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Deep learning-driven computational imaging for light field microscopy 认领 引用 被引量：1
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作者 Wenshuang Liang Beibei Gao +2 位作者 Lu Gao Wei Ge Fu Wang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第4期47-62,共16页
Lightfield microscopy(LFM),with its snapshot-based three-dimensional imaging capability,has become a vital tool for observing dynamic living specimens.To overcome limitations in resolution and reconstruction speed inh... Lightfield microscopy(LFM),with its snapshot-based three-dimensional imaging capability,has become a vital tool for observing dynamic living specimens.To overcome limitations in resolution and reconstruction speed inherent in traditional algorithms,the application of deep learning to lightfield microscopic imaging has emerged as a key development direction.This review will introduce the basic theory and classical algorithms of LFM,survey the application of deep learning-based methods in computational image enhancement and analysis,and discuss the associated challenges and future research directions. 展开更多
关键词 Lightfield deep learning reconstruction algorithm computational imaging
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Artificial intelligence assisted light control and computational imaging through scattering media 认领 引用 被引量：17
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作者 Shengfu Cheng Huanhao Li +2 位作者 Yunqi Luo Yuanjin Zheng Puxiang Lai 《Journal of Innovative Optical Health Sciences》 SCIE EI 2019年第4期32-45,共14页
Coherent optical control within or through scattering media via wavefront shaping has seen broad applications since its invention around 2007.Wavefront shaping is aimed at overcoming the strong scattering,featured by ... Coherent optical control within or through scattering media via wavefront shaping has seen broad applications since its invention around 2007.Wavefront shaping is aimed at overcoming the strong scattering,featured by random interference,namely speckle patterns.This randomness occurs due to the refractive index inhomogeneity in complex media like biological tissue or the modal dispersion in multimode fiber,yet this randomness is actually deterministic and potentially can be time reversal or precompensated.Various wavefront shaping approaches,such as optical phase conjugation,iterative optimization,and transmission matrix measurement,have been developed to generate tight and intense optical delivery or high-resolution image of an optical object behind or within a scattering medium.The performance of these modula-tions,however,is far from satisfaction.Most recently,artifcial intelligence has brought new inspirations to this field,providing exciting hopes to tackle the challenges by mapping the input and output optical patterns and building a neuron network that inherently links them.In this paper,we survey the developments to date on this topic and briefly discuss our views on how to harness machine learning(deep learning in particular)for further advancements in the field. 展开更多
关键词 Optical scattering deep learning wavefront shaping adaptive optics computational imaging
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Multiuser computational imaging encryption and authentication with OFDM-assisted key management 认领 引用
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作者 Hongran Zeng Ping Lu +7 位作者 Xiaowei Li Lingling Huang Chaoyun Song Dahai Li In-kwon Lee Seok-Tae Kim Qiong-Hua Wang Yiguang Liu 《Advanced Photonics Nexus》 2024年第5期162-173,共12页
Single-pixel imaging(SPI)enables an invisible target to be imaged onto a photosensitive surface without a lens,emerging as a promising way for indirect optical encryption.However,due to its linear and broadcast imagin... Single-pixel imaging(SPI)enables an invisible target to be imaged onto a photosensitive surface without a lens,emerging as a promising way for indirect optical encryption.However,due to its linear and broadcast imaging principles,SPI encryption has been confined to a single-user framework for the long term.We propose a multi-image SPI encryption method and combine it with orthogonal frequency division multiplexing-assisted key management,to achieve a multiuser SPI encryption and authentication framework.Multiple images are first encrypted as a composite intensity sequence containing the plaintexts and authentication information,simultaneously generating different sets of keys for users.Then,the SPI keys for encryption and authentication are asymmetrically isolated into independent frequency carriers and encapsulated into a Malus metasurface,so as to establish an individually private and content-independent channel for each user.Users can receive different plaintexts privately and verify the authenticity,eliminating the broadcast transparency of SPI encryption.The improved linear security is also verified by simulating attacks.By the combination of direct key management and indirect image encryption,our work achieves the encryption and authentication functionality under a multiuser computational imaging framework,facilitating its application in optical communication,imaging,and security. 展开更多
关键词 computational imaging optical encryption optical authentication key management
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Computational imaging towards next-generation optical observational astronomy 认领 引用
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作者 Yuduo Guo Peixin Weng +4 位作者 Hao Zhang Chen Zhu Jun Zhang Jiamin Wu Qionghai Dai 《Photonics Insights》 2026年第1期172-191,共20页
Observational astronomy is the cornerstone of exploring the universe, transforming cosmic light into empirical knowledge. Historically, its capability has relied on increasing aperture diameters and integration time, ... Observational astronomy is the cornerstone of exploring the universe, transforming cosmic light into empirical knowledge. Historically, its capability has relied on increasing aperture diameters and integration time, yet the effective space–bandwidth product(SBP) remains fundamentally constrained by the physical limits of diffraction, atmospheric turbulence, and detection noise. In recent years, computational imaging(CI), encompassing techniques from interferometry to deep learning, has emerged as a transformative paradigm, integrating optical hardware and algorithms into a unified co-design system to transcend these rigid barriers. Here, we review the innovative developments in CI for optical astronomy, which achieve exceptional observational capabilities by treating the optical instrument as a physical encoder and the computational backend as a digital decoder. We systematically explore state-of-the-art CI strategies to overcome three fundamental limitations, including aperture size, seeing, and noise. By harnessing metaoptics, photon-counting detectors, advanced artificial intelligence, and other emerging technologies, CI is poised to shift the paradigm from “imaging for looking” to “sensing for understanding”, pushing forward the detection boundary for next-generation telescopes. 展开更多
关键词 computational imaging observational astronomy atmospheric turbulence digital adaptive optics AI for science
High-dimensional computational imaging using spectral-polarization encoding and deep learning 认领 引用
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作者 Yongkang Yan Zeqian Gan +9 位作者 Yihang Zheng Luying Hu Xinrui Xu Ran Kang Jianqiang Mei Paul Beckett William Shieh Rui Yin Xin He Xu Liu 《Chinese Optics Letters》 SCIE EI CAS CSCD 2026年第2期48-55,共8页
We present a high-dimensional computational imaging system integrating spatial,spectral,and polarization detection.It employs four high-dimensional encoders and a neural network to reconstruct 80 high-dimensional targ... We present a high-dimensional computational imaging system integrating spatial,spectral,and polarization detection.It employs four high-dimensional encoders and a neural network to reconstruct 80 high-dimensional target images.The system simultaneously reconstructed spectral and polarization data across 400–800 nm(20 nm intervals,20 bands) with four polarization angles per band(0°,45°,90°,-45°) at 1280 × 960 resolution,achieving a 1:20 reconstruction ratio.Experimental validation shows the spectral root mean squared error(RMSE) can reach 0.0088.This integrated approach overcomes limitations of single-function devices(e.g.,hyperspectral/polarization cameras),offering an efficient solution for advanced optical sensing. 展开更多
关键词 computational imaging hyperspectral imaging polarization imaging
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Revolutionizing optical imaging:computational imaging via deep learning 认领 引用 被引量：12
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作者 Xiyuan Luo Sen Wang +18 位作者 Jinpeng Liu Xue Dong Piao He Qingyu Yang Xi Chen Feiyan Zhou Tong Zhang Shijie Feng Pingli Han Zhiming Zhou Meng Xiang Jiaming Qian Haigang Ma Shun Zhou Linpeng Lu Chao Zuo Zihan Geng Yi Wei Fei Liua 《Photonics Insights》 2025年第2期1-105,共105页
The current state of traditional optoelectronic imaging technology is constrained by the inherent limitations of its hardware.These limitations pose significant challenges in acquiring higher-dimensional information a... The current state of traditional optoelectronic imaging technology is constrained by the inherent limitations of its hardware.These limitations pose significant challenges in acquiring higher-dimensional information and reconstructing accurate images,particularly in applications such as scattering imaging,superresolution,and complex scene reconstruction.However,the rapid development and widespread adoption of deep learning are reshaping the field of optical imaging through computational imaging technology.Datadriven computational imaging has ushered in a paradigm shift by leveraging the nonlinear expression and feature learning capabilities of neural networks.This approach transcends the limitations of conventional physical models,enabling the adaptive extraction of critical features directly from data.As a result,computational imaging overcomes the traditional“what you see is what you get”paradigm,paving the way for more compact optical system designs,broader information acquisition,and improved image reconstruction accuracy.These advancements have significantly enhanced the interpretation of highdimensional light-field information and the processing of complex images.This review presents a comprehensive analysis of the integration of deep learning and computational imaging,emphasizing its transformative potential in three core areas:computational optical system design,high-dimensional information interpretation,and image enhancement and processing.Additionally,this review addresses the challenges and future directions of this cutting-edge technology,providing novel insights into interdisciplinary imaging research. 展开更多
关键词 deep learning computational imaging optical system design high-dimensional information image processing
Deep learning for computational imaging:from data-driven to physics-enhanced approaches 认领 引用 被引量：2
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作者 Fei Wang Juergen W.Czarske Guohai Situ 《Advanced Photonics》 SCIE EI CAS CSCD 2025年第5期33-66,共34页
Computational imaging(CI)leverages the joint optimization of optical system design and reconstruction algorithms,enabling superior performance in terms of dimensionality,resolution,efficiency,and hardware complexity.I... Computational imaging(CI)leverages the joint optimization of optical system design and reconstruction algorithms,enabling superior performance in terms of dimensionality,resolution,efficiency,and hardware complexity.It has found widespread applications in medical diagnosis and astronomy,among others.Recently,deep learning(DL)has changed the paradigm of CI by harnessing learned priors from data through trained neural network models.However,widely used data-driven DL-based CI methods encounter difficulties related to training data acquisition,computation requirements,generalization,and interpretability.Recent studies have indicated that integrating the physics prior of the CI system into various components of DL pipelines(including training data,network design,and loss functions)holds promise for alleviating these challenges.To provide readers with a better understanding of the current research status and ideas,we present an overview of the state-of-the-art in DL-based CI.We begin by briefly introducing the concepts of CI and DL,followed by a comprehensive review of how DL addresses inverse problems in CI.Particularly,we focus on the emerging physics-enhanced approaches.We highlight the perspectives of future research directions and the transfer to real-world applications. 展开更多
关键词 computational imaging deep learning physics-enhanced approaches inverse problems
Super-field-of-view non-line-of-sight imaging via spatial encoding of a translated point spread function 认领 引用
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作者 TONGYAO LI YINGJIE SHI +5 位作者 JINYE MIAO YI WEI LINGFENG LIU LIANFA BAI ENLAI GUO JING HAN 《Photonics Research》 SCIE EI CAS CSCD 2026年第5期1959-1972,共14页
Non-line-of-sight(NLOS)imaging aims to reconstruct objects beyond line-of-sight view,offering potential applications in various fields.However,conventional transient NLOS methods are constrained by the detection regio... Non-line-of-sight(NLOS)imaging aims to reconstruct objects beyond line-of-sight view,offering potential applications in various fields.However,conventional transient NLOS methods are constrained by the detection region,preventing the reconstruction of targets outside its normal space and thereby limiting practical applicability.In this paper,a computational imaging method for super-field-of-view(Super-FoV)reconstruction based on spatial encoding of a translated point spread function(PSF)is proposed. 展开更多
关键词 computational imaging method reconstruct objects translated point spread function psf non line sight imaging spatial encoding super field view computational imaging translated point spread function
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Divide and conquer:parallel processing in computational imaging 认领 引用
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作者 David J.Brady 《Advanced Photonics》 SCIE EI CAS CSCD 2025年第5期3-4,共2页
SpeedShot,a dual camera high-speed imaging technology recently demonstrated by Zhang et al.,'demonstrates a 32×increase in effective frame rate by leveraging the motion gradient in frames captured by parallel... SpeedShot,a dual camera high-speed imaging technology recently demonstrated by Zhang et al.,'demonstrates a 32×increase in effective frame rate by leveraging the motion gradient in frames captured by parallel cameras.As interframe-motion estimation is the core element of most video compression algorithms,SpeedShot can be understood as a physical layer implementation of such an algorithm.Here,we seek to explain the context that makes SpeedShot interesting and to consider the roadmap for continuing improvements in camera information capacity. 展开更多
关键词 video compression algorithmsspeedshot Speedshot motion gradient parallel camerasas divide conquer high speed imaging computational imaging parallel processing
Breaking the speed-resolution trade-off in 3.3-km non-line-of-sight imaging using scanning-free laser reflective tomography 认领 引用
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作者 Zewei Wang Xiaoyin Li +6 位作者 Yinghui Guo Hengshuo Guo Peng Yang Fei Zhang Mingbo Pu Mingfeng Xu Xiangang Luo 《Opto-Electronic Science》 CAS 2026年第6期12-21,共10页
Non-line-of-sight(NLOS)imaging,which allows the recovery of hidden scenes outside the direct view,holds immense potential across numerous fields.However,conventional scanning-based NLOS imaging systems,face a fundamen... Non-line-of-sight(NLOS)imaging,which allows the recovery of hidden scenes outside the direct view,holds immense potential across numerous fields.However,conventional scanning-based NLOS imaging systems,face a fundamental trade-off between imaging speed and resolution due to their reliance on scanning relay surfaces,where dense sampling prolongs measurement.Here,we introduce a scanning-free NLOS imaging technique that adapts laser reflective tomography(LRT)to reconstruct hidden objects by exploiting the diffuse relay surface as a natural beam expander.Our method requires only single-point detection of third-bounce photons,effectively breaking through the resolution and speed limitations imposed by scanning.Compared with scanning-based approaches,it delivers a twofold enhancement in spatial resolution and a 91-fold improvement in imaging speed.Furthermore,we extend the advantages of this method to long-range experiments,demonstrating NLOS imaging over 3.3 km with a resolution of 3 cm in 3 minutes,establishing new benchmarks in imaging range,resolution,and speed for NLOS imaging. 展开更多
关键词 non-line-of-sight imaging laser reflective tomography computational imaging
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Radiolabeled exosomes for theranostics:Personalized tailored therapy through imaging 认领 引用
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作者 Soumya Deep Phadikar Aliza Hyder +4 位作者 Ramya Lakshmi Rajendran Anand Krishnan Chae Moon Hong Prakash Gangadaran Byeong-Cheol Ahn 《World Journal of Radiology》 2026年第7期33-45,共13页
Radiolabeled exosomes have emerged as a transformative platform at the intersection of nanomedicine,molecular imaging,and precision theranostics.These nanoscale extracellular vesicles exhibit intrinsic biocompatibilit... Radiolabeled exosomes have emerged as a transformative platform at the intersection of nanomedicine,molecular imaging,and precision theranostics.These nanoscale extracellular vesicles exhibit intrinsic biocompatibility,low immunogenicity,and inherent targeting capabilities,making them highly attractive for both diagnostic and therapeutic applications.The integration of radiochemistry with exosome biology enables noninvasive,real-time tracking of biodistribution,pharmacokinetics,and target engagement using advanced imaging modalities such as positron emission tomography and single-photon emission computed tomography.This minireview comprehensively summarizes current radiolabeling strategies for exosomes,including direct and indirect approaches,highlighting their advantages,limitations,and impact on vesicle integrity and imaging accuracy.Furthermore,we discuss key imaging platforms,in vivo biodistribution patterns,and pharmacokinetic profiles that influence therapeutic efficacy.Critical challenges such as rapid clearance by the mononuclear phagocyte system,labeling instability,and the lack of standardized protocols are also addressed.Finally,we outline future perspectives focusing on advanced bioengineering,multimodal imaging integration,and clinical translation frameworks.Radiolabeled exosomes represent a promising next-generation theranostic system with the potential to enable personalized,image-guided therapies across oncology and regenerative medicine. 展开更多
关键词 Radiolabeled exosomes Extracellular vesicles Theranostics Positron emission tomography imaging Singlephoton emission computed tomography imaging Biodistribution Pharmacokinetics Nanomedicine Drug delivery
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Metasurface-based computational imaging:a review 认领 引用 被引量：16
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作者 Xuemei Hu Weizhu Xu +4 位作者 Qingbin Fan Tao Yue Feng Yan Yanqing Lu Ting Xu 《Advanced Photonics》 SCIE EI CAS CSCD 2024年第1期43-63,共21页
Metasurface-based imaging has attracted considerable attention owing to its compactness,multifunctionality,and subwavelength coding capability.With the integration of computational imaging techniques,researchers have ... Metasurface-based imaging has attracted considerable attention owing to its compactness,multifunctionality,and subwavelength coding capability.With the integration of computational imaging techniques,researchers have actively explored the extended capabilities of metasurfaces,enabling a wide range of imaging methods.We present an overview of the recent progress in metasurface-based imaging techniques,focusing on the perspective of computational imaging.Specifically,we categorize and review existing metasurface-based imaging into three main groups,including(i)conventional metasurface design employing canonical methods,(ii)computation introduced independently in either the imaging process or postprocessing,and(iii)an end-to-end computation-optimized imaging system based upon metasurfaces.We highlight the advantages and challenges associated with each computational metasurface-based imaging technique and discuss the potential and future prospects of the computational boosted metaimager. 展开更多
关键词 metasurface computational imaging inverse problem algorithm
Computational imaging without a computer:seeing through random diffusers at the speed of light 认领 引用 被引量：71
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作者 Yi Luo Yifan Zhao +4 位作者 Jingxi Li Ege Çetintaş Yair Rivenson Mona Jarrahi Aydogan Ozcan 《eLight》 2022年第1期42-57,共16页
Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers.Here,we present a computer-free,all-optical image reconstruction method... Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers.Here,we present a computer-free,all-optical image reconstruction method to see through random diffusers at the speed of light.Using deep learning,a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown,random phase diffusers.After the training stage,which is a one-time effort,the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown,new phase diffuser.We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown,random diffusers,never used during training.Unlike digital methods,all-optical diffractive reconstructions do not require power except for the illumination light.This diffractive solution to see through diffusers can be extended to other wavelengths,and might fuel various applications in biomedical imaging,astronomy,atmospheric sciences,oceanography,security,robotics,autonomous vehicles,among many others. 展开更多
关键词 Imaging through diffusers Computational imaging Diffractive neural network Deep learning
Emerging theories and technologies on computational imaging 认领 引用 被引量：1
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作者 Xue-mei HU Jia-min WU +1 位作者 Jin-li SUO Qiong-hai DAI 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2017年第9期1207-1221,共15页
Computational imaging describes the whole imaging process from the perspective of light transport and information transmission, features traditional optical computing capabilities, and assists in breaking through the ... Computational imaging describes the whole imaging process from the perspective of light transport and information transmission, features traditional optical computing capabilities, and assists in breaking through the limitations of visual information recording. Progress in computational imaging promotes the development of diverse basic and applied disciplines. In this review, we provide an overview of the fundamental principles and methods in computational imaging, the history of this field, and the important roles that it plays in the development of science. We review the most recent and promising advances in computational imaging, from the perspective of different dimensions of visual signals, including spatial dimension, temporal dimension, angular dimension, spectral dimension, and phase. We also discuss some topics worth studying for future developments in computational imaging. 展开更多
关键词 Computational imaging Multi-scale and multi-dimensional Super-resolution Femto-photography 3D reconstruction Hyperspectral imaging
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Snapshot multispectral imaging through defocusing and a Fourier imager network 认领 引用
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作者 Xilin Yang Michael John Fanous +6 位作者 Hanlong Chen Ryan Lee Paloma Casteleiro Costa Yuhang Li Luzhe Huang Yijie Zhang Aydogan Ozcan 《Advanced Photonics Nexus》 CSCD 2025年第5期24-35,共12页
Multispectral imaging,which simultaneously captures the spatial and spectral information of a scene,is widely used across diverse fields,including remote sensing,biomedical imaging,and agricultural monitoring.We intro... Multispectral imaging,which simultaneously captures the spatial and spectral information of a scene,is widely used across diverse fields,including remote sensing,biomedical imaging,and agricultural monitoring.We introduce a snapshot multispectral imaging approach employing a standard monochrome image sensor with no additional spectral filters or customized components.Our system leverages the inherent chromatic aberration of wavelength-dependent defocusing as a natural source of physical encoding of multispectral information;this encoded image information is rapidly decoded via a deep learning-based multispectral Fourier imager network(mFIN).We experimentally tested our method with six illumination bands and demonstrated an overall accuracy of 98.25%for predicting the illumination channels at the input and achieved a robust multispectral image reconstruction on various test objects.This deep learning-powered framework achieves high-quality multispectral image reconstruction using snapshot image acquisition with a monochrome image sensor and could be useful for applications in biomedicine,industrial quality control,and agriculture,among others. 展开更多
关键词 computational imaging multispectral imaging deep learning image reconstruction Fourier imager network
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Computational Spectral Imaging Based on Compressive Sensing 认领 引用 被引量：2
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作者 Chao Wang Xue-Feng Liu +7 位作者 Wen-Kai Yu Xu-Ri Yao Fu Zheng Qian Dong Ruo-Ming Lan Zhi-Bin Sun Guang-Jie Zhai Qing Zhao 《Chinese Physics Letters》 SCIE EI CAS CSCD 2017年第10期44-48,共5页
Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing （CS）. The spectral and spatial i... Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing （CS）. The spectral and spatial infor- mation is simultaneously obtained using a fiber spectrometer and the spatial light modulation without mechanical scanning. The method allows high-speed, stable, and sub sampling acquisition of spectral data from specimens. The relationship between sampling rate and image quality is discussed and two CS algorithms are compared. 展开更多
关键词 Computational Spectral Imaging Based on Compressive Sensing DMD
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Computational ghost imaging with deep compressed sensing 认领 引用 被引量：2
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作者 Hao Zhang Yunjie Xia Deyang Duan 《Chinese Physics B》 SCIE EI CAS CSCD 2021年第12期455-458,共4页
Computational ghost imaging(CGI)provides an elegant framework for indirect imaging,but its application has been restricted by low imaging performance.Herein,we propose a novel approach that significantly improves the ... Computational ghost imaging(CGI)provides an elegant framework for indirect imaging,but its application has been restricted by low imaging performance.Herein,we propose a novel approach that significantly improves the imaging performance of CGI.In this scheme,we optimize the conventional CGI data processing algorithm by using a novel compressed sensing(CS)algorithm based on a deep convolution generative adversarial network(DCGAN).CS is used to process the data output by a conventional CGI device.The processed data are trained by a DCGAN to reconstruct the image.Qualitative and quantitative results show that this method significantly improves the quality of reconstructed images by jointly training a generator and the optimization process for reconstruction via meta-learning.Moreover,the background noise can be eliminated well by this method. 展开更多
关键词 computational ghost imaging compressed sensing deep convolution generative adversarial network
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Research on the model of high robustness computational optical imaging system 认领 引用
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作者 苏云 席特立 邵晓鹏 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第2期264-272,共9页
Computational optical imaging is an interdisciplinary subject integrating optics, mathematics, and information technology. It introduces information processing into optical imaging and combines it with intelligent com... Computational optical imaging is an interdisciplinary subject integrating optics, mathematics, and information technology. It introduces information processing into optical imaging and combines it with intelligent computing, subverting the imaging mechanism of traditional optical imaging which only relies on orderly information transmission. To meet the high-precision requirements of traditional optical imaging for optical processing and adjustment, as well as to solve its problems of being sensitive to gravity and temperature in use, we establish an optical imaging system model from the perspective of computational optical imaging and studies how to design and solve the imaging consistency problem of optical system under the influence of gravity, thermal effect, stress, and other external environment to build a high robustness optical system. The results show that the high robustness interval of the optical system exists and can effectively reduce the sensitivity of the optical system to the disturbance of each link, thus realizing the high robustness of optical imaging. 展开更多
关键词 computational optical imaging high robustness sensitivity
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Analysis of the clinical value of gemstone spectral computed tomography imaging in the preoperative assessment of colorectal cancer 认领 引用
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作者 Wei Liu De-Min Kong +1 位作者 Jian-Kun An Li-Tao Song 《World Journal of Gastrointestinal Surgery》 SCIE 2025年第8期169-178,共10页
BACKGROUND The diagnostic accuracy for detecting metastatic lymph nodes in colorectal cancer(CRC)remains suboptimal.To address this limitation,our study investigates the potential of gemstone spectral computed tomogra... BACKGROUND The diagnostic accuracy for detecting metastatic lymph nodes in colorectal cancer(CRC)remains suboptimal.To address this limitation,our study investigates the potential of gemstone spectral computed tomography imaging(GSI)to improve diagnostic accuracy in lymph node metastasis(LNM)assessment.AIM To extensively investigate the clinical utility of GSI in the preoperative assessment of CRC.METHODS The subject population included 200 patients with CRC who were admitted to Zibo Central Hospital from January 2022 to December 2023.All patients underwent dual-phase contrast-enhanced scans in the arterial and venous phases using GSI before surgical intervention.During the research,meticulous quantification was conducted regarding the number of patients with CRC with LNM as well as the exact count of metastatic lymph nodes.Moreover,for both metastatic and non-metastatic lymph nodes,the short diameter at the maximum crosssectional area(covering the axial,sagittal,and coronal planes),morphological features(including manifestations such as margin blurring,aggregation,and enhancement),and spectral parameters in the arterial and venous phases[specifically iodine concentration(IC),normalized IC(NIC),and the slope of the spectral curve(λHU)]were measured and recorded,and a comparative analysis was conducted.The diagnostic efficacy of each index with differences was systematically assessed using the receiver operating characteristic(ROC)curve.Concurrently,receiver operating characteristic curves were constructed for LNM screening based on the short diameter at the maximum cross-sectional area of lymph nodes and each spectral parameter in the arterial and venous phases.RESULTS The area under the curve of GSI for diagnosing LNM in patients with CRC can reach 0.897,with sensitivity,specificity,and accuracy of 92.59%,85.87%,and 89.50%,respectively.A total of 265 lymph nodes were analyzed from the 200 participants with CRC,with metastatic lymph nodes accounting for 56.60%.Compared with nonmetastatic lymph nodes,the short diameters of metastatic lymph nodes in the axial,sagittal,and coronal planes were significantly increased,whereas the IC values in the arterial and venous phases,the NIC value in the arterial phase,and theλHU values in the arterial and venous phases were significantly decreased.The short axial,sagittal,and coronal diameters,arterial-phase IC,venous-phase IC,arterial-phase NIC,arterial-phaseλHU,and venousphaseλHU for diagnosing metastatic lymph nodes demonstrated area under the curve values of 0.631,0.681,0.659,0.862,0.808,0.831,0.801,and 0.706,respectively.CONCLUSION GSI exhibits substantial clinical significance in the preoperative assessment of CRC.Among the parameters assessed,the arterial-phase IC demonstrates the most outstanding diagnostic performance,effectively improving the diagnostic efficacy for preoperative LNM in CRC. 展开更多
关键词 Gemstone spectral computed tomography imaging Colorectal cancer Preoperative assessment T staging N staging
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Optimization method of Hadamard coding plate inγ‑ray computational ghost imaging 认领 引用 被引量：4
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作者 Zhi Zhou San‑Gang Li +5 位作者 Qing‑Shan Tan Li Yang Ming‑Zhe Liu Ming Wang Lei Wang Yi Cheng 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第1期146-156,共11页
Owing to the constraints on the fabrication ofγ-ray coding plates with many pixels,few studies have been carried out onγ-ray computational ghost imaging.Thus,the development of coding plates with fewer pixels is ess... Owing to the constraints on the fabrication ofγ-ray coding plates with many pixels,few studies have been carried out onγ-ray computational ghost imaging.Thus,the development of coding plates with fewer pixels is essential to achieveγ-ray computational ghost imaging.Based on the regional similarity between Hadamard subcoding plates,this study presents an optimization method to reduce the number of pixels of Hadamard coding plates.First,a moving distance matrix was obtained to describe the regional similarity quantitatively.Second,based on the matrix,we used two ant colony optimization arrangement algorithms to maximize the reuse of pixels in the regional similarity area and obtain new compressed coding plates.With full sampling,these two algorithms improved the pixel utilization of the coding plate,and the compression ratio values were 54.2%and 58.9%,respectively.In addition,three undersampled sequences(the Harr,Russian dolls,and cake-cutting sequences)with different sampling rates were tested and discussed.With different sampling rates,our method reduced the number of pixels of all three sequences,especially for the Russian dolls and cake-cutting sequences.Therefore,our method can reduce the number of pixels,manufacturing cost,and difficulty of the coding plate,which is beneficial for the implementation and application ofγ-ray computational ghost imaging. 展开更多
关键词 γ-ray computational ghost imaging Regional similarity Hadamard coding plate
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