and sensitive responses to 100-ms pulses. H-field excitation elicits no measurable responses, even at 100-fold higher power input levels (equivalent to 183 A/m). The observed effects on bacterial light output partially correlate with measured E-field-induced temperature increases. In conclusion, the endogenous bioluminescence of V. fischeri provides a sensitive and noninvasive method to assess the biological effects of microwave fields.

Cytotoxic T lymphocytes (CTLs) play a key role in adoptive cell therapy (ACT) by destroying tumor cells. Although some mechanisms of CTLs killing tumor cells have already been revealed, the precise dynamic information of CTLs’ interaction with tumor cells is still not known. Here, we used confocal microscopy to visualize the whole process of how CTLs kill tumor cells in vitro. According to imaging data, CTLs destroyed the target tumor cells rapidly and efficiently. Several CTLs surrounded one or more tumor cells, and the average time for CTLs destroying one or more tumor cells in vitro is dozens of minutes only. Our study displayed the temporal events of CTLs’ interaction with tumor cells at the beginning up to the point of killing them. Furthermore, the imaging data presented strong cytotoxicity of CTLs toward the specific tumor cells. These results could help us to well understand the mechanism of CTLs’ elimination of tumor cells and improve the efficacy of ACT in cancer immunotherapy.

More people die from melanoma after a stage I diagnosis than after a stage IV diagnosis, because the tools available to clinicians do not readily identify which early-stage cancers will be aggressive. Near-infrared pump-probe microscopy detects fundamental differences in melanin structure between benign human moles and melanoma and also correlates with metastatic potential. However, the biological mechanisms of these changes have been difficult to quantify, as many different mechanisms can contribute to the pump-probe signal. We use model systems (sepia, squid, and synthetic eumelanin), cellular uptake studies, and a range of pump and probe wavelengths to demonstrate that the clinically observed effects come from alterations of the aggregated mode from “thick oligomer stacks” to “thin oligomer stacks” (due to changes in monomer composition) and (predominantly) deaggregation of the assembled melanin structure. This provides the opportunity to use pump-probe microscopy for the detection and study of melanin-associated diseases.

The focus of this work is toward the development of a point-of-care (POC) handheld technology for the noninvasive early detection of salivary biomarkers. The initial of focus was the detection and quantification of S100 calcium-binding protein P (S100P) mRNA found in whole saliva for use as a potential biomarker for oral cancer. Specifically, a surface-enhanced Raman spectroscopy (SERS)-based approach and assay were designed, developed, and tested for sensitive and rapid detection of S100P mRNA. Gold nanoparticles (AuNPs) were conjugated with oligonucleotides and malachite green isothiocyanate was then used as a Raman reporter molecule. The hybridization of S100P target to DNA-conjugated AuNPs in sandwich assay format in both free solution and a vertical flow chip (VFC) was confirmed using a handheld SERS system. The detection limit of the SERS-based assay in free solution was determined to be 1.1 nM, whereas on the VFC the detection limit was observed to be 10 nM. SERS-based VFCs were also used to quantify the S100P mRNA from saliva samples of oral cancer patients and a healthy group. The result indicated that the amount of S100P mRNA detected for the oral cancer patients is three times higher than that of a healthy group.

An investigation of the influence of an evanescent wave on the dynamics of motion of erythrocytes in blood plasma is presented. Computer simulation of erythrocytes moving in an evanescent field and experimental demonstration of the forecasted motion substantiate the possibility for control of position of red blood cells in a solution. The range of velocities of transversal motion of erythrocytes due to the action of the optical force of the generated evanescent field is determined as a function of the angle of illumination of a cell by a linearly polarized wave with the azimuth of polarization 45 deg.

In Fourier-domain optical coherence tomography (FD-OCT), proper signal sampling and dispersion compensation are essential steps to achieve optimal axial resolution. These calibration steps can be performed through numerical signal processing, but require calibration information about the system that may require lengthy and complex measurement protocols. We report a highly robust calibration procedure that can simultaneously determine correction vectors for nonlinear wavenumber sampling and dispersion compensation. The proposed method requires only two simple mirror measurements and no prior knowledge about the system’s illumination source or detection scheme. This method applies to both spectral domain and swept-source OCT systems. Furthermore, it may be implemented as a low-cost fail-safe to validate the proper function of calibration hardware such as k-clocks. We demonstrate the method’s simple implementation, effectiveness, and robustness on both types of OCT systems.

High re-excision rates in breast-conserving surgery call for a new intraoperative approach to the lumpectomy margin evaluation problem. The unique intraoperative imaging system, presented here, demonstrated the capability of photoacoustic tomography (PAT) to deliver optical sensitivity and specificity, along with over 2-cm imaging depth, in a clinical setting. The system enabled the evaluation of tumor extent, shape, morphology, and position within lumpectomy specimens measuring up to 11 cm in diameter. The investigation included all major breast cancer-related lesions, such as invasive ductal carcinoma (IDC), multifocal IDC, ductal carcinoma in situ and combinations of these variants. Coregistration with established ultrasound (US) technology, as well as comparison to specimen radiography, validated the performance of PAT, which appeared to facilitate better tumor visualization. Contrary to expected PA contrast mechanisms, PAT images of hemoglobin distribution correlated poorly with US-determined tumor location, while hypointense regions in lipid-weighted PAT images were in better agreement with US.

In conventional retinal region detection methods for optical coherence tomography (OCT) images, many parameters need to be set manually, which is often detrimental to their generalizability. We present a scheme to detect retinal regions based on fully convolutional networks (FCN) for automatic diagnosis of abnormal maculae in OCT images. The FCN model is trained on 900 labeled age-related macular degeneration (AMD), diabetic macular edema (DME) and normal (NOR) OCT images. Its segmentation accuracy is validated and its effectiveness in recognizing abnormal maculae in OCT images is tested and compared with traditional methods, by using the spatial pyramid matching based on sparse coding (ScSPM) classifier and Inception V3 classifier on two datasets: Duke dataset and our clinic dataset. In our clinic dataset, we randomly selected half of the B-scans of each class (300 AMD, 300 DME, and 300 NOR) for training classifier and the rest (300 AMD, 300 DME, and 300 NOR) for testing with 10 repetitions. Average accuracy, sensitivity, and specificity of 98.69%, 98.03%, and 99.01% are obtained by using ScSPM classifier, and those of 99.69%, 99.53%, and 99.77% are obtained by using Inception V3 classifier. These two classification algorithms achieve 100% classification accuracy when directly applied to Duke dataset, where all the 45 OCT volumes are used as test set. Finally, FCN model with or without flattening and cropping and its influence on classification performance are discussed.

Clinical imaging techniques for the anterior segment of the eye provide excellent anatomical information, but molecular imaging techniques are lacking. Molecular photoacoustic imaging is one option to address this need, but implementation requires use of contrast agents to distinguish molecular targets from background photoacoustic signals. Contrast agents are typically selected based on a priori knowledge of photoacoustic properties of tissues. However, photoacoustic properties of anterior ocular tissues have not been studied yet. Herein, anterior segment anatomy and corresponding photoacoustic signals were analyzed in brown and blue porcine eyes ex vivo. Measured photoacoustic spectra were compared to known optical absorption spectra of endogenous chromophores. In general, experimentally measured photoacoustic spectra matched expectations based on absorption spectra of endogenous chromophores reported in the literature, and similar photoacoustic spectra were observed in blue and brown porcine eyes. However, unique light–tissue interactions at the iris modified photoacoustic signals from melanin. Finally, we demonstrated how the measured PA spectra established herein can be used for one application of molecular PA imaging, detecting photoacoustically labeled stem cells in the anterior segment for glaucoma treatment.

Ultrasound (US) guided diffuse optical tomography has demonstrated great potential for breast cancer diagnosis, treatment monitoring, and chemotherapy response prediction. Optical measurements of four different wavelengths are used to reconstruct unknown optical absorption maps, which are then used to calculate the hemoglobin concentration distribution of the US visible lesion. Reconstructed absorption maps are prone to image artifacts from outliers in measurement data from tissue heterogeneity, bad coupling between tissue and light guides, and motion by patient or operator. We propose an automated iterative perturbation correction algorithm to reduce image artifacts based on the structural similarity index (SSIM) of absorption maps of four optical wavelengths. The initial image is estimated from the truncated pseudoinverse solution. The SSIM was calculated for each wavelength to assess its similarity with other wavelengths. An absorption map is repeatedly reconstructed and projected back into measurement space to quantify projection error. Outlier measurements with highest projection errors are iteratively removed until all wavelength images are structurally similar with SSIM values greater than a threshold. Clinical data demonstrate statistically significant improvement in image artifact reduction.

Relatively few imaging and sensing technologies are employed to study human lactation physiology. In particular, human mammary development during pregnancy as well as mammary involution after lactation have been poorly described, despite their importance for breast cancer diagnosis and treatment during these phases. Our case study shows the potential of diffuse optical spectroscopic imaging (DOSI) to uniquely study the spatiotemporal changes in mammary tissue composition during the involution of the lactating breast toward its pre-pregnant state. At nine time intervals over a period of eight months after the cessation of breastfeeding, we reconstructed 2-D maps of mammary water content, lipid content, total hemoglobin (THb) concentration, oxygen saturation (StO₂), and tissue optical scattering. Mammary lipid content in the nonareolar region showed a significant relative increase of 59%, whereas water content and THb concentration showed a significant relative decrease of 50% and 48%, respectively. Significant changes were also found in StO₂ and tissue optical scattering. Our findings are consistent with the gradual replacement of fibroglandular tissue by adipose tissue and vascular regression during mammary involution. Moreover, our data provide unique insight into the dynamics of breast tissue composition and demonstrate the effectiveness of DOSI as a technique to study human lactation physiology.

Accurate assessment of burn severity is critical for wound care and the course of treatment. Delays in classification translate to delays in burn management, increasing the risk of scarring and infection. To this end, numerous imaging techniques have been used to examine tissue properties to infer burn severity. Spatial frequency-domain imaging (SFDI) has also been used to characterize burns based on the relationships between histologic observations and changes in tissue properties. Recently, machine learning has been used to classify burns by combining optical features from multispectral or hyperspectral imaging. Rather than employ models of light propagation to deduce tissue optical properties, we investigated the feasibility of using SFDI reflectance data at multiple spatial frequencies, with a support vector machine (SVM) classifier, to predict severity in a porcine model of graded burns. Calibrated reflectance images were collected using SFDI at eight wavelengths (471 to 851 nm) and five spatial frequencies (0 to 0.2  mm^−  1). Three models were built from subsets of this initial dataset. The first subset included data taken at all wavelengths with the planar (0  mm^−  1) spatial frequency, the second comprised data at all wavelengths and spatial frequencies, and the third used all collected data at values relative to unburned tissue. These data subsets were used to train and test cubic SVM models, and compared against burn status 28 days after injury. Model accuracy was established through leave-one-out cross-validation testing. The model based on images obtained at all wavelengths and spatial frequencies predicted burn severity at 24 h with 92.5% accuracy. The model composed of all values relative to unburned skin was 94.4% accurate. By comparison, the model that employed only planar illumination was 88.8% accurate. This investigation suggests that the combination of SFDI with machine learning has potential for accurately predicting burn severity.

The increase in the number of patients with mental disorders with depressive symptoms has become a significant problem. To prevent people developing those disorders and help with the effective recovery, it is important to quantitatively and objectively monitor an individual’s mental state. Previous studies have shown the relationship between negative or depressive mood state and human prefrontal cortex (PFC) activation during verbal and spatial working memory tasks based on a near-infrared spectroscopy imaging technique. In this study, we aimed to explore a biomarker of the mental state of people in remission of mental disorders with depressive symptoms using this technique. We obtained the PFC activation of return-to-work (RTW) trainees in remission of those disorders, compared that of healthy controls, and obtained subjective questionnaire scores with the Profile of Mood States. We compared the PFC activation with the questionnaire scores by receiver operating characteristic analysis using a logistic-regression model. The results showed that the PFC activation indicates a healthy state compared to that of the RTW trainees evaluated by area-under-curve analysis. This study demonstrates that our PFC measurement technique will be useful as a quantitative and objective assessment of mental state.

Correction of hyperopia requires an increase of the refractive power by steepening of the corneal surface. Present refractive surgical techniques based on corneal ablation (LASIK) or intrastromal lenticule extraction (SMILE) are problematic due to epithelial regrowth. Recently, it was shown that correction of low hyperopia can be achieved by implanting intracorneal inlays or allogeneic lenticules. We demonstrate a steepening of the anterior corneal surface after injection of a transparent, liquid filler material into a laser-dissected intrastromal pocket. We performed the study on ex-vivo porcine eyes. The increase of the refractive power was evaluated by optical coherence tomography (OCT). For a circular pocket, injection of 1 μl filler material increased the refractive power by +4.5 diopters. An astigmatism correction is possible when ellipsoidal intrastromal pockets are created. Injection of 2 μl filler material into an ellipsoidal pocket increased the refractive power by +10.9  dpt on the short and +5.1  dpt on the long axis. OCT will enable to monitor the refractive change during filler injection and is thus a promising technique for real-time dosimetry.