We present photoacoustic computed tomography through an ergodic relay (PACTER), a method for single-shot 3D imaging of hemodynamics using a single-element detector. Our approach allows for ultrafast volumetric imaging at kilohertz rates without the need for numerous detector elements. We demonstrate PACTER in both human and small animal subjects, emphasizing its potential utility in early detection and monitoring of peripheral vascular diseases. Our single-element detector design aims to offer a more convenient and potentially affordable option, while the concept could also be relevant to other imaging technologies, contributing to various applications in medical imaging.
We introduce quantum microscopy by coincidence (QMC) featuring balanced pathlengths, which facilitates super-resolution imaging at the Heisenberg limit, drastically boosting speed and contrast-to-noise ratio (CNR) compared to existing wide-field quantum imaging methods. QMC uses correlated photons traversing symmetric paths, behaving like a photon with half the wavelength for twice the resolution. It withstands 155 times stronger stray light than classical signals, promising non-destructive bioimaging. Our approach propels quantum imaging to microscopic scale by imaging cancer cells. Experimental and theoretical results endorse this balanced pathlength configuration as a path to quantum-enhanced coincidence imaging at the Heisenberg limit.
Quantum correlation is critical in quantum information applications, and numerous inequalities have been established to quantify the non-classical correlations such as the Bell nonlocality and quantum steering. We introduce an experimental method to map full-domain correlation for nonlocality and quantum steering in the Clauser-Horne-Shimony-Holt scenarios. This approach accounts for detection imperfections and simplifies interpretations, answering fundamental questions about nonlocality and quantum steering. Additionally, we illustrate its utility in calibrating an entanglement-based quantum key distribution protocol with arbitrary bipartite states. Our correlation maps offer a direct, straightforward contribution to quantum information applications.
Visualization of the spatiotemporal dynamics of propagation is fundamental to understanding dynamic processes ranging from action potentials to electromagnetic pulses, the two ultrafast processes in biology and physics, respectively. Here, we demonstrate differentially enhanced compressed ultrafast photography to directly visualize propagations of passive current flows at approximately 100 m/s along internodes from Xenopus laevis sciatic nerves and of electromagnetic pulses at approximately 5×107 m/s through lithium niobate. The spatiotemporal dynamics of both propagation processes are consistent with the results from computational models, demonstrating that our method can span these two extreme timescales while maintaining high phase sensitivity.
To extend the depth of field (DOF) in optical-resolution photoacoustic microscopy (OR-PAM), we propose the needle-shaped beam photoacoustic microscopy (NB-PAM) via customized diffractive optical elements to extend the DOF, featuring a well-maintained beam diameter, a uniform axial intensity distribution, and negligible sidelobes. The advantage of using NB-PAM with an improved DOF has been demonstrated by both histology-like imaging of fresh slide-free organs using 266 nm laser and in vivo mouse brain vasculature imaging using 532 nm laser. Our approach provides new perspectives for slide-free intraoperative pathological imaging and various in vivo organ-level imaging applications.
Combining functional optical contrast with high spatiotemporal resolution, photoacoustic computed tomography (PACT) benefits mainstream cardiac imaging modalities for preclinical research. However, PACT has not revealed detailed vasculature or hemodynamics of the whole heart without surgical tissue penetration. Here, we present non-invasive imaging of rat hearts using the recently developed three-dimensional PACT (3D-PACT) platform. 3D-PACT utilizes optimized illumination and detection schemes to reduce the effects of optical attenuation and acoustic distortion through the chest wall, thus visualizing cardiac anatomy and intracardiac hemodynamics within a 10-second scan. We then applied 3D-PACT to investigate different structural and functional variations in healthy, hypertensive, and obese rat hearts. 3D-PACT provides high imaging speed and nonionizing penetration to capture the whole heart for diagnosing animal models, holding promises for clinical translation to human neonatal cardiac imaging without sedation or ionizing radiation.
KEYWORDS: Fluorescence lifetime imaging, Image segmentation, Microscopy, Denoising, In vivo imaging, Luminescence, Convolutional neural networks, Signal to noise ratio, Imaging systems, Signal processing
Fluorescence lifetime imaging microscopy (FLIM) systems are limited by their slow processing speed, low signal- to-noise ratio (SNR), and expensive and challenging hardware setups. In this work, we demonstrate applying a denoising convolutional network to improve FLIM SNR. The network will integrated with an instant FLIM system with fast data acquisition based on analog signal processing, high SNR using high-efficiency pulse-modulation, and cost-effective implementation utilizing off-the-shelf radio-frequency components. Our instant FLIM system simultaneously provides the intensity, lifetime, and phasor plots in vivo and ex vivo. By integrating image de- noising using the trained deep learning model on the FLIM data, provide accurate FLIM phasor measurements are obtained. The enhanced phasor is then passed through the K-means clustering segmentation method, an unbiased and unsupervised machine learning technique to separate different fluorophores accurately. Our experimental in vivo mouse kidney results indicate that introducing the deep learning image denoising model before the segmentation effectively removes the noise in the phasor compared to existing methods and provides clearer segments. Hence, the proposed deep learning-based workflow provides fast and accurate automatic segmentation of fluorescence images using instant FLIM. The denoising operation is effective for the segmentation if the FLIM measurements are noisy. The clustering can effectively enhance the detection of biological structures of interest in biomedical imaging applications.
KEYWORDS: Super resolution, Microscopy, Luminescence, Organisms, Data modeling, X-rays, X-ray imaging, Visualization, Super resolution microscopy, Magnetic resonance imaging
Fluorescence microscopy has enabled a dramatic development in modern biology by visualizing biological organ- isms with micrometer scale resolution. However, due to the diffraction limit, sub-micron/nanometer features are difficult to resolve. While various super-resolution techniques are developed to achieve nanometer-scale resolu- tion, they often either require expensive optical setup or specialized fluorophores. In recent years, deep learning has shown potentials to reduce the technical barrier and obtain super-resolution from diffraction-limited images. For accurate results, conventional deep learning techniques require thousands of images as a training dataset. Obtaining large datasets from biological samples is not often feasible due to photobleaching of fluorophores, phototoxicity, and dynamic processes occurring within the organism. Therefore, achieving deep learning-based super-resolution using small datasets is challenging. We address this limitation with a new convolutional neural network based approach that is successfully trained with small datasets and achieves super-resolution images. We captured 750 images in total from 15 different field-of-views as the training dataset to demonstrate the technique. In each FOV, a single target image is generated using the super-resolution radial fluctuation method. As expected, this small dataset failed to produce a usable model using traditional super-resolution architecture. However, using the new approach, a network can be trained to achieve super-resolution images from this small dataset. This deep learning model can be applied to other biomedical imaging modalities such as MRI and X-ray imaging, where obtaining large training datasets is challenging.
We propose and demonstrate the first analytical model of the spatial resolution of frequency-domain (FD) fluorescence lifetime imaging microscopy (FLIM) that explains how it is fundamentally different with the common resolution limit of the conventional fluorescence microscopy. Frequency modulation (FM) capture effect is also observed by the model, which results in distorted FLIM measurements. A super-resolution FLIM approach based on a localization-based technique, super-resolution radial fluctuations (SRRF), is presented. In this approach, we separately process the intensity and lifetime to generate a super-resolution FLIM composite image. The capability of the approach is validated both numerically and experimentally in fixed cells sample.
In this paper, we show that interfering multiple photon density waves created by intensity-modulated sources in frequency domain diffuse optical spectroscopy (fd-DOS) can be used to recover the optical properties of homogenous and heterogeneous tissues. While fd-DOS can recover the optical properties of homogenous tissue using a single source-detector pair, heterogeneous or layered tissues such as breast, brain, and skin require additional source-detector pairs with multiple separations. Through modelling, we show that the varying illumination patterns created by the interference of two intensity modulated sources can be used to recover the optical properties of two-layer tissue using only a single detector and two phased sources. Two-dimensional fd-DOS models of the conventional multi-distance and proposed multi-phase approaches were compared for homogenous and two-layered tissues. In a homogenous tissue with absorption and reduced scattering coefficients representative of human breast, the simulation results showed that both multi-distance and multi-phase approaches are capable of recovering the absorption and reduced scattering coefficients of the tissue. However, the multi-phase approach has less precision than the conventional multidistance approach. In the two-layer model, the multi-phase approach was capable of recovering the optical properties of both layers, while the multi-distance approach could not.
We propose and demonstrate a novel multiphoton frequency-domain fluorescence lifetime imaging microscopy (MPM-FD-FLIM) system that is able to generate 3D lifetime images in deep scattering tissues. The imaging speed of FD-FLIM is improved using phase multiplexing, where the fluorescence signal is split and mixed with the reference signal from the laser in a multiplexing manner. The system allows for easy generation of phasor plots, which not only address multi-exponential decay problems but also clearly represent the dynamics of the fluorophores being investigated. Lastly, a sensorless adaptive optics setup is used for FLIM imaging in deep scattering tissues. The capability of the system is demonstrated in fixed and living animal models, including mice and zebrafish.
We present the first experimental demonstration of super-resolution multiphoton frequency-domain (FD) fluorescence lifetime imaging microscopy (FLIM). This is obtained through a novel microscopy technique called generalized stepwise optical saturation (GSOS). GSOS√utilizes the linear combination of M steps of raw images to improve the imaging resolution by a factor of √M . Here, a super-resolution multiphoton FD-FLIM is demonstrated on various samples, including fixed cells and biological tissues, with a custom-built two-photon FD-FLIM microscope. We demonstrate simultaneous super-resolution intensity and fluorescence lifetime images of a variety of cell cultures and ex vivo tissues. Combined with multiphoton excitation, the proposed GSOS microscopy is able to generate super-resolution FLIM images deep in scattering samples.
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