Biomedical optics express最新文献

This paper discusses the design, fabrication, and use of thermoplastic polyurethane optical fibers intended for oxygen saturation measurements. It includes an evaluation of the fiber attenuation and the creation of two probes for the measurement assessment. For comparison, a third probe is made using conventional glass optical fibers. The assessment is conducted in two stages: first, absorption measurements are performed using a mixture of methylene blue and intralipid diluted in deionized water; second, a measurement with human blood is conducted to demonstrate the effectiveness of such fibers in measuring blood oxygen saturation. Additionally, a comprehensive study of measurement stability is provided.

Three-photon fluorescence microscopy (3PM) has driven rapid progress in deep-tissue imaging beyond the depth limit of two-photon microscopy, with impacts in neuroscience, immunology, and cancer biology. Three-photon excitation places a premium on ultrashort pulses with high peak power in the 1300- and 1700-nm wavelength bands, which allow deepest imaging. The inefficiency and cost of current sources of these pulses present major barriers to the use of 3PM in biomedical research labs. Fiber sources of such pulses could potentially alleviate these problems, but the peak-power limitations of optical fibers have limited their use in 3PM. Here, we describe a fiber-based source of femtosecond pulses with multi-megawatt peak power. Femtosecond pulses at 1030 nm are launched into an antiresonant hollow-core fiber filled with argon. By varying only the gas pressure, pulses with hundreds of nanojoules of energy and sub-100 fs duration are obtained at wavelengths between 850 and 1700 nm. This approach is a new route to an efficient and potentially low-cost source for deep-tissue imaging. In particular, 960-nJ and 50-fs pulses are generated at 1300 nm with a conversion efficiency of 10%. The nearly 20-MW peak power is an order of magnitude higher than the previous best from a femtosecond solid-core fiber source at 1300 nm. As an example of the capabilities of the source, these pulses are used to image structure and neuronal activity in a mouse brain as deep as 1.1 mm below the dura.

Traditional cell analysis approaches based on quantitative phase imaging (QPI) necessitate a reconstruction stage, which utilizes digital holography. However, phase retrieval processing can be complicated and time-consuming since it needs numerical reconstruction and then phase unwrapping. For analysis of cardiomyocyte (CM) dynamics, it was reported that by estimating the spatial variance of the optical path difference from QPI, the spatial displacement of CMs can be quantified, thereby enabling monitoring of the excitation-contraction activity of CMs. Also, it was reported that the Farnebäck optical flow method could be combined with the holographic imaging information from QPI to characterize the contractile motion of single CMs, enabling monitoring of the mechanical beating activity of CMs for cardiotoxicity screening. However, no studies have analyzed the contractile dynamics of CMs based on raw holograms. In this paper, we present a fast, label-free, and high throughput method for contractile dynamic analysis of human-induced pluripotent stem cell-derived CMs using raw holograms or the filtered holograms, which are obtained by filtering only The proposed approach obviates the need for time-consuming numerical reconstruction and phase unwrapping for CM's dynamic analysis while still having performance comparable to that of the previous methods. Accordingly, we developed a computational algorithm to characterize the CM's functional behaviors from contractile motion waveform obtained from raw or filtered holograms, which allows the calculation of various temporal metrics related to beating activity from contraction-relaxation motion-speed profile. To the best of our knowledge, this approach is the first to analyze drug-treated CM's dynamics from raw or filtered holograms without the need for numerical phase image reconstruction. For one hologram, the reconstruction process itself in the existing methods takes at least three times longer than the process of tracking the contraction-relaxation motion-speed profile using optical flow in the proposed method. Furthermore, our proposed methodology was validated in the toxicity screening of two drugs (E-4031 and isoprenaline) with various concentrations. The findings provide information on CM contractile motion and kinetics for cardiotoxicity screening.

Etomidate is commonly used for induction of anesthesia, but prolonged use can affect brain neurovascular mechanisms, potentially leading to use disorders. However, limited research exists on the impact of etomidate on brain function, and accurately and noninvasively extracting and analyzing neurovascular brain features remains a challenge. This study introduces a novel feature fusion approach based on whole-brain synchronous Electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) signals aimed at addressing the difficulty of jointly analyzing neural and hemodynamic signals and features in specific locations, which is critical for understanding neurovascular mechanism changes in etomidate use disorder individuals. To address the challenge of optimizing the accuracy of neurovascular coupling analysis, we proposed a multi-band local neurovascular coupling (MBLNVC) method. This method enhances spatial precision in NVC analysis by integrating multi-modal brain signals. We then mapped the different brain features to the Yeo 7 brain networks and constructed feature vectors based on these networks. This multilayer feature fusion approach resolves the issue of analyzing complex neural and vascular signals together in specific brain locations. Our approach revealed significant neurovascular coupling enhancement in the sensorimotor and dorsal attention networks (p < 0.05, FDR corrected), corresponding with different frequency bands and brain networks from single-modal features. These features of the intersection of bands and networks showed high sensitivity to etomidate using machine learning classifiers compared to other features (accuracy: support vector machine (SVM) - 82.10%, random forest (RF) - 80.50%, extreme gradient boosting (XGBoost) - 78.40%). These results showed the potential of the proposed feature fusion analysis approach in exploring changes in brain mechanisms and provided new insights into the effects of etomidate on resting neurovascular brain mechanisms.

The filtered back-projection (FBP) algorithm is widely used in photoacoustic computed tomography (PACT) for image reconstruction due to its simplicity and efficiency. Yet, the real-time processing of high-speed PACT data remains challenging for regular FBP implementations. To enhance the reconstruction efficiency of the FBP algorithm, researchers have developed FBP implementations based on the graphics processing units (GPUs). However, existing GPU-accelerated FBP algorithms either sacrifice accuracy for efficiency or are still inefficient for high-speed, real-time PACT imaging. Herein, we report an ultrafast GPU acceleration-based FBP implementation for PACT image reconstruction without sacrificing accuracy. Firstly, the computation complexity of the filtering part of the FBP algorithm is significantly simplified with a pre-computed filtering matrix to enhance filtering efficiency. Secondly, the computation efficiency of the back-projection part of the FBP algorithm is dramatically increased through parallel programming and GPU acceleration. As a result, the proposed FBP implementation takes only 0.38 ms to reconstruct a two-dimensional image of 512 × 512 pixels, which is 439 times faster than regular FBP implementations. Numerical and experimental results show that the proposed FBP implementation outperforms existing GPU-based FBP implementations in reconstruction accuracy and computation efficiency. To the best of our knowledge, this is the fastest implementation of the FBP algorithm ever reported in PACT. This work is expected to provide an ultrafast and accurate image reconstruction solution for high-speed, real-time PACT imaging.

Radiation therapy (RT) is widely used for cancer treatment but is found with side effects of radiation dermatitis and fibrosis thereby calling for timely assessment. Nevertheless, current clinical assessment methods are found to be subjective, prone to bias, and accompanied by variability. There is, therefore, an unmet clinical need to explore a new assessment technique, ideally portable and affordable, making it accessible to less developed regions too. We developed an affordable (16764 CNY) and portable high-resolution ((3.91 μm) darkfield polarization-sensitive multispectral imaging (PS-MSI) microscope. The implementation of the Monte Carlo simulation on the PS multi spectra allows the quantitative analysis of physiological parameters (i.e., blood volume fraction (BVF) and oxygen saturation of hemoglobin) at different skin layers for the dermatitis assessment. Further derivation of the degree of linear polarization (DOLP) reflects randomly distributed collagen fibers associated with fibrosis for the fibrosis assessment. PS-MSI microscope developed revealed a significant decrease (p < 0.001, analysis of variance, ANOVA) in the DOLP associated with fibrosis like scar tissue, and significant (p < 0.001, ANOVA) increases in BVF and oxygen saturation of hemoglobin accompanying artificially induced dermatitis. One-dimensional convolutional neural network implemented on the DOLP and multiple spectra achieved accuracies of 96% and 92.2%, respectively, for the classification of the artificially induced skin dermatitis and fibrosis like scar, demonstrating the potential of the affordable PS-MSI microscope developed for objective, unbiased and consistent assessment of radiation dermatitis and fibrosis in the clinics.

The motion of the trabecular meshwork (TM) facilitates the aqueous drainage from the anterior chamber to the venous system, thereby maintaining normal intraocular pressure. As such, characterizing the TM motion is valuable for assessing the functionality of the aqueous outflow system, as demonstrated by previous phase-sensitive optical coherence tomography (OCT) studies. Current methods typically acquire motion from a single cross-sectional plane along the circumference of the anterior chamber. While effective, the lateral scan pattern only intersects one spatial location on the TM at a time, significantly limiting examination throughput. In this study, we introduce the first volumetric imaging approach for assessing TM motion. Rather than monitoring a single cross-sectional plane, our method employs repeated volumetric scans, allowing for simultaneous observation of a continuous TM band spanning two millimeters. We also show that the field of view could be further expanded by stitching multiple scans. To ensure robust data processing, we developed a customized volume registration algorithm to correct motion artifacts and an automated segmentation algorithm to identify the TM boundary based on the correlation of OCT phase dynamics with heartbeats. Imaging results from a healthy subject confirmed the feasibility of our approach, revealing considerable variation in TM motions at different spatial locations through the stitching process. This proposed methodology offers unprecedented capabilities and examination throughput in the biomechanical imaging of the TM, providing significant scientific insights and diagnostic value for identifying abnormalities in aqueous outflow.

An intraocular lens (IOL) replaces the natural crystalline lens during cataract surgery, and although the vast majority of implants have simple optics, "advanced technology" IOLs have multifocal and extended depth of focus (EDOF) properties. Optical concepts are evaluated here, with image contrast, focal range, and unwanted visual phenomena being the primary concerns. Visual phenomena with earlier bifocal diffractive lenses led to alternative diffractive designs (trifocals, etc.) and also to exploring increasing the depth of focus for monofocal IOLs using refractive methods, where although the defocus range might be more modest, visual phenomena are much less obvious. The designs cover a range of possibilities that might provide the best overall vision for patients with differing motivations, needs, and sensitivity to visual side effects.

Whole slide imaging (WSI) provides tissue visualization at the cellular level, thereby enhancing the effectiveness of computer-aided diagnostic systems. High-precision autofocusing methods are essential for ensuring the quality of WSI. However, the accuracy of existing autofocusing techniques can be notably affected by variations in staining and sample heterogeneity, particularly without the addition of extra hardware. This study proposes a robust autofocusing method based on the difference between Gaussians (DoG) and joint learning. The DoG emphasizes image edge information that is closely related to focal distance, thereby mitigating the influence of staining variations. The joint learning framework constrains the network's sensitivity to defocus distance, effectively addressing the impact of the differences in sample morphology. We first conduct comparative experiments on public datasets against state-of-the-art methods, with results indicating that our approach achieves cutting-edge performance. Subsequently, we apply this method in a low-cost digital microscopy system, showcasing its effectiveness and versatility in practical scenarios.

Lung cancer with heterogeneity has a high mortality rate due to its late-stage detection and chemotherapy resistance. Liquid biopsy that discriminates tumor-related biomarkers in body fluids has emerged as an attractive technique for early-stage and accurate diagnosis. Exosomes, carrying membrane and cytosolic information from original tumor cells, impart themselves endogeneity and heterogeneity, which offer extensive and unique advantages in the field of liquid biopsy for cancer differential diagnosis. Herein, we demonstrate a Gramian angular summation field and MobileNet V2 (GASF-MobileNet)-assisted surface-enhanced Raman spectroscopy (SERS) technique for analyzing exosomes, aimed at precise diagnosis of lung cancer. Specifically, a composite substrate was synthesized for SERS detection of exosomes based on Ti₃C₂Tx Mxene and the array of gold-silver core-shell nanocubes (MGS), that combines sensitivity and signal stability. The employment of MXene facilitates the non-selective capture and enrichment of exosomes. To overcome the issue of potentially overlooking spatial features in spectral data analysis, 1-D spectra were first transformed into 2-D images through GASF. By using transformed images as the input data, a deep learning model based on the MobileNet V2 framework extracted spectral features from higher dimensions, which identified different non-small cell lung cancer (NSCLC) cell lines with an overall accuracy of 95.23%. Moreover, the area under the curve (AUC) for each category exceeded 0.95, demonstrating the great potential of integrating label-free SERS with deep learning for precise lung cancer differential diagnosis. This approach allows routine cancer management, and meanwhile, its non-specific analysis of SERS signatures is anticipated to be expanded to other cancers.