Multifocal structured illumination optoacoustic microscopy

Far-Field Phase-Shifting Structured Light Illumination Enabled by Polarization Multiplexing Metasurface for Super-Resolution Imaging

The phase-shifting structured light illumination technique is widely used in imaging but often relies on mechanical translation stages or spatial light modulators, leading to system instability, low displacement accuracy, and limited integration feasibility. In response to these challenges, we propose and demonstrate an approach for generating far-field phase-shifting structured light using a polarization multiplexing metasurface. By controlling the polarization states of incident and transmitted light, the metasurface creates a three-step displacement of structured light, eliminating the need to move samples or illumination sources. As a proof of concept, we experimentally demonstrate microscopic imaging using structured light illumination generated by metasurfaces, extracting high-frequency information from objects, and surpassing the diffraction limit. The proposed metasurface platform offers a promising approach for developing compact and robust phase-shifting imaging systems, with broad prospects in quantitative detection, machine vision, and beyond.

Deep learning-enabled detection of rare circulating tumor cell clusters in whole blood using label-free, flow cytometry

Metastatic tumors have poor prognoses for progression-free and overall survival for all cancer patients. Rare circulating tumor cells (CTCs) and rarer circulating tumor cell clusters (CTCCs) are potential biomarkers of metastatic growth, with CTCCs representing an increased risk factor for metastasis. Current detection platforms are optimized for ex vivo detection of CTCs only. Microfluidic chips and size exclusion methods have been proposed for CTCC detection; however, they lack in vivo utility and real-time monitoring capability. Confocal backscatter and fluorescence flow cytometry (BSFC) has been used for label-free detection of CTCCs in whole blood based on machine learning (ML) enabled peak classification. Here, we expand to a deep-learning (DL)-based, peak detection and classification model to detect CTCCs in whole blood data. We demonstrate that DL-based BSFC has a low false alarm rate of 0.78 events per min with a high Pearson correlation coefficient of 0.943 between detected events and expected events. DL-based BSFC of whole blood maintains a detection purity of 72% and a sensitivity of 35.3% for both homotypic and heterotypic CTCCs starting at a minimum size of two cells. We also demonstrate through artificial spiking studies that DL-based BSFC is sensitive to changes in the number of CTCCs present in the samples and does not add variability in detection beyond the expected variability from Poisson statistics. The performance established by DL-based BSFC motivates its use for in vivo detection of CTCCs. Using transfer learning, we additionally validate DL-based BSFC on blood samples from different species and cancer cell types. Further developments of label-free BSFC to enhance throughput could lead to critical applications in the clinical detection of CTCCs and ex vivo isolation of CTCC from whole blood with minimal disruption and processing steps.

Deep optoacoustic localization microangiography of ischemic stroke in mice

Super-resolution optoacoustic imaging of microvascular structures deep in mammalian tissues has so far been impeded by strong absorption from densely-packed red blood cells. Here we devised 5 µm biocompatible dichloromethane-based microdroplets exhibiting several orders of magnitude higher optical absorption than red blood cells at near-infrared wavelengths, thus enabling single-particle detection in vivo. We demonstrate non-invasive three-dimensional microangiography of the mouse brain beyond the acoustic diffraction limit (<20 µm resolution). Blood flow velocity quantification in microvascular networks and light fluence mapping was also accomplished. In mice affected by acute ischemic stroke, the multi-parametric multi-scale observations enabled by super-resolution and spectroscopic optoacoustic imaging revealed significant differences in microvascular density, flow and oxygen saturation in ipsi- and contra-lateral brain hemispheres. Given the sensitivity of optoacoustics to functional, metabolic and molecular events in living tissues, the new approach paves the way for non-invasive microscopic observations with unrivaled resolution, contrast and speed.

Generation and Modulation of Controllable Multi-Focus Array Based on Phase Segmentation

A Circular-Sectorial Phase Segmentation (CSPS) noniterative method for effectively generating and manipulating muti-focus array (MFA) was proposed in this work. The theoretical model of the CSPS was built up based on vectorial diffraction integral and the phase modulation factor was deduced with inverse fast Fourier transform. By segmenting the entrance pupil into specified regions, which were sequentially assigned with the values carried out by phase modulation factor, the methodology could generate flexible MFAs with desired position and morphology. Subsequently, the CSPS was investigated in parallelized fabrication with a laser direct writing system. The positioning accuracy was greater than 96% and the morphologic consistency of the parallelly fabricated results was greater than 92%.

Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging

Multispectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) which offers excellent soft-tissue contrast and high-resolution brain anatomy. Nevertheless, registration of MSOT-MRI images remains challenging, chiefly due to the entirely different image contrast rendered by these two modalities. Previously reported registration algorithms mostly relied on manual user-dependent brain segmentation, which compromised data interpretation and quantification. Here we propose a fully automated registration method for MSOT-MRI multimodal imaging empowered by deep learning. The automated workflow includes neural network-based image segmentation to generate suitable masks, which are subsequently registered using an additional neural network. The performance of the algorithm is showcased with datasets acquired by cross-sectional MSOT and high-field MRI preclinical scanners. The automated registration method is further validated with manual and half-automated registration, demonstrating its robustness and accuracy.

Photoacoustic computational ghost imaging

Photoacoustic imaging with optical resolution usually requires a single-pixel raster scan. An alternative approach based on illumination with patterns obtained from a Hadamard matrix, measurement of the generated ultrasound wave with a single detector, followed by a reconstruction known from computational ghost imaging is demonstrated here. Since many pixels on the object are illuminated at the same time, thereby contributing to the recorded signal, this approach gives a better contrast-to-noise ratio compared to the raster scan, as demonstrated in a phantom experiment. Furthermore, exploiting the temporal information for depth-resolved imaging is possible. The proposed method will be beneficial in situations where the radiant exposure of a sample is limited due to either safety precautions or the properties of the available light source.

Non-invasive longitudinal imaging of VEGF-induced microvascular alterations in skin wounds

Background: Microcirculation is essential for skin homeostasis and repair. A variety of growth factors have been identified as important regulators of wound healing. However, direct observation and longitudinal monitoring of skin remodeling in an unperturbed in vivo environment remains challenging.

Methods: We report on non-invasive longitudinal imaging of the wound healing process in transgenic mice overexpressing vascular endothelial growth factor A (VEGF-A) in keratinocytes by means of large-scale optoacoustic microscopy (LSOM). This rapid, label-free, high throughput intravital microscopy method averts the use of dorsal skin-fold chambers, allowing for fully non-invasive repeated imaging of intact wounds with capillary resolution over field-of-view spanning several centimeters.

Results: We observed VEGF-driven enhancement of dermal vascularization in ears, dorsal skin and healing wounds and quantified the hemoglobin content, fill fraction, vessel diameter and tortuosity. The in vivo findings were further corroborated by detailed side-by-side classical histological whole-mount vascular stainings and pan-endothelial CD31 immunofluorescence.

Conclusion: The new approach is suitable for supplementing or replacing the cumbersome histological procedures in a broad range of skin regeneration and tissue engineering applications.

Self-Fluence-Compensated Functional Photoacoustic Microscopy

Optical-resolution photoacoustic microscopy (OR-PAM) can image blood oxygen saturation (sO₂) in vivo with high resolution and excellent sensitivity and offers a great tool for neurovascular study and early cancer diagnosis. OR-PAM ignores the wavelength-dependent optical attenuation in superficial tissue, which cause errors in sO₂ imaging. Monte Carlo simulation shows that variations in imaging depth, vessel diameter, and focal position can cause up to  ∼ 60 % decrease in sO₂ imaging. Here, we develop a self-fluence-compensated OR-PAM to compensate for the wavelength-dependent fluence attenuation. We propose a linearized model to estimate the fluence attenuations and use three optical wavelengths to compensate for them in sO₂ calculation. We validate the model in both numerical and physical phantoms and show that the compensation method can effectively reduce the sO₂ errors. In functional brain imaging, we demonstrate that the compensation method can effectively improve sO₂ accuracy, especially in small vessels. Compared with uncompensated ones, the sO₂ values are improved by 10~30% in the brain. We monitor ischemic-stroke-induced brain injury which demonstrates great potential for the pre-clinical study of vascular diseases.

High-speed photoacoustic microscopy: A review dedicated on light sources

In recent years, many methods have been investigated to improve imaging speed in photoacoustic microscopy (PAM). These methods mainly focused upon three critical factors contributing to fast PAM: laser pulse repetition rate, scanning speed, and computing power of the microprocessors. A high laser repetition rate is fundamentally the most crucial factor to increase the PAM speed. In this paper, we review methods adopted for fast PAM systems in detail, specifically with respect to light sources. To the best of our knowledge, ours is the first review article analyzing the fundamental requirements for developing high-speed PAM and their limitations from the perspective of light sources.

Optoacoustic imaging of the skin

Optoacoustic (OA, photoacoustic) imaging capitalizes on the synergistic combination of light excitation and ultrasound detection to empower biological and clinical investigations with rich optical contrast while effectively bridging the gap between micro and macroscopic imaging realms. State-of-the-art OA embodiments consistently provide images at micron-scale resolution through superficial tissue layers by means of focused illumination that can be smoothly exchanged for acoustic-resolution images at diffuse light depths of several millimetres to centimetres via ultrasound beamforming or tomographic reconstruction. Taken together, this unique multi-scale imaging capacity opens unprecedented capabilities for high-resolution in vivo interrogations of the skin at scalable depths. Moreover, diverse anatomical and functional information is retrieved via dynamic mapping of endogenous chromophores such as haemoglobin, melanin, lipids, collagen, water and others. This, along with the use of non-ionizing radiation, facilitates a clinical translation of the OA modalities. We review recent progress in OA imaging of the skin in preclinical and clinical studies exploiting the rich contrast provided by endogenous substances in tissues. The imaging capabilities of existing approaches are discussed in the context of initial translational studies on skin cancer, inflammatory skin diseases, wounds and other conditions.