Imaging Speed Research Articles

Single-shot spatial light interference microscopy for dynamic monitoring of membrane fluctuations.

Single-shot spatial light interference microscopy (SS-SLIM) with a pair of non-polarizing beam splitters is proposed for substantially enhancing the speed and efficiency of conventional SLIM systems. Traditional methods are limited by the need for multiple-frame serial modulation and acquisition by spatial light modulators and detectors. Our approach integrates non-polarizing beam splitters to simultaneously capture four phase-shifted intensity images, increasing the imaging speed by at least fourfold while maintaining high quality. This capability is crucial for effectively monitoring the dynamic fluctuations of red blood cell membranes. Furthermore, the potential applications of the SS-SLIM system in biomedical research are demonstrated, particularly in scenarios requiring high temporal resolution and label-free imaging.

Performance improved for two-photon multi-focus microscopy based on a spatial light modulator by eliminating the zero-order beam

Two-photon microscopy (TPM) has a wide range of applications in the biomedical field. Two-photon multi-focus microscopy (TPMM) greatly improves the imaging speed by combining TPM with multi-focus technology. Therefore, TPMM based on spatial light modulator (SLM) has greater advantages in generating multi-focus point (MFP) with uniform intensity and flexible position than to other schemes. However, the zero-order beam (ZOB) produced by SLM in TPMM causes imperfection of the imaging system. For example, some sample areas are scanned repeatedly or illuminated unexpectedly. In this article, we proposed a TPMM system with improved performance by eliminating the ZOB. Apart from the target MFP, we utilize a phase-only SLM to generate another corrective beam (CB) with controllable amplitude, phase, and position precisely. The CB can produce destructive interference completely with the ZOB generated by the dead areas of the SLM. The method has a larger field of view, higher efficiency, and better uniformity in generating MFP compared to the method of overlaying the blaze grating. Simulations and experiments demonstrate the advantages of this approach.

High-speed reflectance confocal microscopy using speckle modulation.

We developed a spectrally-encoded, line reflectance confocal microscope (RCM) that uses a rotating diffuser to rapidly modulate the illumination speckle pattern. The speckle modulation approach reduced speckle noise while imaging with a spatially coherent light source needed for high imaging speed and cellular resolution. The speckle-modulation RCM device achieved lateral and axial resolutions of 1.1 µm and 2.8 µm, respectively. With an imaging speed of 107 frames/sec, three-dimensional RCM imaging over 300-µm depth was completed within less than 1 second. RCM images of human fingers, forearms, and oral mucosa clearly visualized the characteristic cellular features without any noticeable speckle noise.

A Novel Hand-held Multi-color Magnetic Particle Imaging Device based on Rapid Frequency Conversion.

Multi-color Magnetic Particle Imaging (MPI) technology offers high sensitivity and non-invasive imaging capabilities. It can simultaneously image multiple superparamagnetic iron oxide nanoparticles (SPIOs), facilitating more precise detection of multiple molecular markers in vivo. However, the fixed drive frequency of existing hand-held MPI devices makes it difficult to fully match the nonlinear magnetic response of different SPIOs, affecting the spatial resolution and quantitative accuracy of multi-color imaging. We designed a novel rapid frequency conversion based hand-held multi-color magnetic particle imaging (RFC-MPI) device. This device adjusts the drive frequency based on the nonlinear magnetic response of SPIOs at different frequencies, effectively expanding the system matrix information and thereby improving spatial resolution and multi-color imaging capabilities simultaneously. The device achieved a spatial resolution of 2 mm and an imaging speed of 1 frame/s. The scanning depth is 8 mm. It was used to scan a 22 cm x 22 cm area of a human-shaped phantom, verifying its potential for scanning humans. The ability of the device to identify and quantify SPIOs was validated using mice breast tumors. The quantitative accuracy during simultaneous imaging was determined to be 96.58%. Due to its innovative structural design and rapid frequency conversion method, the RFC-MPI device exhibits excellent in vivo imaging performance. Both simulation and phantom experiments have verified the effectiveness of the proposed method. The hand-held RFC-MPI device can effectively improve the spatial resolution and quantitative accuracy of multi-color MPI, laying the foundation for future clinical applications.

High-quality ghost imaging based on undersampled natural-order Hadamard source

Abstract Improving the speed of ghost imaging is one of the main ways to leverage its advantages in sensitivity and imperfect spectral regions for practical applications. Because of the proportional relationship between image resolution and measurement time, when the image pixels are large, the measurement time increases, making it difficult to achieve real-time imaging. Therefore, a high-quality ghost imaging method based on undersampled natural-order Hadamard is proposed. This method uses the characteristics of the Hadamard matrix under undersampling conditions where image information can be fully obtained but overlaps, as well as deep learning to extract aliasing information from the overlapping results to obtain the true original image information. We conducted numerical simulations and experimental tests on binary and grayscale objects under undersampling conditions to demonstrate the effectiveness and scalability of this method. This method can significantly reduce the number of measurements required to obtain high-quality image information and advance application promotion.

A rapidly metabolizable and enzyme-activated NIR fluorescent probe based on isophorone for imaging in vivo

Near-infrared (NIR) fluorescent probes have excellent properties such as fast imaging speed, non-invasive, good stability and low background interference, and have great application potential in biological imaging and surgical guidance. At present, the study of fluorescent probes with long time imaging has attracted extensive attention to guide surgery, while some issues need to be considered, such as toxicity, long tissue residence time and long time for metabolism. Meanwhile, fluorescent probes with rapid metabolism tend to decay fast. Therefore, fluorescent probes need to be continuously designed and synthesized to achieve a balance of performance between suitable imaging time and reasonable metabolic rate. To this end, we design a NIR fluorescence probe that can not only complete the needs of in vivo or cell imaging in a reasonable time, but also can rapidly metabolize and shorten its residence time in the body. The synthesis of a rapidly metabolizable NIR fluorescent probe Kang-CES2 were based on isophorone for enzyme-activated imaging anaplastic thyroid cancer (ATC) in vivo. Kang-CES2 has demonstrated excellent imaging capabilities in vivo and potentiality as a spray and biopsies tool. Meanwhile, the results of intratumoral injection in mice showed that Kang-CES2 can quickly distinguish between normal and ATC tissues and continue to emit NIR light until the end of surgery. The results of tail vein injection of mice showed that Kang-CES2 can rapidly metabolize in the body within 33 minutes. We believe Kang-CES2 has the potential to be a powerful fluorescent tool capable of guiding ATC surgical resection with rapid metabolism.

Light-Sheet Microscopy Enables Three-Dimensional Fluorescence Imaging and Live Imaging of Satellite Cells on Skeletal Muscle Fibers.

The ex vivo myofiber culture system has proven to be a useful methodology to explore the biology and behavior of satellite cells within their niche environment. However, a limitation of this system is that myofibers and their associated satellite cells are commonly examined using conventional fluorescence microscopy, which renders a three-dimensional system into two-dimensional imaging, leading to the loss of precious information or misleading interpretation of observations. Here, we report on the use of light-sheet fluorescence microscopy to generate three-dimensional and live imaging of satellite cells on myofibers. Light-sheet microscopy offers high imaging speed and good spatial resolution with minimal photo-bleaching, allowing live imaging and three-dimensional acquisition of skeletal muscle fiber specimen. The potentials of this technology are wide, ranging from the visualization of satellite cell behavior such as cell division and cell migration to imaging the sub-cellular localization of proteins or organelles.

Highly sensitive volumetric single-molecule imaging.

Volumetric subcellular imaging has long been essential for studying structures and dynamics in cells and tissues. However, due to limited imaging speed and depth of field, it has been challenging to perform live-cell imaging and single-particle tracking. Here we report a 2.5D fluorescence microscopy combined with highly inclined illumination beams, which significantly reduce not only the image acquisition time but also the out-of-focus background by ∼2-fold compared to epi-illumination. Instead of sequential z-scanning, our method projects a certain depth of volumetric information onto a 2D plane in a single shot using multi-layered glass for incoherent wavefront splitting, enabling high photon detection efficiency. We apply our method to multi-color immunofluorescence imaging and volumetric super-resolution imaging, covering ∼3-4 µm thickness of samples without z-scanning. Additionally, we demonstrate that our approach can substantially extend the observation time of single-particle tracking in living cells.

A generalized deep neural network approach for improving resolution of fluorescence microscopy images

Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed, imaging resolution, and imaging flux. This paper proposes a deep neural network based on a generative adversarial network (GAN). The generator employs a U-Net-based network, which integrates DenseNet for the downsampling component. The proposed method has excellent properties, for example, the network model is trained with several different datasets of biological structures; the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging; and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets. In addition, experimental results showed that the method improved the resolution of caveolin-coated pits (CCPs) structures from 264[Formula: see text]nm to 138[Formula: see text]nm, a 1.91-fold increase, and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.

Reconstructing Cancellous Bone From Down-Sampled Optical-Resolution Photoacoustic Microscopy Images With Deep Learning

ObjectiveBone diseases deteriorate the microstructure of bone tissue. Optical-resolution photoacoustic microscopy (OR-PAM) enables high spatial resolution of imaging bone tissues. However, the spatiotemporal trade-off limits the application of OR-PAM. The purpose of this study was to improve the quality of OR-PAM images without sacrificing temporal resolution. MethodsIn this study, we proposed the Photoacoustic Dense Attention U-Net (PADA U-Net) model, which was used for reconstructing full-scanning images from under-sampled images. Thereby, this approach breaks the trade-off between imaging speed and spatial resolution. ResultsThe proposed method was validated on resolution test targets and bovine cancellous bone samples to demonstrate the capability of PADA U-Net in recovering full-scanning images from under-sampled OR-PAM images. With a down-sampling ratio of [4, 1], compared to bilinear interpolation, the Peak Signal-to-Noise Ratio and Structural Similarity Index Measure values (averaged over the test set of bovine cancellous bone) of the PADA U-Net were improved by 2.325 dB and 0.117, respectively. ConclusionThe results demonstrate that the PADA U-Net model reconstructed the OR-PAM images well with different levels of sparsity. Our proposed method can further facilitate early diagnosis and treatment of bone diseases using OR-PAM.

Fast, high-quality, and unshielded 0.2 T low-field mobile MRI using minimal hardware resources.

To propose a deep learning-based low-field mobile MRI strategy for fast, high-quality, unshielded imaging using minimal hardware resources. Firstly, we analyze the correlation of EMI signals between the sensing coil and the MRI coil to preliminarily verify the feasibility of active EMI shielding using a single sensing coil. Then, a powerful deep learning EMI elimination model is proposed, which can accurately predict the EMI components in the MRI coil signals using EMI signals from at least one sensing coil. Further, deep learning models with different task objectives (super-resolution and denoising) are strategically stacked for multi-level post-processing to enable fast and high-quality low-field MRI. Finally, extensive phantom and brain experiments were conducted on a home-built 0.2 T mobile brain scanner for the evaluation of the proposed strategy. 20 healthy volunteers were recruited to participate in the experiment. The results show that the proposed strategy enables the 0.2 T scanner to generate images with sufficient anatomical information and diagnostic value under unshielded conditions using a single sensing coil. In particular, the EMI elimination outperforms the state-of-the-art deep learning methods and numerical computation methods. In addition, 2 × super-resolution (DDSRNet) and denoising (SwinIR) techniques enable further improvements in imaging speed and quality. The proposed strategy enables low-field mobile MRI scanners to achieve fast, high-quality imaging under unshielded conditions using minimal hardware resources, which has great significance for the widespread deployment of low-field mobile MRI scanners.

A Dual-Modal, Label-Free Raman Imaging Method for Rapid Virtual Staining of Large-Area Breast Cancer Tissue Sections.

As one of the most common cancers, accurate, rapid, and simple histopathological diagnosis is very important for breast cancer. Raman imaging is a powerful technique for label-free analysis of tissue composition and histopathology, but it suffers from slow speed when applied to large-area tissue sections. In this study, we propose a dual-modal Raman imaging method that combines Raman mapping data with microscopy bright-field images to achieve virtual staining of breast cancer tissue sections. We validate our method on various breast tissue sections with different morphologies and biomarker expressions and compare it with the golden standard of histopathological methods. The results demonstrate that our method can effectively distinguish various types and components of tissues, and provide staining images comparable to stained tissue sections. Moreover, our method can improve imaging speed by up to 65 times compared to general spontaneous Raman imaging methods. It is simple, fast, and suitable for clinical applications.

Ultrafast Super-Resolution Imaging Exploiting Spontaneous Blinking of Static Excimer Aggregates.

Single-molecule localization methods have been popularly exploited to obtain super-resolved images of biological structures. However, the low blinking frequency of randomly switching emission states of individual fluorophores greatly limits the imaging speed of single-molecule localization microscopy (SMLM). Here we present an ultrafast SMLM technique exploiting spontaneous fluorescence blinking of cyanine dye aggregates confined to DNA framework nanostructures. The DNA template guides the formation of static excimer aggregates as a "light-harvesting nanoantenna", whereas intermolecular excitation energy transfer (EET) between static excimers causes collective ultrafast fluorescence blinking of fluorophore aggregates. This DNA framework-based strategy enables the imaging of DNA nanostructures with 12.5-fold improvement in speed compared to conventional SMLM. Further, we demonstrate the use of this strategy to track the movement of super-resolved DNA nanostructures for over 20 min in a microfluidic system. Thus, this ultrafast SMLM holds great potential for revealing the dynamic processes of biomacromolecules in living cells.

Line-scanning microscopy with laterally symmetric imaging using simultaneous cross-line illumination

Using an on-the-fly scanning scheme, line confocal microscopy can obtain complex structures of large biological tissues with high throughput. Yet, it suffers from lateral imaging asymmetry and thus introduces the potential deformations of the observation results. Here, we propose cross-line illumination microscopy (cLIM) that acquires the imaging data of two perpendicular directions simultaneously through the same objective lens in a line scanning and utilizes two-direction deconvolution fusion to achieve lateral symmetric imaging performance. Imaging fluorescence beads indicates that cLIM reduces lateral resolution asymmetry from 46.1% to 2.5% and improves lateral resolution by 31.0%, compared with traditional line-scanning imaging. Compared with commercial point-confocal microscopy, the cLIM has a 25.84× increase in imaging speed and 1.93× better background-suppressing ability when imaging an 11,306 μm×7783 μm×100 μm mouse kidney slice. We also show the advantages of the cLIM in observing direction-sensitive texture features by imaging a muscular tissue slice. cLIM offers a novel solution to achieve laterally symmetric line-scanning imaging with simple modifications while maintaining high throughput and accuracy for imaging large-scale samples.

Phase-modulated multi-foci microscopy for rapid 3D imaging

3D imaging technology is pivotal in monitoring the functional dynamics and morphological alterations in living cells and tissues. However, conventional volumetric imaging associated with mechanical z-scanning encounters challenges in limited 3D imaging speed with inertial artifact. Here, we present a unique phase-modulated multi-foci microscopy (PM3) technique to achieve snapshot 3D imaging with the advantages of extended imaging depths and adjustable imaging intervals between each focus in a rapid fashion. To accomplish the tasks, we utilize a spatial light modulator (SLM) to encode the phases of the scattered or fluorescence light emanating from a volumetric sample and then project the multiple-depth images of the sample onto a single charge-coupled device camera for rapid 3D imaging. We demonstrate that the PM3 technique provides ∼55-fold improvement in imaging depth in polystyrene beads phantom compared to the depth of field of the objective lens used. PM3 also enables the real-time monitoring of Brownian motion of fluorescent beads in water at a 15 Hz volume rate. By precisely manipulating the phase of scattered light on the SLM, PM3 can pinpoint the specific depth information in living zebrafish and rapidly observe the 3D dynamic processes of blood flow in the zebrafish trunk. This work shows that the PM3 technique developed is robust and versatile for fast 3D dynamic imaging in biological and biomedical systems.

Enhanced Compression Medical Image Speed for Computed Radiography

In recent years, there has been a significant surge in the volume of medical imaging data. This surge poses challenges for the functioning of PACS communication systems and image archiving. The most effective solution to address this issue involves compressing images through digital encryption, which optimally utilizes storage space. This process involves reformatting the imaging data by reducing redundancy, leading to image compression. While this reduction in redundancy is readily apparent in individual images, there is a vulnerability in these methods that tends to overlook a source of repetition present in similar stored images. To emphasize this common occurrence, we introduce the term "redundancy group." Similar images are frequently encountered within medical image databases, resulting in a considerable redundancy reduction. In this paper, our focus is on enhancing the control of redundancy extraction in the data used, specifically medical images. To enhance the compression efficiency of standard image compression, we employ improved methods, namely MinMax Predictive (MMP) and Min-Max Differential (MMD). Our experiments demonstrate that these methods lead to a substantial enhancement in brain CT compression, with potential improvements of up to 130% when using Huffman coding. Similar improvements are observed in arithmetic coding, with a 94% improvement compared to the number-arithmetic code and a 37% improvement compared to the Lempel-Ziv compression. These improvements occur when combining the MMP technology with the MMD technology, utilizing inverse operations that result in lossless compression.

High-robust compressive multimode fiber imaging based on observation vector feedback correction

Multimode fiber (MMF) imaging technology has promising applications in endoscopic systems, and the compressive multimode fiber imaging (CMMFI) scheme has garnered considerable attention due to its simple optical setup and fast imaging speed. However, the imaging quality of this scheme is significantly impacted by detector noise and environmental interference. This paper introduces a sampling method and reconstruction algorithm that utilizes edge detection and region growing techniques for feedback-based correction of the observation vector data acquired from the detector, aiming to reduce the interference of noise in the system. Simulation and experimental tests were conducted to validate this method, and the results underwent statistical analysis. The findings demonstrate that the proposed method improves imaging quality of the low-cost CMMFI without the need for increasing the number of samples. Moreover, it exhibits robustness against noise, further enhancing the reliability of the imaging process. Overall, this research represents a valuable contribution to the advancement of robust endoscopy based on multimode fibers.

CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The ‘CMRxRecon’ dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.

Compressive confocal microscopy imaging at the single-photon level with ultra-low sampling ratios

Laser-scanning confocal microscopy serves as a critical instrument for microscopic research in biology. However, it suffers from low imaging speed and high phototoxicity. Here we build a novel deep compressive confocal microscope, which employs a digital micromirror device as a coding mask for single-pixel imaging and a pinhole for confocal microscopic imaging respectively. Combined with a deep learning reconstruction algorithm, our system is able to achieve high-quality confocal microscopic imaging with low phototoxicity. Our imaging experiments with fluorescent microspheres demonstrate its capability of achieving single-pixel confocal imaging with a sampling ratio of only approximately 0.03% in specific sparse scenarios. Moreover, the deep compressive confocal microscope allows single-pixel imaging at the single-photon level, thus reducing the excitation light power requirement for confocal imaging and suppressing the phototoxicity. We believe that our system has great potential for long-duration and high-speed microscopic imaging of living cells.

Reconstruction method for gamma-ray coded-aperture imaging based on mask and anti-mask functions

Gamma-ray coded-aperture imaging technology plays an important role in nuclear security, decommissioning of nuclear facilities, and nuclear medicine diagnosis. However, under near-field imaging condition, artifacts in the reconstructed image can interfere with identifying the shape and position of the radioactive source. In this paper, a gamma-ray coded-aperture imaging method based on mask and anti-mask functions was proposed to suppress imaging artifacts and speed up the acquisition of low-noise reconstructed images. Through simulation, the effects of the number of iterations and the thickness of the coded-aperture collimator on the imaging quality were studied, and the range of the optimal correction factor in the method was determined. Imaging experiments were conducted using a compact coded-aperture gamma camera based on CdZnTe detector to verify the applicability of the optimal correction factor range. The limitations of the proposed method were analyzed through complex-shaped source imaging simulations and multi-source imaging experiments. This method has an insufficient suppression effect on random artifacts and requires further improvement in imaging irregular radioactive sources. However, it has good imaging performance for single-point source and multi-point sources, effectively reducing regular cross-shaped and stripe-like artifacts. In the non-uniform radioactive background, it can eliminate a part of artifacts, significantly improving imaging quality. Therefore, this method has potential applications in complex radioactive environments.