Biomedical optics express最新文献

Ultrasound (US) imaging is a standard clinical tool for assessment of soft tissue inflammation, particularly for imaging the peripheral joints of the hands and feet, which are usually the first to be affected by rheumatoid arthritis. Robotic US has recently gained attention as a means of delivering repeatable and operator-independent imaging. Previously developed robotic US for 3D volumetric imaging of human finger joints relies on the linear scan of the target joint to collect a series of 2D B-mode US images. Although effective in many contexts and simple in control and image reconstruction, the linear scan is suboptimal for imaging cylindrical tissue structures such as human fingers, often exhibiting degraded image quality in peripheral regions due to poor angular alignment with the tissue surface. To address this limitation, we developed a robotic US system incorporating an arc-shaped scanning trajectory designed to maintain a consistently perpendicular orientation to the curved surface of the finger throughout the scan. In an experiment on a spherical phantom, arc scan yielded improved boundary sharpness and image contrast compared to linear scan. In a clinical study involving both a healthy volunteer and an arthritis patient, the arc scan produced better B-mode US image quality in imaging finger joints compared to the linear scan, as reflected in more consistent representations of phalangeal and soft tissue structures along different radial directions. Functional Doppler US and photoacoustic (PA) imaging of finger joints were also conducted. Arc scan and linear scan achieved comparable detection of vascular signals. These findings demonstrate that arc scan facilitated by the robotic arm can achieve improved B-mode US imaging of tissue anatomy in human finger joints while preserving the functional imaging capability of Doppler US and PA imaging.

Pulsed near-infrared (NIR) lasers can be perceived as light of approximately half their wavelength due to the process of two-photon (2P) absorption. For high intensities of light, single-photon (1P) absorption can still be perceived beyond 700 nm, so that the overall color perception in this region is the result of a mix of 1P and 2P absorption. In this study, color matching experiments were performed with seven laser wavelengths between 730 and 920 nm to investigate the interaction between 1P and 2P absorption and the range of colors that can be created by changing the laser power and repetition frequency of a ns-pulsed laser. We recorded color matches in a range from pure red for shorter wavelengths across shades of purple up to pure blue colors for the longest wavelengths, showing that nonspectral (purple) colors can be created using only one stimulating wavelength in the NIR. Changes in hue could be observed between wavelengths of 850 nm and 920 nm when laser power or repetition frequency were modified, with the highest color shifts occurring between 880 nm and 900 nm.

Photoacoustic imaging (PAI) is a hybrid imaging modality that combines optical excitation with ultrasound (US) detection to achieve high-resolution, high-contrast imaging of biological tissues. As a non-ionizing and scalable imaging technique, PAI has been widely applied in biomedical research and clinical diagnostics, enabling functional and molecular imaging of a variety of medical conditions. In recent years, the rapid advancement of medical robotics has introduced new possibilities for PAI, particularly in the technologies of automated scanning, teleoperated robotics systems, and microrobots. This review surveys the latest developments at the intersection of PAI and robotics, categorizing them by integration level and application domain, with a primary focus on biomedical and clinical implementations. We conclude with perspectives on how these emerging technologies may drive future innovations in imaging, intervention, and healthcare robotics.

Tumor surgery requires rapid intraoperative histopathological diagnosis to guide treatment and improve patient prognosis. Autofluorescence (AF) imaging offers a label-free approach with strong potential for rapid intraoperative use; however, most studies to date have been performed on deparaffinized sections based on bandpass emission settings. Its compatibility with frozen sections-which is essential for time-constrained intraoperative settings-remains unclear. Here, we systematically compared AF imaging of frozen sections with conventional deparaffinized sections, finding that the bandpass-filtered AF signal of frozen sections was weak and susceptible to photobleaching. To address this issue, we demonstrated multi-excitation and broad-emission (MEBE) AF imaging of frozen sections and showed that MEBE AF imaging of frozen sections enabled reliable tissue morphology assessment. This approach potentially provides diagnostic quality comparable to standard H&E staining and paves the way for rapid intraoperative histopathological diagnosis. We further evaluated the approach in liver disease models and found that MEBE AF imaging accurately delineated tumor boundaries in liver cancer and assessed injury severity in acute liver injury, consistent with histopathological confirmation. Our study bridges MEBE AF imaging of frozen and deparaffinized sections, along with preliminary exploration in liver disease diagnosis. The findings will advance AF-based histopathology for clinical translation, enhancing surgical efficiency and patient prognosis.

Previously, we introduced a microscope design that enabled rapid, random-access well plate imaging [ eLife, 10, e56426 (2021)10.7554/eLife.56426]. Here, we implement this design in a low-cost, compact, and portable prototype (the Exeter Multiscope) and apply it to the problem of capturing the contraction of cardiomyocyte monolayers, which have been plated into nine wells within a 96-well plate. Using a transmissive rather than reflective geometry, each well is sampled using 500 × 500 pixels across a 1.4 × 1.4 mm field of view, acquired in three colours at 3.7 Hz per well. The use of multiple illumination wavelengths provides post-hoc focus selection, further increasing the level of automation. The performance of the Exeter Multiscope is benchmarked against industry standard methods using a commercial microscope with a motorised stage and demonstrates that the Multiscope can acquire data almost 40 times faster. The data from both Multiscope and the commercial systems are processed by a 'pixel variance' algorithm that uses information from the pixel value variability over time to determine the timing and amplitude of tissue contraction. This algorithm is also benchmarked against an existing algorithm that employs an absolute difference measure of tissue contraction.

While data-driven deep learning has empirically advanced ptychographic reconstruction, its inherent limitations-a lack of theoretical interpretability and limited adaptability-remain unresolved. Emerging hybrid architectures integrate physics-based ptychographic iterative engines (PIE) with machine-learning (ML) optimization to preserve interpretability while achieving superior gradient-search performance. Our previous work introduced momentum-accelerated co-optimization (using first- and second-order methods) for single-iteration PIE updates, which simplified hyperparameter configuration in ML-enhanced modules. However, PIE's inherent process of two-dimensional fixed-point adjustment creates a paradox between optimization and stability: achieving high performance requires compensatory hyperparameters to balance transient performance and long-term convergence. This dilemma leads to a fundamental conflict between momentum-driven adaptability and iterative equilibrium, posing a challenge for developing universally stable hybrid architectures. To address these limitations, we have revisited the optimization direction selection in conventional PIE workflows by analyzing Fourier ptychographic microscopy (FPM). We introduce a three-dimensional (3D) autonomous iterative path design framework in which the reconstruction stage is treated as a third spatial dimension. This transforms the conventional challenge of 2D fixed-point tuning into a systematic parameter space planning problem. Extensive tests demonstrate that our proposed method, Adam-DPIE (Dynamic PIE with Adaptive Moment Estimation integration), overcomes three key constraints in current designs: the large number of hyperparameters, hyperparameter sensitivity, and the trade-off between optimization and stability. Remarkably, Adam-DPIE achieves this with only a single hyperparameter while maintaining backward compatibility. This approach provides both methodological insights into PIE research and practical solutions enabling high-performance biomedical imaging systems.

Computational corrections of defocus and aberrations in optical coherence tomography (OCT) offers a promising approach to realize high-resolution imaging with deep imaging depth, but without additional high hardware costs. However, these techniques are not well understood owing to a lack of accurate theoretical models and investigation tools. The image formation theory for OCT with optical aberrations is thus formulated here. Based on this theory, a numerical simulation method is developed, and computational refocusing and computational aberration correction (CAC) methods are designed. The CAC method based on the image formation theory is applied to simulated OCT signals and OCT images of a microparticle phantom and an in vivo human retina for simultaneous multi-depth correction of systematic aberration. The numerical simulation under the effective numerical aperture of 0.2 and 1.05 µm central wavelength shows that the proposed method can obtain the Strehl ratios of more than 0.8 over a ± 100 µm defocus range, while the conventional method cannot achieve this under the simulated conditions. Imaging results show that the CAC method designed based on the image formation theory can correct optical aberrations and improve the image quality more than the conventional CAC method. The proposed method improved the frequency component corresponding to the density of cone photoreceptors in OCT photoreceptor images by 1.2 to 1.4 times under the multi-depth correction. This theoretical model-based approach provides a powerful aid for understanding OCT imaging properties and processing method design.

Recent advancements in multispectral (MS) and hyperspectral (HS) microscopy have focused on sensor and system improvements, yet sample processing remains overlooked. We conducted an analysis of the literature, revealing that 40% of studies do not report sample thickness. Among those that did report it, the vast majority, 98%, used 2-10 µm samples. This study investigates the impact of unstained sample thickness on MS/HS image quality through light transport simulations. Monte Carlo simulations were conducted on various tissue types (i.e., breast, colorectal, liver, and lung) using optical property parameters extracted from the literature. The simulations revealed that thin samples reduce tissue differentiation, while higher thicknesses (approximately 500 µm) improve discrimination, though at the cost of reduced light intensity. Although the results are based on idealized conditions and exclude certain real-world factors such as sample variability and instrument-specific effects, they highlight the need to study and optimize sample thickness for enhanced tissue characterization and diagnostic accuracy in MS/HS microscopy.

Optical coherence tomography angiography (OCTA) is a pivotal imaging modality for non-invasive visualization of the retinal microvasculature, but current clinical OCTA systems lack the capability to segment and quantify vascular features separately for arteries and veins. This study introduces OCTA-ReVA^{+ AV}, an open-source, fully automated toolbox that integrates deep learning-based artery-vein (AV) segmentation and vessel-specific quantitative analysis of OCTA images. OCTA-ReVA^{+ AV} computes a comprehensive set of vascular metrics including blood vessel density (BVD), vessel skeleton density (VSD), vessel perimeter index (VPI), blood vessel caliber (BVC), blood vessel tortuosity (BVT), vessel complexity index (VCI), perfusion intensity density (PID), vessel area flux (VAF), and normalized blood flow index (NBFI) independently for arteries and veins. These features are extracted within a user-friendly graphical interface and demonstrate high repeatability and segmentation consistency. By separately quantifying arterial and venous alterations, OCTA-ReVA^{+ AV} fills a critical gap in OCTA analytics, enhancing detection and monitoring of retinal vascular diseases.

Light sheet fluorescence microscopy (LSFM) has transformed the way we visualize biological tissues in three dimensions, offering high-resolution imaging while minimizing photo-induced damage to the samples. Recent breakthroughs in tissue-clearing methods have further improved LSFM's capabilities, making it possible to study larger, intact samples in unprecedented detail. To overcome limitations like shallow penetration and light diffraction in traditional LSFM setups, advanced beam shaping with devices such as spatial light modulators and digital micromirror arrays has been utilized. These improve the resolution and the extent of tissues that can be imaged. Advances such as Bessel-beam-based LSFM and lattice light sheet microscopy increase the field of view that can be imaged with low background noise, but often require complex and bulky equipment. Addressing these complexities, a new approach builds on silicon nitride photonic integrated circuits to create a structured light sheet in a very compact device. This system incorporates beam-steering based on wavelength control in a limited tuning range and enables the generation of light sheets with optimized characteristics in regard to thickness and diffraction-length-limited penetration depth. Simulations include extensive fabrication tolerance analysis that confirms the practicability of the approach, which can be straightforwardly extended to dual wavelength excitation. This compact, chip-based LSFM system could make high-quality imaging more accessible and transform biomedical instrumentation.