Neutron-induced gamma-ray imaging is a spectroscopic technique that uses characteristic gamma rays to infer the elemental distribution of an object.Currently,this technique requires the use of large facilities to supp...Neutron-induced gamma-ray imaging is a spectroscopic technique that uses characteristic gamma rays to infer the elemental distribution of an object.Currently,this technique requires the use of large facilities to supply a high neutron flux and a time-consuming detection procedure involving direct collimating measurements.In this study,a new method based on low neutron flux was proposed.A single-pixel gamma-ray detector combined with random pattern gamma-ray masks was used to measure the characteristic gamma rays emitted from the sample.Images of the elemental distribution in the sample,comprising 30×30 pixels,were reconstructed using the maximum-likelihood expectation-maximization algorithm.The results demonstrate that the elemental imaging of the sample can be accurately determined using this method.The proposed approach,which eliminates the need for high neutron flux and scanning measurements,can be used for in-field imaging applications.展开更多
In x-ray dark-field imaging using dual phase grating interferometer,multi-contrast signals are extracted from a set of acquired phase-stepping data by using the least-squares fitting algorithm.The extracted mean inten...In x-ray dark-field imaging using dual phase grating interferometer,multi-contrast signals are extracted from a set of acquired phase-stepping data by using the least-squares fitting algorithm.The extracted mean intensity,amplitude and visibility signals may be intrinsically biased.However,it is still unclear how large these biases are and how the data acquisition parameters influence the biases in the extracted signals.This work set out to address these questions.Analytical expressions of the biases of the extracted signals were theoretically derived by using a second-order Taylor series expansion.Extensive numerical simulations were performed to validate the theoretical results.It is illustrated that while the estimated mean intensity signal is always unbiased,the estimated amplitude and visibility signals are both positively biased.While the biases of the estimated amplitude signals are proportional to the inverse of the total number of phase steps,the biases of the estimated visibility signals are inversely proportional to the product of the total number of phase steps and the mean number of photons counted per phase step.Meanwhile,it is demonstrated that the dependence of the biases on the mean visibility is quite different from that of Talbot-Lau interferometer due to the difference in the intensity model.We expect that these results can be useful for data acquisition optimizations and interpretation of x-ray dark-field images.展开更多
Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferome...Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.展开更多
Structured-illumination reflectance imaging(SIRI)provides a new means for food quality detection.This original work investigated the capability of(SIRI)technique coupled with multivariate chemometrics to evaluate the ...Structured-illumination reflectance imaging(SIRI)provides a new means for food quality detection.This original work investigated the capability of(SIRI)technique coupled with multivariate chemometrics to evaluate the microbial contamination in pork inoculated with Pseudomonas fluorescens and Brochothrix thermosphacta during storage at different temperatures.The prediction performances based on different spectrum and the textural features of direct component and amplitude component images demodulated from the SIRI pattern,as well as their data fusion were comprehensively compared.Based on the full wavelength spectrum(420-700 nm)of amplitude component images,the orthogonal signal correction coupled with support vector machine regression provided the best predictions of the number of P.fluorescens and B.thermosphacta in pork,with the determination coefficients of prediction(R_(p)^(2))values of 0.870 and 0.906,respectively.Besides,the prediction models based on the amplitude component or direct component image textural features and the data fusion models using spectrum and textural features from direct component and amplitude component images cannot significantly improve their prediction accuracy.Consequently,SIRI can be further considered as a potential technique for the rapid evaluation of microbial contaminations in pork meat.展开更多
Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithm...Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.展开更多
In this work,one-step growth models using hyperspectral imaging(HSI)(400-1000 nm)were successfully developed in order to estimate the microbial loads,minimum growth temperature(T_(min))and maximum specific growth rate...In this work,one-step growth models using hyperspectral imaging(HSI)(400-1000 nm)were successfully developed in order to estimate the microbial loads,minimum growth temperature(T_(min))and maximum specific growth rate(μ_(max))of Brochothrix thermosphacta in chilled beef at isothermal temperatures(4-25℃).Three different methods were compared for model development,particularly using(Model Ⅰ)the predicted microbial loads from partial least squares regression of the whole spectral variables;(Model Ⅱ)the selected spectral variables related to microbial loads;and(Model Ⅲ)the first principal scores of HSI spectra by principal component analysis.Consequently,Model Ⅰ showed the best ability to predict the microbial loads of B.thermosphacta,with the coefficient of determination(R_(v)^(2))and root mean square error in internal validation(RMSEV)of 0.921 and 0.498(lg(CFU/g)).The T_(min)(-12.32℃)andμmax can be well estimated with R^(2) and root mean square error(RMSE)of 0.971 and 0.276(lg(CFU/g)),respectively.The upward trend ofμmax with temperature was similar to that of the plate count method.HSI technique thus can be used as a simple method for one-step growth simulation of B.thermosphacta in chilled beef during storage.展开更多
Ultrasonic Lamb waves undergo complex mode conversion and diffraction at non-penetrating defects, such as plate corrosion and cracks. Lamb wave imaging has a resolution limit due to the guided wave dispersion characte...Ultrasonic Lamb waves undergo complex mode conversion and diffraction at non-penetrating defects, such as plate corrosion and cracks. Lamb wave imaging has a resolution limit due to the guided wave dispersion characteristics and Rayleigh criterion limitations. In this paper, a full convolutional network is designed to segment and reconstruct the received signals, enabling the automatic identification of target modalities. This approach eliminates clutter and mode conversion interference when calculating direct and accompanying acoustic fields in time-domain topological energy(TDTE) imaging.Subsequently, the measured accompanying acoustic field is reversed for adaptive focusing on defects and enhance the imaging quality. To circumvent the limitations of the Rayleigh criterion, the direct acoustic field and the accompanying acoustic field were fused to characterize the pixel distribution in the imaging region, achieving Lamb wave super-resolution imaging. Experimental results indicate that compared to the sign coherence factor-total focusing method(SCF-TFM),the proposed method achieves a 31.41% improvement in lateral resolution and a 29.53% increase in signal-to-noise ratio for single-blind-hole defects. In the case of multiple-blind-hole defects with spacings greater than the Rayleigh criterion resolution limit, it exhibits a 27.23% enhancement in signal-to-noise ratio. On the contrary, when the defect spacings are relatively smaller than the limit, this method has a higher resolution limit than SCF-TFM in super-resolution imaging.展开更多
Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large hospitals.This voluminous data stream requires a significant amount of network bandwidth...Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large hospitals.This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure.Additionally,local data processing demands substantial manpower and hardware investments.Data isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and research.In this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain technology.This system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative work.The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)format.Then,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic analysis.Then,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services.The Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.展开更多
Gradient coil is an essential component of a magnetic resonance imaging(MRI)scanner.To achieve high spatial resolution and imaging speed,a high-efficiency gradient coil with high slew rate is required.In consideration...Gradient coil is an essential component of a magnetic resonance imaging(MRI)scanner.To achieve high spatial resolution and imaging speed,a high-efficiency gradient coil with high slew rate is required.In consideration of the safety and comfort of the patient,the mechanical stability,acoustic noise and peripheral nerve stimulation(PNS)are also need to be concerned for practical use.In our previous work,a high-efficiency whole-body gradient coil set with a hybrid cylindrical-planar structure has been presented,which offers significantly improved coil performances.In this work,we propose to design this transverse gradient coil system with transformed magnetic gradient fields.By shifting up the zero point of gradient fields,the designed new Y-gradient coil could provide enhanced electromagnetic performances.With more uniform coil winding arrangement,the net torque of the new coil is significantly reduced and the generated sound pressure level(SPL)is lower at most tested frequency bands.On the other hand,the new transverse gradient coil designed with rotated magnetic gradient fields produces considerably reduced electric field in the human body,which is important for the use of rapid MR sequences.It's demonstrated that a safer and patient-friendly design could be obtained by using transformed magnetic gradient fields,which is critical for practical use.展开更多
Acoustic reflection imaging logging technology can detect and evaluate the development of reflection anomalies,such as fractures,caves and faults,within a range of tens of meters from the wellbore,greatly expanding th...Acoustic reflection imaging logging technology can detect and evaluate the development of reflection anomalies,such as fractures,caves and faults,within a range of tens of meters from the wellbore,greatly expanding the application scope of well logging technology.This article reviews the development history of the technology and focuses on introducing key methods,software,and on-site applications of acoustic reflection imaging logging technology.Based on the analyses of major challenges faced by existing technologies,and in conjunction with the practical production requirements of oilfields,the further development directions of acoustic reflection imaging logging are proposed.Following the current approach that utilizes the reflection coefficients,derived from the computation of acoustic slowness and density,to perform seismic inversion constrained by well logging,the next frontier is to directly establish the forward and inverse relationships between the downhole measured reflection waves and the surface seismic reflection waves.It is essential to advance research in imaging of fractures within shale reservoirs,the assessment of hydraulic fracturing effectiveness,the study of geosteering while drilling,and the innovation in instruments of acoustic reflection imaging logging technology.展开更多
This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications.The widespread international research and activity in polarimetric imaging techni...This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications.The widespread international research and activity in polarimetric imaging techniques demonstrate their broad applications and interest.Polarization information is increasingly incorporated into convolutional neural networks(CNN)as a supplemental feature of objects to improve performance in computer vision task applications.Polarimetric imaging and deep learning can extract abundant information to address various challenges.Therefore,this article briefly reviews recent developments in data-driven polarimetric imaging,including polarimetric descattering,3D imaging,reflection removal,target detection,and biomedical imaging.Furthermore,we synthetically analyze the input,datasets,and loss functions and list the existing datasets and loss functions with an evaluation of their advantages and disadvantages.We also highlight the significance of data-driven polarimetric imaging in future research and development.展开更多
Stimulated emission depletion microscopy(STED)holds great potential in biological science applications,especially in studying nanoscale subcellular structures.However,multi-color STED imaging in live-cell remains chal...Stimulated emission depletion microscopy(STED)holds great potential in biological science applications,especially in studying nanoscale subcellular structures.However,multi-color STED imaging in live-cell remains challenging due to the limited excitation wavelengths and large amount of laser radiation.Here,we develop a multiplexed live-cell STED method to observe more structures simultaneously with limited photo-bleaching and photo-cytotoxicity.By separating live-cell fluorescent probes with similar spectral properties using phasor analysis,our method enables five-color live-cell STED imaging and reveals long-term interactions between different subcellular structures.The results here provide an avenue for understanding the complex and delicate interactome of subcellular structures in live-cell.展开更多
Interpreting experimental diagnostics data in tokamaks,while considering non-ideal effects,is challenging due to the complexity of plasmas.To address this challenge,a general synthetic diagnostics(GSD)platform has bee...Interpreting experimental diagnostics data in tokamaks,while considering non-ideal effects,is challenging due to the complexity of plasmas.To address this challenge,a general synthetic diagnostics(GSD)platform has been established that facilitates microwave imaging reflectometry and electron cyclotron emission imaging.This platform utilizes plasma profiles as input and incorporates the finite-difference time domain,ray tracing and the radiative transfer equation to calculate the propagation of plasma spontaneous radiation and the external electromagnetic field in plasmas.Benchmark tests for classical cases have been conducted to verify the accuracy of every core module in the GSD platform.Finally,2D imaging of a typical electron temperature distribution is reproduced by this platform and the results are consistent with the given real experimental data.This platform also has the potential to be extended to 3D electromagnetic field simulations and other microwave diagnostics such as cross-polarization scattering.展开更多
We report a novel stimulated Raman scattering(SRS)microscopy technique featuring phase-controlled light focusing and aberration corrections for rapid,deep tissue 3D chemical imaging with subcellular resolution.To acco...We report a novel stimulated Raman scattering(SRS)microscopy technique featuring phase-controlled light focusing and aberration corrections for rapid,deep tissue 3D chemical imaging with subcellular resolution.To accomplish phasecontrolled SRS(PC-SRS),we utilize a single spatial light modulator to electronically tune the axial positioning of both the shortened-length Bessel pump and the focused Gaussian Stokes beams,enabling z-scanning-free optical sectioning in the sample.By incorporating Zernike polynomials into the phase patterns,we simultaneously correct the system aberrations at two separate wavelengths(~240 nm difference),achieving a~3-fold enhancement in signal-to-noise ratio over the uncorrected imaging system.PC-SRS provides>2-fold improvement in imaging depth in various samples(e.g.,polystyrene bead phantoms,porcine brain tissue)as well as achieves SRS 3D imaging speed of~13 Hz per volume for real-time monitoring of Brownian motion of polymer beads in water,superior to conventional point-scanning SRS 3D imaging.We further utilize PC-SRS to observe the metabolic activities of the entire tumor liver in living zebrafish in cellsilent region,unraveling the upregulated metabolism in liver tumor compared to normal liver.This work shows that PCSRS provides unprecedented insights into morpho-chemistry,metabolic and dynamic functioning of live cells and tissue in real-time at the subcellular level.展开更多
As a non-destructive testing technology,neutron imaging plays an important role in various fields,including material science,nuclear engineering,and fundamental science.An imaging detector with a neutron-sensitive ima...As a non-destructive testing technology,neutron imaging plays an important role in various fields,including material science,nuclear engineering,and fundamental science.An imaging detector with a neutron-sensitive image intensifier has been developed and demonstrated to achieve good spatial resolution and timing resolution.However,the influence of the working voltage on the performance of the neutron-sensitive imaging intensifier has not been studied.To optimize the performance of the neutron-sensitive image intensifier at different voltages,experiments have been performed at the China Spallation Neutron Source(CSNS)neutron beamline.The change in the light yield and imaging quality with different voltages has been acquired.It is shown that the image quality benefits from the high gain of the microchannel plate(MCP)and the high accelerating electric field between the MCP and the screen.Increasing the accelerating electric field is more effective than increasing the gain of MCPs for the improvement of the imaging quality.Increasing the total gain of the MCP stack can be realized more effectively by improving the gain of the standard MCP than that of the n MCP.These results offer a development direction for image intensifiers in the future.展开更多
The fast X-ray imaging beamline(BL16U2)at Shanghai Synchrotron Radiation Facility(SSRF)is a new beamline that provides X-ray micro-imaging capabilities across a wide range of time scales,spanning from 100 ps toμs and...The fast X-ray imaging beamline(BL16U2)at Shanghai Synchrotron Radiation Facility(SSRF)is a new beamline that provides X-ray micro-imaging capabilities across a wide range of time scales,spanning from 100 ps toμs and ms.This beamline has been specifically designed to facilitate the investigation of a wide range of rapid phenomena,such as the deformation and failure of materials subjected to intense dynamic loads.In addition,it enables the study of high-pressure and high-speed fuel spray processes in automotive engines.The light source of this beamline is a cryogenic permanent magnet undulator(CPMU)that is cooled by liquid nitrogen.This CPMU can generate X-ray photons within an energy range of 8.7-30 keV.The beamline offers two modes of operation:monochromatic beam mode with a liquid nitrogen-cooled double-crystal monochromator(DCM)and pink beam mode with the first crystal of the DCM out of the beam path.Four X-ray imaging methods were implemented in BL16U2:single-pulse ultrafast X-ray imaging,microsecond-resolved X-ray dynamic imaging,millisecond-resolved X-ray dynamic micro-CT,and high-resolution quantitative micro-CT.Furthermore,BL16U2 is equipped with various in situ impact loading systems,such as a split Hopkinson bar system,light gas gun,and fuel spray chamber.Following the completion of the final commissioning in 2021 and subsequent trial operations in 2022,the beamline has been officially available to users from 2023.展开更多
Single-pixel imaging(SPI)can transform 2D or 3D image data into 1D light signals,which offers promising prospects for image compression and transmission.However,during data communication these light signals in public ...Single-pixel imaging(SPI)can transform 2D or 3D image data into 1D light signals,which offers promising prospects for image compression and transmission.However,during data communication these light signals in public channels will easily draw the attention of eavesdroppers.Here,we introduce an efficient encryption method for SPI data transmission that uses the 3D Arnold transformation to directly disrupt 1D single-pixel light signals and utilizes the elliptic curve encryption algorithm for key transmission.This encryption scheme immediately employs Hadamard patterns to illuminate the scene and then utilizes the 3D Arnold transformation to permutate the 1D light signal of single-pixel detection.Then the transformation parameters serve as the secret key,while the security of key exchange is guaranteed by an elliptic curve-based key exchange mechanism.Compared with existing encryption schemes,both computer simulations and optical experiments have been conducted to demonstrate that the proposed technique not only enhances the security of encryption but also eliminates the need for complicated pattern scrambling rules.Additionally,this approach solves the problem of secure key transmission,thus ensuring the security of information and the quality of the decrypted images.展开更多
In a single-pixel fast imaging setup,the data collected by the single-pixel detector needs to be processed by a computer,but the speed of the latter will affect the image reconstruction time.Here we propose two kinds ...In a single-pixel fast imaging setup,the data collected by the single-pixel detector needs to be processed by a computer,but the speed of the latter will affect the image reconstruction time.Here we propose two kinds of setups which are able to transform non-visible into visible light imaging,wherein their computing process is replaced by a camera integration mode.The image captured by the camera has a low contrast,so here we present an algorithm that can realize a high quality image in near-infrared to visible cross-waveband imaging.The scheme is verified both by simulation and in actual experiments.The setups demonstrate the great potential for single-pixel imaging and high-speed cross-waveband imaging for future practical applications.展开更多
A two-stage training method is proposed to enhance imaging quality and reduce reconstruction time in datadriven single-pixel imaging(SPI)under undersampling conditions.This approach leverages a deep learning algorithm...A two-stage training method is proposed to enhance imaging quality and reduce reconstruction time in datadriven single-pixel imaging(SPI)under undersampling conditions.This approach leverages a deep learning algorithm to simulate single-pixel detection and image reconstruction.During the initial training stage,an L2 regularization constraint is imposed on convolution modulation patterns to determine the optimal initial network weights.In the subsequent stage,a coupled deep learning method integrating coded-aperture design and SPI is adopted,which utilizes backpropagation of the loss function to iteratively optimize both the binarized modulation patterns and imaging network parameters.By reducing the binarization errors introduced by the dithering algorithm,this approach improves the quality of data-driven SPI.Compared with traditional deep-learning SPI methods,the proposed method significantly reduces computational complexity,resulting in accelerated image reconstruction.Experimental and simulation results demonstrate the advantages of the method,including high imaging quality,short image reconstruction time,and simplified training.For an image size of 64×64 pixels and 10%sampling rate,the proposed method achieves a peak signal-to-noise ratio of 23.22 dB,structural similarity index of 0.76,and image reconstruction time of approximately 2.57×10^(−4) seconds.展开更多
Rheumatoid arthritis(RA)is a systemic autoimmune disease that is primarily manifested as synovitis and polyarticular opacity and typically leads to serious joint damage and irreversible disability,thus adversely affec...Rheumatoid arthritis(RA)is a systemic autoimmune disease that is primarily manifested as synovitis and polyarticular opacity and typically leads to serious joint damage and irreversible disability,thus adversely affecting locomotion ability and life quality.Consequently,good prognosis heavily relies on the early diagnosis and effective therapeutic monitoring of RA.Activatable fluorescent probes play vital roles in the detection and imaging of biomarkers for disease diagnosis and in vivo imaging.Herein,we review the fluorescent probes developed for the detection and imaging of RA biomarkers,namely reactive oxygen/nitrogen species(hypochlorous acid,peroxynitrite,hydroxyl radical,nitroxyl),pH,and cysteine,and address the related challenges and prospects to inspire the design of novel fluorescent probes and the improvement of their performance in RA studies.展开更多
基金supported by the National Natural Science Foundation of China(Nos.12105143 and 11975121)the China Postdoctoral Science Foundation(No.2023M741453)+1 种基金the Engineering Research Center of Nuclear Technology Application(No.HJSJYB2020-1)the Key Laboratory of Ionizing Radiation Metering and Safety Evaluation for Jiangsu Province Market Regulation,and the Jiangsu Province Excellent Postdoctoral Program(No.JB23057).
文摘Neutron-induced gamma-ray imaging is a spectroscopic technique that uses characteristic gamma rays to infer the elemental distribution of an object.Currently,this technique requires the use of large facilities to supply a high neutron flux and a time-consuming detection procedure involving direct collimating measurements.In this study,a new method based on low neutron flux was proposed.A single-pixel gamma-ray detector combined with random pattern gamma-ray masks was used to measure the characteristic gamma rays emitted from the sample.Images of the elemental distribution in the sample,comprising 30×30 pixels,were reconstructed using the maximum-likelihood expectation-maximization algorithm.The results demonstrate that the elemental imaging of the sample can be accurately determined using this method.The proposed approach,which eliminates the need for high neutron flux and scanning measurements,can be used for in-field imaging applications.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.U1532113,11475170,11905041)Anhui Provincial Natural Science Foundation(Grant No.2208085MA18)Fundamental Research Funds for the Central Universities(Grant No.JZ2022HGTB0244)。
文摘In x-ray dark-field imaging using dual phase grating interferometer,multi-contrast signals are extracted from a set of acquired phase-stepping data by using the least-squares fitting algorithm.The extracted mean intensity,amplitude and visibility signals may be intrinsically biased.However,it is still unclear how large these biases are and how the data acquisition parameters influence the biases in the extracted signals.This work set out to address these questions.Analytical expressions of the biases of the extracted signals were theoretically derived by using a second-order Taylor series expansion.Extensive numerical simulations were performed to validate the theoretical results.It is illustrated that while the estimated mean intensity signal is always unbiased,the estimated amplitude and visibility signals are both positively biased.While the biases of the estimated amplitude signals are proportional to the inverse of the total number of phase steps,the biases of the estimated visibility signals are inversely proportional to the product of the total number of phase steps and the mean number of photons counted per phase step.Meanwhile,it is demonstrated that the dependence of the biases on the mean visibility is quite different from that of Talbot-Lau interferometer due to the difference in the intensity model.We expect that these results can be useful for data acquisition optimizations and interpretation of x-ray dark-field images.
文摘Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.
基金supported by Key Research&Development Program of Jiangsu Province in China(BE2020693)Major Project of Science and Technology of Anhui Province(201903a06020010)+1 种基金Joint Key Project of Science and Technology Innovation of Yangtze River Delta in Anhui Province(202004g01020009)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)。
文摘Structured-illumination reflectance imaging(SIRI)provides a new means for food quality detection.This original work investigated the capability of(SIRI)technique coupled with multivariate chemometrics to evaluate the microbial contamination in pork inoculated with Pseudomonas fluorescens and Brochothrix thermosphacta during storage at different temperatures.The prediction performances based on different spectrum and the textural features of direct component and amplitude component images demodulated from the SIRI pattern,as well as their data fusion were comprehensively compared.Based on the full wavelength spectrum(420-700 nm)of amplitude component images,the orthogonal signal correction coupled with support vector machine regression provided the best predictions of the number of P.fluorescens and B.thermosphacta in pork,with the determination coefficients of prediction(R_(p)^(2))values of 0.870 and 0.906,respectively.Besides,the prediction models based on the amplitude component or direct component image textural features and the data fusion models using spectrum and textural features from direct component and amplitude component images cannot significantly improve their prediction accuracy.Consequently,SIRI can be further considered as a potential technique for the rapid evaluation of microbial contaminations in pork meat.
基金supported in part by the National Natural Science Foundation of China (No. U23B2011)。
文摘Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.
基金supported by Key Research&Development Program of Jiangsu Province in China(BE2020693)Major Project of Science and Technology of Anhui Province(201903a06020010)+1 种基金Joint Key Project of Science and Technology Innovation of Yangtze River Delta in Anhui Province(202004g01020009)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)。
文摘In this work,one-step growth models using hyperspectral imaging(HSI)(400-1000 nm)were successfully developed in order to estimate the microbial loads,minimum growth temperature(T_(min))and maximum specific growth rate(μ_(max))of Brochothrix thermosphacta in chilled beef at isothermal temperatures(4-25℃).Three different methods were compared for model development,particularly using(Model Ⅰ)the predicted microbial loads from partial least squares regression of the whole spectral variables;(Model Ⅱ)the selected spectral variables related to microbial loads;and(Model Ⅲ)the first principal scores of HSI spectra by principal component analysis.Consequently,Model Ⅰ showed the best ability to predict the microbial loads of B.thermosphacta,with the coefficient of determination(R_(v)^(2))and root mean square error in internal validation(RMSEV)of 0.921 and 0.498(lg(CFU/g)).The T_(min)(-12.32℃)andμmax can be well estimated with R^(2) and root mean square error(RMSE)of 0.971 and 0.276(lg(CFU/g)),respectively.The upward trend ofμmax with temperature was similar to that of the plate count method.HSI technique thus can be used as a simple method for one-step growth simulation of B.thermosphacta in chilled beef during storage.
基金Project supported by the National Natural Science Foundation of China (Grant No. 12174085)the Key Research and Development Project of Changzhou, Jiangsu Province, China (Grant No. CE20235054)the Postgraduate Research and Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX24 0833)。
文摘Ultrasonic Lamb waves undergo complex mode conversion and diffraction at non-penetrating defects, such as plate corrosion and cracks. Lamb wave imaging has a resolution limit due to the guided wave dispersion characteristics and Rayleigh criterion limitations. In this paper, a full convolutional network is designed to segment and reconstruct the received signals, enabling the automatic identification of target modalities. This approach eliminates clutter and mode conversion interference when calculating direct and accompanying acoustic fields in time-domain topological energy(TDTE) imaging.Subsequently, the measured accompanying acoustic field is reversed for adaptive focusing on defects and enhance the imaging quality. To circumvent the limitations of the Rayleigh criterion, the direct acoustic field and the accompanying acoustic field were fused to characterize the pixel distribution in the imaging region, achieving Lamb wave super-resolution imaging. Experimental results indicate that compared to the sign coherence factor-total focusing method(SCF-TFM),the proposed method achieves a 31.41% improvement in lateral resolution and a 29.53% increase in signal-to-noise ratio for single-blind-hole defects. In the case of multiple-blind-hole defects with spacings greater than the Rayleigh criterion resolution limit, it exhibits a 27.23% enhancement in signal-to-noise ratio. On the contrary, when the defect spacings are relatively smaller than the limit, this method has a higher resolution limit than SCF-TFM in super-resolution imaging.
基金supported by the National Natural Science Foundation of China(62122064,62331021,62371410)the Natural Science Foundation of Fujian Province of China(2023J02005 and 2021J011184)+1 种基金the President Fund of Xiamen University(20720220063)the Nanqiang Outstanding Talents Program of Xiamen University.
文摘Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large hospitals.This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure.Additionally,local data processing demands substantial manpower and hardware investments.Data isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and research.In this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain technology.This system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative work.The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)format.Then,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic analysis.Then,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services.The Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.
基金supported by the Instrument Developing Project of Magnetic Resonance Union of Chinese Academy of Sciences,Grant No.2022GZL002.
文摘Gradient coil is an essential component of a magnetic resonance imaging(MRI)scanner.To achieve high spatial resolution and imaging speed,a high-efficiency gradient coil with high slew rate is required.In consideration of the safety and comfort of the patient,the mechanical stability,acoustic noise and peripheral nerve stimulation(PNS)are also need to be concerned for practical use.In our previous work,a high-efficiency whole-body gradient coil set with a hybrid cylindrical-planar structure has been presented,which offers significantly improved coil performances.In this work,we propose to design this transverse gradient coil system with transformed magnetic gradient fields.By shifting up the zero point of gradient fields,the designed new Y-gradient coil could provide enhanced electromagnetic performances.With more uniform coil winding arrangement,the net torque of the new coil is significantly reduced and the generated sound pressure level(SPL)is lower at most tested frequency bands.On the other hand,the new transverse gradient coil designed with rotated magnetic gradient fields produces considerably reduced electric field in the human body,which is important for the use of rapid MR sequences.It's demonstrated that a safer and patient-friendly design could be obtained by using transformed magnetic gradient fields,which is critical for practical use.
基金Supported by the PetroChina Science and Technology Project(2021DJ4002,2022DJ3908)。
文摘Acoustic reflection imaging logging technology can detect and evaluate the development of reflection anomalies,such as fractures,caves and faults,within a range of tens of meters from the wellbore,greatly expanding the application scope of well logging technology.This article reviews the development history of the technology and focuses on introducing key methods,software,and on-site applications of acoustic reflection imaging logging technology.Based on the analyses of major challenges faced by existing technologies,and in conjunction with the practical production requirements of oilfields,the further development directions of acoustic reflection imaging logging are proposed.Following the current approach that utilizes the reflection coefficients,derived from the computation of acoustic slowness and density,to perform seismic inversion constrained by well logging,the next frontier is to directly establish the forward and inverse relationships between the downhole measured reflection waves and the surface seismic reflection waves.It is essential to advance research in imaging of fractures within shale reservoirs,the assessment of hydraulic fracturing effectiveness,the study of geosteering while drilling,and the innovation in instruments of acoustic reflection imaging logging technology.
基金support from the National Natural Science Foundation of China(Nos.62205259,62075175,61975254,62375212,62005203 and 62105254)the Open Research Fund of CAS Key Laboratory of Space Precision Measurement Technology(No.B022420004)the Fundamental Research Funds for the Central Universities(No.ZYTS23125).
文摘This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications.The widespread international research and activity in polarimetric imaging techniques demonstrate their broad applications and interest.Polarization information is increasingly incorporated into convolutional neural networks(CNN)as a supplemental feature of objects to improve performance in computer vision task applications.Polarimetric imaging and deep learning can extract abundant information to address various challenges.Therefore,this article briefly reviews recent developments in data-driven polarimetric imaging,including polarimetric descattering,3D imaging,reflection removal,target detection,and biomedical imaging.Furthermore,we synthetically analyze the input,datasets,and loss functions and list the existing datasets and loss functions with an evaluation of their advantages and disadvantages.We also highlight the significance of data-driven polarimetric imaging in future research and development.
基金supported by the following grants:National Natural Science Foundation of China(62125504,62361166631)STI 2030-Major Projects(2021ZD0200401)+1 种基金the Fundamental Research Funds for the Central Universities(226-2022-00201)the Open Project Program of Wuhan National Laboratory for Optoelectronics(2021WNLOKF007).
文摘Stimulated emission depletion microscopy(STED)holds great potential in biological science applications,especially in studying nanoscale subcellular structures.However,multi-color STED imaging in live-cell remains challenging due to the limited excitation wavelengths and large amount of laser radiation.Here,we develop a multiplexed live-cell STED method to observe more structures simultaneously with limited photo-bleaching and photo-cytotoxicity.By separating live-cell fluorescent probes with similar spectral properties using phasor analysis,our method enables five-color live-cell STED imaging and reveals long-term interactions between different subcellular structures.The results here provide an avenue for understanding the complex and delicate interactome of subcellular structures in live-cell.
基金supported by the National Magnetic Confinement Fusion Energy Program of China(No.2019YFE03020001)the Collaborative Innovation Program of Hefei Science Center,CAS(No.2021HSC-CIP010)the Fundamental Research Funds for the Central Universities。
文摘Interpreting experimental diagnostics data in tokamaks,while considering non-ideal effects,is challenging due to the complexity of plasmas.To address this challenge,a general synthetic diagnostics(GSD)platform has been established that facilitates microwave imaging reflectometry and electron cyclotron emission imaging.This platform utilizes plasma profiles as input and incorporates the finite-difference time domain,ray tracing and the radiative transfer equation to calculate the propagation of plasma spontaneous radiation and the external electromagnetic field in plasmas.Benchmark tests for classical cases have been conducted to verify the accuracy of every core module in the GSD platform.Finally,2D imaging of a typical electron temperature distribution is reproduced by this platform and the results are consistent with the given real experimental data.This platform also has the potential to be extended to 3D electromagnetic field simulations and other microwave diagnostics such as cross-polarization scattering.
基金supported by the Academic Research Fund(AcRF)from the Ministry of Education(MOE)(Tier 2(A-8000117-01-00)Tier 1(R397-000-334-114,R397-000-371-114,and R397-000-378-114)2024 Tsinghua-NUS Joint Research Initiative Fund,and the National Medical Research Council(NMRC)(A-0009502-01-00,and A-8001143-00-00),Singapore.
文摘We report a novel stimulated Raman scattering(SRS)microscopy technique featuring phase-controlled light focusing and aberration corrections for rapid,deep tissue 3D chemical imaging with subcellular resolution.To accomplish phasecontrolled SRS(PC-SRS),we utilize a single spatial light modulator to electronically tune the axial positioning of both the shortened-length Bessel pump and the focused Gaussian Stokes beams,enabling z-scanning-free optical sectioning in the sample.By incorporating Zernike polynomials into the phase patterns,we simultaneously correct the system aberrations at two separate wavelengths(~240 nm difference),achieving a~3-fold enhancement in signal-to-noise ratio over the uncorrected imaging system.PC-SRS provides>2-fold improvement in imaging depth in various samples(e.g.,polystyrene bead phantoms,porcine brain tissue)as well as achieves SRS 3D imaging speed of~13 Hz per volume for real-time monitoring of Brownian motion of polymer beads in water,superior to conventional point-scanning SRS 3D imaging.We further utilize PC-SRS to observe the metabolic activities of the entire tumor liver in living zebrafish in cellsilent region,unraveling the upregulated metabolism in liver tumor compared to normal liver.This work shows that PCSRS provides unprecedented insights into morpho-chemistry,metabolic and dynamic functioning of live cells and tissue in real-time at the subcellular level.
基金Project supported by the National Key R&D Program of China (Grant Nos.2023YFC2206502 and 2021YFA1600703)the National Natural Science Foundation of China (Grant Nos.12175254 and 12227810)the Guangdong–Hong Kong–Macao Joint Laboratory for Neutron Scattering Science and Technology。
文摘As a non-destructive testing technology,neutron imaging plays an important role in various fields,including material science,nuclear engineering,and fundamental science.An imaging detector with a neutron-sensitive image intensifier has been developed and demonstrated to achieve good spatial resolution and timing resolution.However,the influence of the working voltage on the performance of the neutron-sensitive imaging intensifier has not been studied.To optimize the performance of the neutron-sensitive image intensifier at different voltages,experiments have been performed at the China Spallation Neutron Source(CSNS)neutron beamline.The change in the light yield and imaging quality with different voltages has been acquired.It is shown that the image quality benefits from the high gain of the microchannel plate(MCP)and the high accelerating electric field between the MCP and the screen.Increasing the accelerating electric field is more effective than increasing the gain of MCPs for the improvement of the imaging quality.Increasing the total gain of the MCP stack can be realized more effectively by improving the gain of the standard MCP than that of the n MCP.These results offer a development direction for image intensifiers in the future.
基金supported by the CAS Project for Young Scientists in Basic Research(YSBR-096)the National Major Scientific Instruments and Equipment Development Project of China(No.11627901)+1 种基金the National Key Research and Development Program of China(Nos.2021YFF0701202,2021YFA1600703)the National Natural Science Foundation of China(Nos.U1932205,12275343).
文摘The fast X-ray imaging beamline(BL16U2)at Shanghai Synchrotron Radiation Facility(SSRF)is a new beamline that provides X-ray micro-imaging capabilities across a wide range of time scales,spanning from 100 ps toμs and ms.This beamline has been specifically designed to facilitate the investigation of a wide range of rapid phenomena,such as the deformation and failure of materials subjected to intense dynamic loads.In addition,it enables the study of high-pressure and high-speed fuel spray processes in automotive engines.The light source of this beamline is a cryogenic permanent magnet undulator(CPMU)that is cooled by liquid nitrogen.This CPMU can generate X-ray photons within an energy range of 8.7-30 keV.The beamline offers two modes of operation:monochromatic beam mode with a liquid nitrogen-cooled double-crystal monochromator(DCM)and pink beam mode with the first crystal of the DCM out of the beam path.Four X-ray imaging methods were implemented in BL16U2:single-pulse ultrafast X-ray imaging,microsecond-resolved X-ray dynamic imaging,millisecond-resolved X-ray dynamic micro-CT,and high-resolution quantitative micro-CT.Furthermore,BL16U2 is equipped with various in situ impact loading systems,such as a split Hopkinson bar system,light gas gun,and fuel spray chamber.Following the completion of the final commissioning in 2021 and subsequent trial operations in 2022,the beamline has been officially available to users from 2023.
基金Project supported by the National Natural Science Foundation of China(Grant No.62075241).
文摘Single-pixel imaging(SPI)can transform 2D or 3D image data into 1D light signals,which offers promising prospects for image compression and transmission.However,during data communication these light signals in public channels will easily draw the attention of eavesdroppers.Here,we introduce an efficient encryption method for SPI data transmission that uses the 3D Arnold transformation to directly disrupt 1D single-pixel light signals and utilizes the elliptic curve encryption algorithm for key transmission.This encryption scheme immediately employs Hadamard patterns to illuminate the scene and then utilizes the 3D Arnold transformation to permutate the 1D light signal of single-pixel detection.Then the transformation parameters serve as the secret key,while the security of key exchange is guaranteed by an elliptic curve-based key exchange mechanism.Compared with existing encryption schemes,both computer simulations and optical experiments have been conducted to demonstrate that the proposed technique not only enhances the security of encryption but also eliminates the need for complicated pattern scrambling rules.Additionally,this approach solves the problem of secure key transmission,thus ensuring the security of information and the quality of the decrypted images.
文摘In a single-pixel fast imaging setup,the data collected by the single-pixel detector needs to be processed by a computer,but the speed of the latter will affect the image reconstruction time.Here we propose two kinds of setups which are able to transform non-visible into visible light imaging,wherein their computing process is replaced by a camera integration mode.The image captured by the camera has a low contrast,so here we present an algorithm that can realize a high quality image in near-infrared to visible cross-waveband imaging.The scheme is verified both by simulation and in actual experiments.The setups demonstrate the great potential for single-pixel imaging and high-speed cross-waveband imaging for future practical applications.
文摘A two-stage training method is proposed to enhance imaging quality and reduce reconstruction time in datadriven single-pixel imaging(SPI)under undersampling conditions.This approach leverages a deep learning algorithm to simulate single-pixel detection and image reconstruction.During the initial training stage,an L2 regularization constraint is imposed on convolution modulation patterns to determine the optimal initial network weights.In the subsequent stage,a coupled deep learning method integrating coded-aperture design and SPI is adopted,which utilizes backpropagation of the loss function to iteratively optimize both the binarized modulation patterns and imaging network parameters.By reducing the binarization errors introduced by the dithering algorithm,this approach improves the quality of data-driven SPI.Compared with traditional deep-learning SPI methods,the proposed method significantly reduces computational complexity,resulting in accelerated image reconstruction.Experimental and simulation results demonstrate the advantages of the method,including high imaging quality,short image reconstruction time,and simplified training.For an image size of 64×64 pixels and 10%sampling rate,the proposed method achieves a peak signal-to-noise ratio of 23.22 dB,structural similarity index of 0.76,and image reconstruction time of approximately 2.57×10^(−4) seconds.
基金supported by the National Natural Science Foundation of China(82072432)the China-Japan Friendship Hospital Horizontal Project/Spontaneous Research Funding(2022-HX-JC-7)+1 种基金the National High Level Hospital Clinical Research Funding(2022-NHLHCRF-PY-20)the Elite Medical Professionals project of China-Japan Friendship Hospital(ZRJY2021-GG12).
文摘Rheumatoid arthritis(RA)is a systemic autoimmune disease that is primarily manifested as synovitis and polyarticular opacity and typically leads to serious joint damage and irreversible disability,thus adversely affecting locomotion ability and life quality.Consequently,good prognosis heavily relies on the early diagnosis and effective therapeutic monitoring of RA.Activatable fluorescent probes play vital roles in the detection and imaging of biomarkers for disease diagnosis and in vivo imaging.Herein,we review the fluorescent probes developed for the detection and imaging of RA biomarkers,namely reactive oxygen/nitrogen species(hypochlorous acid,peroxynitrite,hydroxyl radical,nitroxyl),pH,and cysteine,and address the related challenges and prospects to inspire the design of novel fluorescent probes and the improvement of their performance in RA studies.
