Optics Letters

Structured Illumination Microscopy (SIM) provides imaging with spatial super-resolution, as well as optical sectioning capability, without relying on specialized fluorescent dyes. 2D and 3D variants of this method exist, but most bespoke implementations are 2D-SIM, because it is easier to realize and modify than 3D-SIM. 2D-SIM systems, however, often experience reconstruction artifacts, especially when pushing for high lateral spatial resolution in thicker samples. We present enhanced 2D-SIM, an approach to 2D-SIM where both, coarse patterns optimized for removing out-of-focus background, and fine patterns optimized for resolution improvement beyond the diffraction limit are used. In combination, this achieves 2D-SIM reconstructions with high contrast, spatial super-resolution, and significantly reduced reconstruction artifacts. We present the theoretical framework of this technique, and provide enhanced 2D-SIM imaging results of liver sinusoidal endothelial cells stained with fluorophores emitting at visible and near-infrared wavelengths. Quantitative comparisons of power spectral distribution and image resolution are provided.

Digital holographic microscopy systems in a common-path configuration, compared to systems with a separate reference arm, offer a compact design and resistance to disturbances. They can operate with partially coherent illumination, reducing speckle noise. However, they are limited by the overlapping of the object beam and its laterally shifted replica. As a result, images from different regions of the object overlap on the detector, preventing imaging of dense samples. We present the wavelength-scanning replica-removal method, which solves this problem by enabling the separation of information from both replicas and thereby doubling the effective field of view (FOV). The wavelength-scanning multi-shear replica removal algorithm plays a key role in reconstructing the undisturbed phase from a series of holograms recorded with variable shears. The shear value is controlled by changing the illumination wavelength. This enabled the development of two measurement modes: time-domain wavelength scanning for high-quality imaging, and a single-shot mode with frame division into color channels to improve temporal resolution. The method was validated using resolution tests and biological samples - neurons and dynamic yeast cultures. By combining the advantages of the common-path configuration with dense-structure imaging and dynamic processes, the proposed method constitutes a versatile tool for quantitative phase microscopy.

Beam shaping of ultra-short pulses is essential for medical ultrasound, where single-cycle excitations are required to achieve high axial resolution and improve frame rate. Conventional methods, such as the Gerchberg-Saxton (GS) algorithm or more recent deep learning approaches, are generally effective for continuous-wave excitation but degrade significantly under single-cycle conditions. In diagnostic imaging, high frame rate is critical for applications demanding rapid scanning. In this context, multi-line transmission (MLT) leverages beam shaping to synthesize multiple simultaneous foci, thereby increasing frame rate. In parallel, structured illumination methods for super-resolution and acoustical holography likewise depend on actively shaping single-cycle pulses to produce controlled patterns, highlighting the need for precise short-pulse beam shaping. To address this challenge, we introduce the spatio-temporal adaptive reconstruction (STAR) algorithm, which performs active beam shaping directly in the time domain by integrating the generalized angular spectrum method (GASM) into an iterative optimization scheme. STAR enforces constraints on both the transducer and focal planes, enabling accurate control of single-cycle excitations. Simulations showed that STAR consistently outperformed GS for multi-focus patterns. For example, in a four-foci configuration, STAR achieved a correlation of 0.80 compared to 0.64 for GS, with significantly improved uniformity across focal peaks. Resolution analysis demonstrated that STAR reduced the minimum distinguishable foci spacing from 1.09 mm with GS to 0.87 mm. Experimental hydrophone measurements confirmed these improvements. Across multi-foci patterns, STAR produced more distinct and balanced foci compared to those observed with GS. These results demonstrate that STAR provides robust and efficient active beam shaping of single-cycle pulses, maintaining accuracy across different depths and frequencies for diagnostic applications.

The performance of a laser scanning microscope inevitably depends on the performance of the point detector. As laser scanning approaches aim to penetrate deeper in tissue, there is a commensurate need for detectors that can operate with high sensitivity, bandwidth, and dynamic range at near-infrared wavelengths where scattering is reduced. Here, we demonstrate that fiber optical parametric amplification can be used to boost low-power microscopy signals to levels that can be detected by near-infrared photodiodes without introducing prohibitive noise. We construct amplifiers that achieve >50 dB of parametric gain at wavelengths within the third near-infrared transparency window and have similar sensitivity to near-infrared photomultiplier tubes. Furthermore, these amplifiers outperform detection with a photodiode and subsequent electrical amplification, providing a factor of 10-100-fold improvement in sensitivity. We demonstrate amplifier bandwidths up to ~1.6 GHz, a factor of 10 faster than conventional detectors, including near-infrared photo-multiplier tubes, with sensitivity of ~8 nW (corresponding to ~20 photons/pixel). Finally, the increased performance of the optical amplifier is confirmed in diagnostic imaging experiments where >10x less power is required to achieve the same signal-to-noise ratio and contrast as images using electrical amplification. Accordingly, fiber optical parametric amplification is a new path forward for extending the performance of laser scanning microscopes in the near infrared.

Ultrasound is emerging as a method for molecular and cellular imaging by connecting the versatile physics of sound waves to protein-based contrast agents such as gas vesicles (GVs). BURST is a common imaging mode that leverages the strong, transient echoes generated when GVs collapse under acoustic pressure to enable highly sensitive ultrasound visualization of cells and biomolecules, down to the single cell level. However, BURST is vulnerable to fluctuating background signals, with large-amplitude fluctuations in scattering, as often present in vivo, obscuring genuine GV responses. In this study, we mathematically examine this limitation and show that incorporating statistical metrics such as correlation or temporal contrast-to-noise ratio effectively suppresses unwanted non-GV voxels and quantifies detection confidence, including in image sequences in which GV collapse spans multiple frames. Compared with prior methods, our approach enhances the clarity of BURST images and provides probabilistic interpretations of GV signals, facilitating more reliable analysis of ambiguous in vivo molecular imaging, as we demonstrate in imaging tumor-homing probiotics and gene expression in the brain.

Cerebral blood volume (CBV) and blood flow (CBF) constitute key metrics for cerebrovascular monitoring, enabling assessment of stroke severity and risk-prediction, aging-related changes, and neurological diseases. CBF and CBV monitoring are key aspects in diagnosis, treatment triage, and clinical outcome of ischemic and hemorrhagic strokes. In recent years, there have been ongoing efforts toward the development of optical devices for noninvasive monitoring of CBV and CBF. Speckle contrast optical spectroscopy (SCOS) has recently emerged as a strong candidate for clinical translation in monitoring CBF and CBV, due to its affordability, compact and wearable design, and noninvasive nature. However, experimental demonstrations that SCOS can effectively monitor brain hemodynamics remain sparse. This is primarily due to challenges in design experiments that isolate cerebral blood dynamics from those in the scalp and skull. In this paper, we report experiments using SCOS to monitor cerebral hemodynamics in rats during intracerebral blood flow modulation. To modify cerebral blood dynamics, a surgical procedure was performed to insert a catheter for direct injection of flow modulation fluids into the brain. Using the SCOS device, we monitored changes in CBV during deliberate CBF interventions into the brains of five rats. A saline solution was also injected as a sham control of the flow intervention. The results show a significant decrease in CBV during injection, followed by a return to baseline. This behavior is consistent with physiological expectations, as the injected fluids dilute the blood, leading to a transient reduction in blood volume. Notably, the CBV decrease induced by the flow modulation fluid solution required more than twice as long to recover to baseline compared with the saline solution, which is consistent with the delayed clearance of the flow modulation fluid by design. These experimental results demonstrate the effectiveness of SCOS for monitoring cerebral hemodynamics in animal models and highlight its potential for translation to human studies. Moreover, this work paves the way for the testing and characterization of cerebral therapeutic agents intended for blood flow modulation in animal models.

Clearing Assisted Scattering Tomography (CAST) extends coherent scattering tomography to whole-brain imaging, enabling visualization of fine-scale brain-wide connectivity. As a coherent optical tomography modality, CAST is inherently affected by speckle noise, which degrades image quality and limits quantitative analysis. However, existing speckle reduction methods developed for optical coherence tomography (OCT) are not directly transferable to CAST images due to differences in sample and noise statistics. Here, we present a learning-based cleared-sample speckle reduction network, termed CLEAR Net, specifically designed for CAST imaging, which effectively suppresses speckle noise in whole-brain white matter images while preserving fine structural details. We quantitatively benchmarked CLEAR Net against representative speckle reduction algorithms on CAST datasets and further evaluated its generalizability using publicly available ophthalmic datasets.

Multi-exposure speckle imaging (MESI) estimates flow-related parameters by fitting a physics-based speckle contrast model to measurements acquired over multiple exposure times. In standard pipelines, parameters are recovered via nonlinear least-squares fitting at each pixel, which is computationally expensive and can yield spatially inconsistent maps when uncertainty in the estimated speckle contrast variance [Formula] (from camera noise and finite spatial/temporal sampling used to compute speckle contrast) is amplified by independent pixel wise inversion. This work reframes MESI parameter estimation as identification of a globally shared inverse operator of the analytical forward model, exploiting the fact that a single physical mapping governs all pixels while noise drives large variance in independent pixel wise inversion. Rather than solving millions of iterative optimizations, a single parameterized inverse mapping is learned directly from a single acquired MESI dataset. Physics consistency is enforced by embedding the fixed MESI forward model as an analysis-by-synthesis layer that re-synthesizes speckle contrast curves from the predicted parameters. Training is self-supervised: the inverse mapping is optimized by minimizing a reconstruction loss between measured and re-synthesized speckle contrast curves, which constrains estimates to the set of physically admissible MESI curves without requiring ground truth parameter labels. Experiments on a numerical MESI phantom with known ground truth and on in vivo mouse cortex data show that the proposed method produces more stable inverse correlation time (ICT) maps (1/{tau}c) and improved spatial coherence relative to conventional per-pixel fitting, while substantially reducing inference time by replacing iterative optimization with a single feed-forward evaluation.

Spectral information plays a crucial role in biological imaging, yet conventional epifluorescence and histological techniques often rely on RGB image acquisition, limiting the resolution of spectrally overlapping components. Here, we present a phasor-based spectral analysis framework adapted for RGB images, enabling unsupervised segmentation and unmixing without the need for hyperspectral systems or sequential acquisition. By applying a discrete Fourier transform to the red, green, and blue intensities at each pixel, we generate a two-dimensional phasor plot where spectral relationships are encoded in modulation and phase. We demonstrate the utility of this approach across three distinct applications: segmentation of lung histology images stained with hematoxylin and eosin to quantify alveolar collapse, analysis of autofluorescence in skin lesions (nevi and melanoma) to highlight pathological spectral signatures, and spectral unmixing in multicolor-labeled U2OS cells to resolve overlapping fluorophores. Our method improves signal separation, reduces noise, and enhances biological interpretability using standard RGB acquisition. These findings establish RGB phasor analysis as a practical and powerful tool for spectral decomposition and segmentation in microscopy, bridging the gap between conventional imaging and advanced spectral analysis.

This study uses two different quantitative phase imaging techniques (QPI) and for the first time measures the height, volume, and mass dynamics of Madin-Darby Canine Kidney (MDCK) monolayers. We demonstrate novel methods to determine the height of confluent monolayers of cells from 2D and 3D QPI data and validate that the two methods agree. We developed a novel cell segmentation method adapted to QPI images of confluent cell layers and present a robust measure of relative error. We also demonstrate that height statistics of cells can be obtained without segmenting the images. We obtain the following precisions of cell density (1 %), height (3 %), area (5 %) and volume (6 %). Cell height varies 15-25 % over a monolayer and increases 50-100 % when cell density doubles. The average refractive index and the dry mass fraction of the cells, on the other hand, are constant over the entire density range.

Accurate cell type classification is essential for a wide range of biomedical applications, including disease diagnosis, drug discovery, and the study of cellular processes. Holographic imaging flow cytometry (HIFC) provides label-free quantitative phase imaging (QPI) of individual cells, enabling classification based on phase images. However, reconstructing holograms into phase images involves multi-step image processing, which introduces substantial computational overhead. The availability of diverse image representations across holographic reconstruction stages allows for flexible analytical strategies, enabling the optimization of trade-off between classification accuracy and computational efficiency. Moreover, deep learning offers an efficient alternative, accelerating the reconstruction process while performing accurate classification. However, despite its importance, this optimization challenge remains largely unexplored in the current literature. Here, we present the first systematic evaluation aimed at balancing classification accuracy with computational efficiency, highlighting how different image representations affect overall performance. We focus on a binary classification task discriminating natural killer cells from breast cancer cells. Six distinct classification pipelines are evaluated: direct processing of raw holograms, analysis of demodulated complex fields (CFs), refocused CFs, unwrapped phase images, and two deep learning-based methods that either replace the automatic refocusing stage or perform end-to-end hologram-to-phase reconstruction. For each strategy, we assess both computational cost and classification performance. Our results reveal a clear trade-off: reconstructed phase images provide the highest accuracy, whereas simpler representations or accelerated reconstruction methods significantly reduce processing time with minimal loss of accuracy. A Pareto analysis identifies the optimal set of strategies, offering practical guidelines for selecting image representations and processing pipelines based on available hardware and desired performance. Thus, this work offers a systematic framework for high-throughput deep learning classification in HIFC, serving as a potential reference for future biomedical applications.

The ability to record 3D neuronal activity with cellular resolution, high signal-to-noise ratio (SNR) and millisecond temporal resolution is a major challenge in neuroscience. One powerful method is random-access two-photon microscopy based on acousto-optic deflectors (AODs), which uses a holographically-shaped point spread function (PSF) scanned in 3D to maximize the sampling rate and SNR. However, this approach suffers from greater background contamination due to the holographically shaped PSF than standard two-photon microscopy with diffraction-limited PSF. To overcome this limitation, we implemented a new version of an AOD scanning system, which integrates temporal focusing. The complex spatiotemporal distortions encountered in this configuration, including a significant group delay dispersion associated with the pulse front tilt generated by the AOD, were compensated for by introducing an acousto-optic modulator before the AOD. We designed extended patterns by combining temporal focusing on one direction and holographic wavefront shaping in the perpendicular axis. Taking advantage of the AODs ability to shape the wavefront at the same speed as the scan, we were able to accurately superimpose the spatial and temporal foci over the entire field of view. Finally, we generated complex, extended two-photon excitation patterns by combining temporal focusing in one direction and holographic multiplexing in the perpendicular direction. These patterns provide significantly improved background rejection compared to 2D holographic patterns, thus offering promising prospects for in vivo recordings of neuronal activity in dense samples with improved SNR.

We established a photoacoustic tomography and ultrasound imaging system capable of resolving visually evoked hemodynamic responses in the cortical and subcortical visual regions of the brains of freely behaving mice. By searching for anatomical landmarks in the US imaging planes, we can locate brain regions of interest and continuously record HR in these regions. The system was examined using a 100-minute-long vision research protocol in wild-type mice and mice with vision deficits. We found that: 1) visually evoked HR amplitudes increase as visual stimulation intensity increases in both scotopic and photopic conditions; and 2) HR amplitudes increase during the light adaptation time course.

Optical coherence tomography (OCT) has transformed clinical eye care by providing high-resolution volumetric imaging of the retina. Recently, ultrawide-field-of-view (FOV) OCT played an increasingly significant clinical role; however, most clinical OCT systems offer only a rather limited FOV. We increased the FOV of clinical OCT by volumetrically montaging multiple OCT datasets in three dimensions (3D). We performed volumetric montaging by representing the internal limiting membrane (ILM) and retinal pigment epithelium (RPE) in each volume as point clouds and using these point clouds to compute transformations that map each volume to a common reference frame. We validated our methodology using datasets from three institutions with different OCT hardware and data-acquisition procedures. Using the mean surface distance between point clouds, we found the error in montaging was less than the lateral pixel size. Our method enabled existing clinical OCT to achieve ultrawide FOV imaging without any hardware modification.

Ptychographic single-particle analysis (SPA) is a promising technique for high-resolution biological imaging but is still limited by sub-nanometer resolution. In this study, we identified and investigated a critical issue termed sampling mismatch in ptychography that is caused by inaccuracies in the scanning step size and the pixel size of convergent beam electron diffraction (CBED) images. This mismatch induces pixel-size deviations in the reconstructed micrographs and modulates information transfer through a mismatch-induced modulation function (MIMF), which is characterized by phase reversals at specific spatial frequencies of the micrographs. These phase reversals, which vary with the defocus, cause destructive interference when merging micrographs, fundamentally limiting the resolution of SPA. We proposed a correction strategy and demonstrated, on the T. Acidophilum 20S proteasome and apoferritin datasets, that correcting sampling parameters eliminates signal distortions and improves resolution for [~]1.5 [A]. These findings underscore the necessity for the precise control and calibration of the scanning system to achieve high-resolution ptychographic SPA.

In contemporary bio-imaging-based research, computer-based assessment is becoming crucial for the characterisation of biological structures, as it minimises the need for time-consuming human annotation, which is prone to human error. Furthermore, it allows for the use of optical techniques that use lower photon intensities, thereby reducing reliance on high-intensity excitation and mitigating adverse effects on their activities. This study details the development and evaluation of sophisticated deep-learning models for amoeba detection using phase-contrast imaging. Using a single-class annotated dataset comprising 88 images and 4,131 annotations, we developed nine object detection models based on Detectron 2 and six variants based on YOLO v10. The diversity of the dataset, acquired under varying setup parameters, facilitated a comprehensive evaluation of the strengths and limitations of each model. A comparative analysis of speed and accuracy was performed to identify the most efficient models for real-time detection, providing critical insights for future microscopic analyses.

Sectioning techniques based on patterned illumination have been widely used to obtain well-contrasted images of thick samples using widefield imaging setups. While their application to fluorescence microscopy has been extensively demonstrated and studied, their application to reflection imaging is scarcer and their performance has only been partly characterized. In this paper, we study numerically and analytically two such sectioning techniques, line confocal (LC) and structured illumination (SI), in the context of their application to reflection interference contrast microscopy (RICM), an imaging technique widely use in soft matter and biophysics studies to monitor object-surface interactions, or quantify surface functionalization. Our derivation, however, should provide insight into their use with other reflection methods such as optical coherence tomography (OCT) or scanning laser ophtalmoscope (SLO). We derive approximate analytical equations to relate the performance of sectioning to the optical setup parameters, allowing straightforward understanding of their influence on the achieved image intensity and depth of focus, and we systematically compare our prediction with experimental data. Finally, we quantify the precision and accuracy of each method in typical practical cases, providing guidelines to choose the most appropriate (LC, SI, or a simple background subtraction on a widefield image) for the sample under study.

Accurate blood flow mapping over mesoscale fields of view is essential for understanding physiological and pathological processes, yet conventional optical methods often rely on bulky high-speed cameras that generate massive datasets with excessive computation burden. Here, we introduce Event2Flow, a compact and data-efficient framework leveraging event-based vision sensors, which asynchronously capture brightness changes with sub-millisecond latency and minimal data redundancy. Event2Flow supports multiple contrast mechanisms for flow measurement, including speckle fluctuation and particle tracking. By correlating the event count with flow velocity through simulations and experiments, we first demonstrate its application in laser speckle imaging for noninvasive mapping of mouse ear vasculature and ethanol-induced hemodynamic changes. When integrated with widefield fluorescence localization microscopy and point spread function engineering, Event2Flow further enables kilohertz-rate particle tracking for rapid 3D velocity quantification in transcranial brain imaging and snapshot flow direction estimations using event polarity. Overall, Event2Flow offers a scalable alternative to conventional high-speed imaging systems for vascular and neuroimaging applications.

Laser Speckle Contrast Imaging (LSCI) is a non-contact, label-free optical technique widely used in biomedical research and clinical applications. It enables real-time visualization and quantification of microvascular blood flow by analyzing the temporal fluctuations of laser speckles induced by moving red blood cells. However, conventional LSCI uses visible or near-infrared illumination, which--during prolonged exposure (e.g., >1{square}hr)--can induce sublethal neural stress and cause signal drift, compromising physiological relevance and raising ethical concerns. To mitigate these limitations, we introduce TunLSCI--a TransUNet-based recovery network designed to reconstruct high-fidelity mouse cerebral blood flow (CBF) indices from ultra-low-illumination LSCI. We train our network on paired ultra-low-illumination (1.27 {micro}W/mm2) and conventional LSCI data ([~]200 {micro}W/mm2 illumination, the latter as reference), and demonstrate that it outperforms the conventional standard analytical LSCI processing pipeline based on stLASCA, particularly in reconstructing fine vasculature from few frames, suppressing speckle noise, and maintaining robustness against exposure variations. We validate that the proposed TunLSCI reduces illumination power density by [~]157-fold compared with conventional stLASCA, well below the safety threshold for cortical exposure in mice and markedly improves stability during a 2-hour continuous mouse CBF monitoring. Our method significantly minimizes the phototoxic burden of LSCI while preserving spatiotemporal fidelity and quantitative accuracy, thus enabling longitudinal, high-biosafety cerebral perfusion tracking in vivo over multi-hours.

SignificanceLaser speckle contrast imaging (LSCI) is widely used to measure blood flow, but speckle fluctuations may also encode biologically meaningful dynamics beyond perfusion. Foundational studies in dynamic light scattering (DLS) and micro-optical coherence tomography (OCT) have also demonstrated that slow coherent signal fluctuations can arise from energy-dependent intracellular motion in in vitro and ex vivo systems. Building upon these advances, recent work has shown that LSCI has the potential to detect slow speckle dynamics (SSD) correlated with cellular dynamics in vivo. However, the biophysical mechanisms underlying SSD in intact brain tissues remain insufficiently validated. Establishing a mechanistic bridge from controlled ex vivo and in vitro conditions to in vivo brain measurements is critical for translating speckle-based imaging beyond perfusion measurements to enable label-free assessment of cellular and metabolic activity in disease models. AimThe objective of this study is to investigate the biophysical origin of the SSD in vivo and evaluate its sensitivity to intracellular metabolic activity in brain tissue. ApproachWe utilize an epi-illumination LSCI system to measure speckle contrast as a function of camera exposure time and extract characteristic decorrelation time constants. SSD was investigated in acute mouse brain slices, where blood flow is absent, to eliminate vascular confounds. Cellular metabolism was systematically modulated using 2-deoxyglucose and glucose. Complementary in vivo measurements were performed to reveal SSDs response to hyperoxia and normoxia after ischemic stroke. ResultsSSD signals persisted in acute brain slices in the absence of blood flow. Inhibition of glycolysis significantly reduced SSD, while restoration of metabolic substrates partially recovered the signal. In in vivo measurements, SSD increased during hyperoxia compared to normoxia after ischemic stroke, suggesting increased oxygen-supported cellular metabolic activity. ConclusionsThese results indicate that SSD is sensitive to energy-dependent cellular processes closely tied to metabolic activity. SSD represents a previously uncharacterized, label-free in vivo optical contrast that enables assessment of cellular metabolic activity as well as vascular dynamics. This work establishes a mechanistic foundation for using SSD as a general optical marker of cellular viability in in vivo measurements.