Fast Imaging Speed Research Articles

Compressed Sensing-Based Cine-MRI for Location of Dysphagia and Swallowing Function assessment in Patients with Head and Neck Cancer: A Comparative Study.

Cine magnetic resonance imaging (Cine-MRI) may evaluate the swallowing function and locations of patients with dysphagia, which requires very fast imaging speed. Compressed sensing is a technique that allows for faster MRI imaging by sampling fewer data points and reconstructing the image via optimization techniques, crucial for capturing the rapid movements involved in swallowing. This study aimed to analyze swallowing function and locations in patients with head and neck cancer and healthy individuals using Cine-MRI based on compressed sensing. This comparative study enrolled 36 patients with dysphagia and 10 healthy controls at the Department of Radiology, Yunnan Cancer Hospital between June 2020 and January 2021. Significant correlations were found between primary tumor location and Cine-MRI parameters, and between swallowing function and Cine-MRI parameters. The areas under the receiver operating characteristic curve for the combined Cine-MRI-related parameters in predicting mild and severe dysphagia were 0.806 (95% CI: 0.662-0.949). Cine-MRI with compressed sensing may identify swallowing function and abnormal stages of the physiologic swallowing process in patients with dysphagia after treatment.

Imaging of permeability defect distribution by electromagnetic tomography with hybrid L1 normand nuclear normpenalty terms.

Electromagnetic tomography (EMT), with the advantages of being non-contact, non-invasiveness, low cost, simple structure, and fast imaging speed, is a multi-functional tomography technique based on boundary measurement voltages to image the conductivity distribution within the sensing field. EMT is widely used in industrial and biomedical fields. Currently, there are few studies on the application of EMT in magnetic permeability materials, which makes it difficult to obtain high-quality reconstructed images due to its own properties that lead to obvious attenuation of electromagnetic waves during propagation, as well as the ill-posed and ill-conditioned characteristics of EMT. In this paper, a multi-feature objective function integrating L2 normregularization, L1 normregularization, and low-rank normregularization is proposed to solve the challenge of magnetic permeability material imaging. This approach emphasizes the smoothness and sparsity. The split Bregman algorithm is introduced to efficiently solve the proposed objective function by decomposing the complex optimization problem into several simple sub-task iterative schemes. In addition, a nine-coil planar array electromagnetic sensor was developed and a flexible modular EMT system was constructed. We use correlation coefficient and error coefficient as indicators to evaluate the performance of the proposed image reconstruction algorithm. The effectiveness of the proposed method in improving the reconstruction accuracy and robustness is verified through numerical simulations and experiments.

Enhanced denoising for weak signal preservation in structured illumination microscopy.

Structured illumination microscopy (SIM) is a powerful super-resolution technology in biological science because of its fast imaging speed, low phototoxicity, and full-field imaging. Despite this, SIM is hampered by out-of-focus background noise, which can obscure weak fluorescence signals and render them unrecognizable. Previous denoising algorithms tended to eliminate the noise along with the weak signals, causing a decrease in image quality. To address this issue, we propose a denoising algorithm based on out-of-focus plane information extraction (OPIE-SIM) that salvages the weak signal from the out-of-focus background noise. The OPIE-SIM algorithm enhances weak fluorescence signals by combining out-of-focus layer information with focal plane data and correcting the differences in point spread functions (PSF). This approach eliminates out-of-focus background noise and preserves the integrity of weak fluorescence structures while significantly reducing image acquisition time compared to traditional over-focusing imaging techniques. Through extensive simulations and experiments, we verified the feasibility of our approach. Compared with other denoising algorithms, our method generates images with a higher signal-to-noise ratio while maintaining the integrity of weak fluorescence structures.

A modified capacitance tomography image reconstruction approach based on iterative shrinkage-thresholding algorithm combined with deep networks

Abstract Due to the ‘soft-field’ effect and the challenges posed by ill-posed and ill-conditioned inverse problems, it is difficult to obtain high quality images from an electrical capacitance tomography (ECT) system. To achieve both high-quality images and fast imaging speed with limited measurement data, an image reconstruction algorithm, which was initially proposed for compressive sensing, is adapted for ECT image reconstruction to optimize the ill-posed nature of its inverse problem. The proposed algorithm leverages deep learning networks inspired by the iterative shrinkage-thresholding algorithm (ISTA), thereby creating a model that is both mathematically interpretable and endowed with trainable parameters. Building upon this foundation, the conventional Landweber iteration is integrated with the ISTA-Net to refine the optimization process for ECT image reconstruction. In order to propose an effective model adapting to the actual multiphase flow characteristics and complex flow pattern changes, the training and test process is driven by a comprehensive dataset generated from dynamic simulations, rather than artificial samples of multiphase distributions. This numerical methodology simulates the dynamic measurement process of a virtual ECT sensor by coupling the gas–liquid two-phase flow field and the ECT electrostatic field. The results of the testing phase indicate that the proposed algorithm outperforms traditional ECT image reconstruction methods. Compared with the linear back projection algorithm, the average image error and gas fraction error have been reduced by 20.44% and 16.74%, respectively, while maintaining a computational speed comparable to that of the Landweber iteration. The accuracy of the new algorithm in reconstructing the two-phase interface and estimating the gas fraction has been validated by static experimental tests, showing its potential for practical application in online gas–liquid two-phase flow measurement scenarios.

Confocal structured illumination microscopy for improving the signal-to-noise ratio and depth of fluorescent optical section imaging.

Light scattering from the sample is an unavoidable problem in fluorescence imaging. Compared with laser scanning confocal scanning microscopy, although optical-sectioning structured illumination microscopy (OS-SIM) has the advantages of fast imaging speed and low phototoxicity, it faces the challenge of removing the scattering fluorescent noise particularly when imaging thick and densely labeled sampling. To improve the imaging performance of OS-SIM, we introduce the concept of confocal imaging to OS-SIM and propose confocal structured illumination microscopy (CSIM). CSIM exploits the principle of dual imaging to reconstruct a dual image from each camera pixel. The scattered fluorescent noise and the unscattered fluorescent signal recorded by the camera pixel are separated in the reconstructed dual image. By extracting the unscattered fluorescent signal from each dual image based on the conjugate relationship between the camera and the spatial light modulator, we can eliminate the scattered fluorescent noise and reconstruct a confocal image. We have built the theoretical framework of CSIM. Experimental results of fluorescent optical-sectioning demonstrate that CSIM achieves a superior performance in eliminating scattered fluorescent noise and in extending imaging depth compared with existing OS-SIM. CSIM is expected to broaden the application range of OS-SIM.

Integrated line-field optical coherence tomography and scheimpflug imaging for corneal imaging

Previously we demonstrated a combined scanning-point optical coherence tomography (OCT) and Scheimpflug Imaging (SI) system, to provide cross-section images of the corneal layers and the whole anterior segment of the eye simultaneously. However, to fully realise the benefits of this dual imaging modality concept towards clinical practice, it is required that the OCT part of the system can achieve ultra-high axial resolution (<3 µm in air) and faster imaging speeds, without using prohibitively expensive components. To achieve this, a new imaging device integrating line-field OCT and SI, using a supercontinuum light source, is demonstrated. This line-field configuration enables both OCT and SI B-scan to be captured using the same light illumination in a single shot, which is the most significant improvement over single point scanning. This achieves an OCT axial resolution down to 2.1 µm in air (1.6 µm in corneal tissue) and an imaging speed up to 213 kA-Scans/s. Since the OCT and SI capture the identical corneal position and serve as cross-validation, this technique offers a great method for accurately determining the individual corneal thickness and refractive index, thereby minimizing individual variations, which can help determine the extent of cutting or correction needed in vision correction. Moreover, the precise measurement of corneal thickness provided by this technique allows for a better understanding of the biomechanical properties of the cornea. We demonstrate the efficacy of the proposed system by evaluating porcine and bovine eyes ex-vivo for determining the corneal refractive index and thickness.

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.

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.

Comprehensive framework of GPU-accelerated image reconstruction for photoacoustic computed tomography.

Photoacoustic computed tomography (PACT) is a promising non-invasive imaging technique for both life science and clinical implementations. To achieve fast imaging speed, modern PACT systems have equipped arrays that have hundreds to thousands of ultrasound transducer (UST) elements, and the element number continues to increase. However, large number of UST elements with parallel data acquisition could generate a massive data size, making it very challenging to realize fast image reconstruction. Although several research groups have developed GPU-accelerated method for PACT, there lacks an explicit and feasible step-by-step description of GPU-based algorithms for various hardware platforms. In this study, we propose a comprehensive framework for developing GPU-accelerated PACT image reconstruction (GPU-accelerated photoacoustic computed tomography), to help the research community to grasp this advanced image reconstruction method. We leverage widely accessible open-source parallel computing tools, including Python multiprocessing-based parallelism, Taichi Lang for Python, CUDA, and possible other backends. We demonstrate that our framework promotes significant performance of PACT reconstruction, enabling faster analysis and real-time applications. Besides, we also described how to realize parallel computing on various hardware configurations, including multicore CPU, single GPU, and multiple GPUs platform. Notably, our framework can achieve an effective rate of times when reconstructing extremely large-scale three-dimensional PACT images on a dual-GPU platform compared to a 24-core workstation CPU. In this paper, we share example codes via GitHub. Our approach allows for easy adoption and adaptation by the research community, fostering implementations of PACT for both life science and medicine.

Structured-Light 3D Imaging Based on Vector Iterative Fourier Transform Algorithm.

Quasi-continuous-phase metasurfaces overcome the side effects imposed by high-order diffraction on imaging and can impart optical parameters such as amplitude, phase, polarization, and frequency to incident light at sub-wavelength scales with high efficiency. Structured-light three-dimensional (3D) imaging is a hot topic in the field of 3D imaging because of its advantages of low computation cost, high imaging accuracy, fast imaging speed, and cost-effectiveness. Structured-light 3D imaging requires uniform diffractive optical elements (DOEs), which could be realized by quasi-continuous-phase metasurfaces. In this paper, we design a quasi-continuous-phase metasurface beam splitter through a vector iterative Fourier transform algorithm and utilize this device to realize structured-light 3D imaging of a target object with subsequent target reconstruction. A structured-light 3D imaging system is then experimentally implemented by combining the fabricated quasi-continuous-phase metasurface illuminated by the vertical-cavity surface-emitting laser and a binocular recognition system, which eventually provides a new technological path for the 3D imaging field.

LIT-Former: Linking In-Plane and Through-Plane Transformers for Simultaneous CT Image Denoising and Deblurring.

This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep learning methods were developed in this context, typically they focus on 2D images and perform denoising due to low-dose and deblurring for super-resolution separately. Up to date, little work was done for simultaneous in-plane denoising and through-plane deblurring, which is important to obtain high-quality 3D CT images with lower radiation and faster imaging speed. For this task, a straightforward method is to directly train an end-to-end 3D network. However, it demands much more training data and expensive computational costs. Here, we propose to link in-plane and through-plane transformers for simultaneous in-plane denoising and through-plane deblurring, termed as LIT-Former, which can efficiently synergize in-plane and through-plane sub-tasks for 3D CT imaging and enjoy the advantages of both convolution and transformer networks. LIT-Former has two novel designs: efficient multi-head self-attention modules (eMSM) and efficient convolutional feed-forward networks (eCFN). First, eMSM integrates in-plane 2D self-attention and through-plane 1D self-attention to efficiently capture global interactions of 3D self-attention, the core unit of transformer networks. Second, eCFN integrates 2D convolution and 1D convolution to extract local information of 3D convolution in the same fashion. As a result, the proposed LIT-Former synergizes these two sub-tasks, significantly reducing the computational complexity as compared to 3D counterparts and enabling rapid convergence. Extensive experimental results on simulated and clinical datasets demonstrate superior performance over state-of-the-art models. The source code is made available at https://github.com/hao1635/LIT-Former.

High-speed and wide-field 3D Raman imaging using flower-like gap-enhanced Raman tags

Raman imaging technology plays a pivotal role in biomedical research, with the pursuit of higher resolution, faster imaging speed, and a larger field of view consistently driving the development of Raman imaging systems. In this work, we propose a high-speed and wide-field three-dimensional (3D) Raman imaging method based on light field microscopy and flower-like gap-enhanced Raman tags (F-GERTs). F-GERTs with enhanced Raman signals and high stability are used to label microbial and cell samples, avoiding the problem of photobleaching. The micro-lens array and scientific camera are incorporated to capture light field Raman images containing spatial information of the sample in a single snapshot acquisition process. The 3D reconstruction algorithm can complete the processing of the light field image within 1 min, reconstructing the 3D Raman image of the sample and the precisely slice structure at different depths. This high-speed and wide-field Raman imaging technology has great potential in the field of real-time 3D Raman imaging of microorganisms and cells.

Comparative analysis of divergence of tipper imaging with natural and orthogonal electric sources

ABSTRACT The divergence of the tipper (DT) is an important imaging method characterized by fast imaging speed and high lateral resolution. It has been recently adopted from the natural source airborne electromagnetic method to the orthogonal electric sources ground-airborne frequency-domain electromagnetic method. However, the imaging characteristics and patterns of DT in the two methods are unclear due to significant differences in the sources used. The application of DT in ground-airborne frequency-domain electromagnetic method is limited. Therefore, this study first identifies the similarities and differences between the two methods from the theory. Furthermore, based on the same simulation model, the response characteristics of the DT excited by natural and orthogonal electric sources are investigated. The similarities and differences between the two methods are clarified. It will contribute to the rapid development and application of the DT imaging in ground-airborne electromagnetic detection method.

Rail Flaw Imaging Prototype Based on Improved Ultrasonic Synthetic Aperture Focus Method

This paper presents an experimental prototype developed for rail flaw imaging. This capability can help obtain quantitative information on detected flaws during manual flaw verification. Ultrasonic synthetic aperture focus (SAF) imaging has advantages over phased-array imaging for both speed and accuracy. The prototype developed is hosted in a portable and battery-powered carry-on size case. The probe is a linear ultrasonic array mounted on a wedge and with a position encoder to build 3D point clouds from 2D beamformed images. The prototype includes several advances over the basic SAF technique, including sparse subarray firing that allows fast imaging speeds (e.g., 25 Hz) without sacrificing image accuracy. Validation results are presented from scans performed on rail sections from the FRA rail defect library, which contains natural transverse defects and artificial end-drilled hole defects. The tests showed good accuracy in defect size and shape, as compared to the available ground truth information, for defects located away from the railhead corners. Additional developments are required to properly cover the head corners, and especially in the case of heavily worn rails.

Super-resolution reconstruction of structured illumination microscopy using deep-learning and sparse deconvolution

Structured illumination microscopy (SIM) is one of the essential super-resolution microscopic imaging techniques, featuring fast imaging speed, low exposure dose, and having no special requirements for fluorescent probes. In this paper, we propose a super-resolution SIM reconstruction approach, which integrates an end-to-end multi-scale neural network (entitled Scale Richardson-Lucy Network, SRLN). SRLN fits the SIM imaging process through Richardson-Lucy deconvolution incorporated in the neural network. After being trained with sufficient data pairs, among which the ground-truth images were refined with sparse deconvolution, the SRLN network can yield super-resolution images with a spatial resolution of ∼70 nm from raw SIM intensity images. The feasibility and generalization capability of SRLN were experimentally demonstrated by reconstructing SR images for different biological structures, of which the raw images were acquired with different devices. The proposed network will be valuable for super-resolution imaging in the biology and medicine fields.

Single-pixel real-part and magnitude imaging system based on digital micromirror device

Single-pixel imaging (SPI) can capture images using a single-pixel detector. However, conventional SPI schemes only provide a magnitude image of the object, where the phase information is completely lost. Here, we present a dual-modal SPI (DMSPI) system capable of simultaneously capturing the real-part and magnitude images. Since the real-part image fuses the phase distribution, DMSPI has a strong information acquisition capability for all types of objects. The DMSPI system utilizes the two reflection arms provided by the digital micromirror device (DMD) to perform zero-frequency detection in one arm and bucket detection in the other to achieve dual-modal imaging. Benefiting from the unique modulation characteristics of DMD, the DMSPI system is simple and efficient, with high spatial resolution and fast imaging speed. Thus, it might find broad applications in biomedical diagnostics and industrial inspection.

Light Addressable Potentiometric Sensors for Biochemical Imaging on Microscale: A Review on Optimization of Imaging Speed and Spatial Resolution.

Light addressable potentiometric sensors (LAPS) are a competitive tool for unmarked biochemical imaging, especially imaging on microscale. It is essential to optimize the imaging speed and spatial resolution of LAPS since the imaging targets of LAPS, such as cell, microfluidic channel, etc., require LAPS to image at the micrometer level, and a fast enough imaging speed is a prerequisite for the dynamic process involved in biochemical imaging. In this study, we discuss the improvement of LAPS in terms of imaging speed and spatial resolution. The development of LAPS in imaging speed and spatial resolution is demonstrated by the latest applications of biochemistry monitoring and imaging on the microscale.

A novel multi-frame wavelet generative adversarial network for scattering reconstruction of structured illumination microscopy.

Structured illumination microscopy (SIM) is widely used in various fields of life science research. In clinical practice, it has low phototoxicity, fast imaging speed and no special fluorescent markers. However, SIM is still affected by the scattering medium of biological tissues, resulting in insufficient resolution of the obtained images, which limits the development of life sciences. A novel multi-frame wavelet generation adversarial network (MWGAN) is proposed to improve the scattering reconstruction capability of SIM.&#xD;Approach: MWGAN is based on two components derived from the original image. A generative adversarial network constructed by wavelet transform is trained to reconstruct some complex details in the cell structure. Multi-frame adversarial network is used to obtain the inter-frame information of the image and use the complementary information of the before and after frames to improve the quality of the model reconstruction.&#xD;Results: To demonstrate the robustness of MWGAN, multiple low-quality SIM image datasets are tested. Compared with the state-of-the-art methods, the proposed method achieves superior performance in both of the subjective and objective evaluation.&#xD;Conclusion: MWGAN is effective for improving the clarity of SIM images. Meanwhile, the SIM images reconstructed by multiple frames improve the reconstruction quality of complex regions and allow clearer and dynamic observation of cellular functions.

Pulse-sampling fluorescence lifetime imaging: evaluation of photon economy.

This Letter presents an experimental study comparing the photon rate and photon economy of pulse sampling fluorescence lifetime imaging (PS-FLIm) with the conventional time-correlated single photon counting (TCSPC) technique. We found that PS-FLIm has a significantly higher photon detection rate (200 MHz) compared with TCSPC (2-8 MHz) but lower photon economy (4-5 versus 1-1.3). The main factor contributing to the lower photon economy in PS-FLIm is laser pulse variability. These results demonstrate that PS-FLIm offers 25× faster imaging speed than TCSPC while maintaining room light rejection in clinical settings. This makes PS-FLIm a robust technique for clinical applications.

An expanded GCaMP reporter toolkit for functional imaging in Caenorhabditis elegans.

In living organisms, changes in calcium flux are integral to many different cellular functions and are especially critical for the activity of neurons and myocytes. Genetically encoded calcium indicators (GECIs) have been popular tools for reporting changes in calcium levels in vivo. In particular, GCaMPs, derived from GFP, are the most widely used GECIs and have become an invaluable toolkit for neurophysiological studies. Recently, new variants of GCaMP, which offer a greater variety of temporal dynamics and improved brightness, have been developed. However, these variants are not readily available to the Caenorhabditis elegans research community. This work reports a set of GCaMP6 and jGCaMP7 reporters optimized for C. elegans studies. Our toolkit provides reporters with improved dynamic range, varied kinetics, and targeted subcellular localizations. Besides optimized routine uses, this set of reporters is also well suited for studies requiring fast imaging speeds and low magnification or low-cost platforms.