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European Conference on Antennas and Propagation, EuCAP

作者： Okan Yurduseven Jonah Gollub Hayrettin Odabasi Mohammadreza F. Imani Guy Lipworth Alec Rose Parker Trofetter David R. Smith Department of Electrical and Computer Engineering Duke University Durham North Carolina United States Evolv Technology Waltham Massachusetts United States Metamaterials Commercialization Center Intellectual Ventures Bellevue Washington United States

We investigate the performance of various image reconstruction algorithms for the design of a metamaterial aperture-based imaging system. The metamaterial imaging system consists of a transmitting metamaterial aperture panel and a receiving low-gain probe antenna. Imaging of a 3 cm resolution target is performed using a number of reconstruction algorithms, including pseudo-inverse, matched filter, conjugate gradient squares (CGS), and two-step iterative shrinkage/thresholding (TwIST) methods. The differing quality of the resulting reconstructed images is shown as a function of these reconstruction techniques.

关键词： Image reconstruction Metamaterials Image resolution Matched filters Signal to noise ratio reconstruction algorithms

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DARCS: Memory-Efficient Deep Compressed Sensing reconstruction for Acceleration of 3D Whole-Heart Coronary MR Angiography

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2025年第5期44卷 2105-2119页

作者： Xue, Zhihao Yang, Fan Gao, Juan Chen, Zhuo Peng, Hao Zou, Chao Jin, Hang Hu, Chenxi Shanghai Jiao Tong Univ Natl Engn Res Ctr Adv Magnet Resonance Technol Dia Sch Biomed Engn Shanghai 200240 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China Fudan Univ Zhongshan Hosp Dept Radiol Shanghai 200032 Peoples R China Shanghai Med Imaging Inst Shanghai 200032 Peoples R China

Three-dimensional coronary magnetic resonance angiography (CMRA) requires reconstruction algorithms that can significantly suppress the artifacts encountered in heavily undersampled acquisitions. While unrolling-based deep reconstruction methods have achieved state-of-the-art performance on 2D image reconstruction, their application in 3D reconstruction is hindered by the large amount of memory required to train an unrolled network. In this study, we propose a memory-efficient deep compressed sensing method that employs a sparsifying transform based on a pre-trained artifact estimation network. The artifact image estimated by a well-trained network is expected to be sparse when the input image is artifact-free and less sparse when the input image has artifacts. Thus, the artifact estimation network can be used as an inherent sparsifying transform. The proposed method, De-Aliasing Regularization-based Compressed Sensing (DARCS), was compared with a patch-based low-rank method, de-aliasing generative adversarial network (DAGAN), 3D model-based deep learning (MoDL), plug-and-play, and AI-assisted compressed sensing (AI-CS) in terms of 3D CMRA acceleration. The results demonstrate that DARCS surpasses the reconstruction quality of the comparison methods, by approximately 2 dB in peak signal-to-noise ratio (PSNR). Furthermore, the proposed method generalizes well to different undersampling rates, patterns, and noise levels, with a memory usage of only 63% of that needed by 3D MoDL. In conclusion, DARCS improves reconstruction quality for 3D CMRA with reduced memory burden.

关键词： Image reconstruction Transforms Three-dimensional displays Compressed sensing Deep learning Training reconstruction algorithms Adaptation models Noise reduction Magnetic resonance Coronary magnetic resonance angiography deep learning iterative reconstruction magnetic resonance image reconstruction

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Joint reconstruction algorithms for one-bit distributed compressed sensing

Joint reconstruction algorithms for one-bit distributed comp...

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International Conference on Telecommunications (ICT)

作者： Yun Tian Wenbo Xu Cong Zhang Yue Wang Hongwen Yang School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Research Department of Hisilicon Huawei Technologies Co. Ltd. Beijing China

Distributed compressed sensing (DCS), exploiting the correlation among multiple signals, enjoys the advantage of reduced number of measurements. This paper considers a type of joint sparsity model in DCS, where each signal contains a common component and an innovation component. In order to reduce the transmission cost, the measurements are derived as the sign information of the compressed samples by using one-bit quantization. We study such CS operation, and propose two joint reconstruction algorithms by iteratively deriving the sign information of each component. Simulation results show that the proposed algorithms can recover the signals efficiently.

关键词： Signal processing algorithms Compressed sensing Estimation Signal to noise ratio Joints reconstruction algorithms Technological innovation

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APG-Net: APG Optimization-Induced Multiscale Attention Network for MRI reconstruction From Undersampled k-Space Measurements

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Gan, Hongping Zhao, Zitong Chen, Yunong Shan, Shanshan Shi, Baoshun Liu, Chunyi Northwestern Polytech Univ Sch Software Xian 710072 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430072 Peoples R China Soochow Univ Sch Radiol & Interdisciplinary Sci State Key Lab Radiat Med & Protect Suzhou 215123 Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China

Compressed sensing magnetic resonance imaging (CS-MRI) can quickly reconstruct magnetic resonance (MR) images from undersampled k-space measurements, thereby accelerating MRI reconstruction. However, existing CS-MRI methods suffer from insufficient information interaction and an inability to adequately capture image features during the iterative reconstruction process, resulting in the reconstructed images having room for improvement in detail. Inspired by the accelerated proximal gradient (APG) algorithm, we propose an APG optimization-induced deep multiscale attention network, called APG-Net, for accelerating MRI reconstruction from k-space measurements. APG-Net transforms the traditional APG algorithm into a learnable network structure while introducing efficient feature extraction modules to effectively capture the features of the k-space data, resulting in high-quality MR image reconstruction. Specifically, it unrolls the iterative steps of the APG algorithm into a cascade block of fixed reconstruction phases, which include the iterative unfolding module (IUM) and multiscale dual-attention block (MDAB). The proposed IUM enhances feature fusion by leveraging information interaction between iterative phases. Moreover, the customized MDAB is utilized to extract multiscale feature information from the k-space measurements while enhancing attention to spatial and channel-related features in both dimensions, effectively boosting feature extraction capability. Extensive experiments demonstrate that our proposed APG-Net achieves remarkable performance on MRI reconstruction tasks in contrast to state-of-the-art methods. Our code is available at APG-Net.

关键词： Image reconstruction Feature extraction Magnetic resonance imaging Iterative algorithms Data mining Signal processing algorithms reconstruction algorithms Convergence Training Optimization Accelerated proximal gradient (APG) compressed sensing (CS) deep learning (DL) magnetic resonance (MR) imaging undersampled measurements

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A U-Residual Local Window Transformer for Ultrasound Images Super-Resolution reconstruction

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Yan, Yadi Liu, Yingying Qiao, Xiaoyang Wang, Diya Xu, Lei Su, Xiao Wan, Mingxi Xi An Jiao Tong Univ Minist Educ Sch Life Sci & Technol Key Lab Biomed Informat Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Life Sci & Technol Dept Biomed Engn Xian 710049 Peoples R China Xian Hosp Tradit Chinese Med Xian 710021 Peoples R China

Ultrasound images have inherent disadvantages, such as blurred details and high noise, and super-resolution (SR) reconstruction will improve the image quality and help for more accurate clinical diagnosis. In order to solve the problem that it is difficult to capture local feature information and lose important information in the process of downsampling, we propose an SR reconstruction method of ultrasound single image based on U-residual local window transformer (U-RLWT). Specifically, we construct a network encoder module that fuses two residual swim transformer blocks and a downsampling layer. It obtains more low-frequency information from ultrasound images by improving the receptive field of the feature extractor. Then, to combine the feature information between the layers and recover the feature map, the decoder module was constructed by using the residual swim transformer block and the upsampling layer. Finally, the reconstruction module was designed with convolution and Pixel-shuffle network to realize the SR reconstruction of ultrasound single images. Experimental results show that, compared with the advanced reconstruction methods, the proposed method can reconstruct the better results compared with the ground truth. When applied to larynx ultrasound images and PICMUS datasets, it achieves the highest peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) on the scale factors of x2 and x4 .

关键词： Image reconstruction Feature extraction Ultrasonic imaging Superresolution Transformers Decoding Data mining reconstruction algorithms Convolution Vectors Deep learning residual super-resolution (SR) texture features transformer ultrasound images

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Joint Edge Optimization Deep Unfolding Network for Accelerated MRI reconstruction

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2025年 11卷 11-23页

作者： Luo, Yu Cai, Yue Ling, Jie Ji, Yingdan Tie, Yanmei Yao, Shun Guangdong Univ Technol Sch Comp Sci Guangzhou 510006 Peoples R China Guangdong Univ Technol Sch Math & Stat Guangzhou 510520 Peoples R China Harvard Med Sch Brigham & Womens Hosp Dept Neurosurg Boston MA 02115 USA Sun Yat Sen Univ Affiliated Hosp 1 Dept Neurosurg Guangzhou 510080 Peoples R China

Magnetic Resonance Imaging (MRI) is a widely used imaging technique, however it has the limitation of long scanning time. Though previous model-based and learning-based MRI reconstruction methods have shown promising performance, most of them have not fully utilized the edge prior of MR images, and there is still much room for improvement. In this paper, we build a joint edge optimization model that not only incorporates individual regularizers specific to both the MR image and the edges, but also enforces a co-regularizer to effectively establish a stronger correlation between them. Specifically, the edge information is defined through a non-edge probability map to guide the image reconstruction during the optimization process. Meanwhile, the regularizers pertaining to images and edges are incorporated into a deep unfolding network to automatically learn their respective inherent a-priori information. Numerical experiments, consisting of multi-coil and single-coil MRI data with different sampling schemes at a variety of sampling factors, demonstrate that the proposed method outperforms other state-of-the-art methods.

关键词： Image edge detection Image reconstruction Magnetic resonance imaging Imaging Optimization Neural networks Adaptation models Iterative methods reconstruction algorithms Compressed sensing Accelerated MRI edge prior deep unfolding inverse problem parallel imaging

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Exploring Spatial Feature Regularization in Deep-Learning-Based TomoSAR reconstruction: A Preliminary Study and Performance Analysis

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Zeng, Tianjiao Zhan, Xu Ren, Yu Ma, Xiangdong Liu, Liang Shi, Jun Wei, Shunjun Wang, Mou Zhang, Xiaoling Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Tomographic synthetic aperture radar (TomoSAR) shows great potential for high-quality 3-D mapping, especially in urban areas. As TomoSAR reconstruction methods advance into the deep learning (DL) era, current studies have demonstrated DL's strengths in both precision and efficiency. However, for reconstructing urban areas with prominent spatial features from building structures, current studies focus on pixel-by-pixel reconstruction without leveraging the potential benefits of these features. In this context, an exploratory study to introduce spatial feature regularization in DL reconstruction is proposed for the first time, focusing on feature description, modeling, and regularization. Spatial features are analyzed and summarized by sharp edges and regular geometric shapes within the scene. To model these features, 2-D slices are used as the basic reconstruction units, and a general intraslice and interslice strategy is proposed to harness features within and between slices. Two-dimensional slices are fused into the entire 3-D scene. Two methods of fusion are designed: parallel and serial. To regularize these features, a new computational framework called light reconstruction and enhancement is designed, which includes two stages: light reconstruction with sparsity feature regularization and enhancement with spatial feature regularization. Finally, to evaluate performance, we design an extensive evaluation framework. A newly self-constructed compound urban building simulation dataset, combined with two public measured data, forms six different tests ranging from a classical close point resolution test to a diverse urban landscape challenge test. Evaluation results reveal the effectiveness of the designs and the boost provided by spatial feature regularization, resulting in higher reconstruction precision, more complete building spatial structure retrieval, and fewer outliers.

关键词： Image reconstruction Three-dimensional displays Tensors Buildings reconstruction algorithms Urban areas Discrete Fourier transforms Deep learning Vectors Spatial resolution Deep learning (DL) deep unfolding model-driven spatial regularization tomographic synthetic aperture radar (TomoSAR) reconstruction

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Representation Model for Electromagnetic Maps reconstruction via Sparse Nodes

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Liu, Gongxu Liu, Xu-An Huang, Lu Li, Long Gao, Xinbo Xidian Univ Sch Elect Engn Key Lab High Speed Circuit Design & EMC Minist Educ Xian 710071 Peoples R China Xidian Univ Microelect Sch Xian 710071 Peoples R China State Key Lab Satellite Nav Syst & Equipment Techn Shijiazhuang Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China

Electromagnetic (EM) maps hold promise for providing strategic support for future localization and navigation. However, reconstruction of EM maps under sparse nodes conditions is a critical challenge, which constrains the reliable and rapid construction of EM maps. To address this issue, we absorb the concept of Moran's index and use location index, fingerprint index, and scaling index to quantify the topology, quality, and quantity of EM nodes, respectively. Besides, three node selection criteria were used: one based on random selection, another using the K-means clustering, and the third involving affinity propagation (AP) clustering. Under each criterion, the overall EM maps were reconstructed using four representative interpolation methods: inverse distance weighting (IDW), modified Shepard's method (MSM), Kriging algorithm (KGA), and biharmonic spline interpolation (v4). Extensive experiments were conducted to verify the representation model. Experimental results indicate that the node selection criterion based on AP clustering outperforms the K-means criterion, which itself is superior to random selection criterion. This is attributed to the fact that under the same conditions, the AP clustering node selection criterion can better take the quality and the topological relationship of EM nodes into account. Another significant finding is that with good node selection criterion and reconstruction algorithms, only approximately 0.3%-0.5% of the nodes are required to achieve the reconstruction performance that would otherwise require up to 20% or more of the nodes. The above conclusions are of good guidance for the reliable and rapid reconstruction of EM maps via sparse or limited nodes.

关键词： Location awareness Interpolation Navigation Affinity propagation reconstruction algorithms Topology Indexes Reliability Electromagnetics Splines (mathematics) Affinity propagation (AP) clustering electromagnetic (EM) map interpolation method localization and navigation reconstruction sparse node

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Score-Based Generative Model With Conditional Null-Space Learning for Limited-Angle Tomographic reconstruction in Medical Imaging

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2025年 11卷 556-569页

作者： Zhang, Genyuan Wang, Zihao Yu, Haijun Ni, Song Xie, Haixia Liu, Qiegen Liu, Fenglin Wang, Shaoyu Chongqing Univ Key Lab Optoelect Technol & Syst Chongqing 400044 Peoples R China Chongqing Univ Engn Res Ctr Ind Computed Tomog Nondestruct Testin Minist Educ Chongqing 400044 Peoples R China Nanchang Univ Dept Informat Engn Nanchang 330031 Peoples R China

Limited-angle computed tomography (LA-CT) reconstruction represents a typically ill-posed inverse problem, frequently resulting in reconstructions with noticeable edge divergence and missing features. Score-based generative models (SGMs) based reconstruction methods have shown strong ability to reconstruct high-fidelity images for LA-CT. Data consistency is crucial for generating reliable and high-quality results in SGMs-based reconstruction methods. However, existing deep reconstruction methods have not fully explored data consistency, resulting in suboptimal performance. Based on this, we proposed a Conditional Score-based Null-space (CSN) generative model for LA-CT reconstruction. First, CSN integrates prior physical information of limited-angle scanning as conditional constraint, which can enable SGMs to obtain more accurate generation. Second, in order to balance the consistency and realness of the reconstruction results, the range-null space decomposition strategy is introduced in the sampling process. This strategy ensures that the estimation of the information occurs only in the null-space. Finally, we employ the sparse least square (LSQR) instead of commonly used consistency terms such as simultaneous iterative reconstruction technique (SIRT), thereby achieving superior reconstruction results. In addition, a mathematical convergence analysis of our CSN method is provided. Experimental evaluations on both numerical simulations and real-world datasets demonstrate that the proposed method offers notable advantages in reconstruction quality.

关键词： Image reconstruction reconstruction algorithms Computed tomography Training Noise reduction Mathematical models Computational modeling Accuracy Inverse problems Numerical models image reconstruction limited-angle score-based generative models null-space sparse least squares algorithm

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reconstruction of Fan Beam X-Ray Fluorescence Computed Tomography Based on Parallel Hole Collimator via Total Variation and Ordered Subsets

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IEEE ACCESS 2025年 13卷 7793-7800页

作者： Jiang, Shanghai Chen, Le Zhong, Jie Ai, Li Yang, Hua Lu, Hong Chongqing Univ Technol Cent Hosp Peoples Hosp Chongqing 7 Dept Radiol Chongqing 400054 Peoples R China Chongqing Tradit Chinese Med Hosp Dept Med Imaging Chongqing 400021 Peoples R China Chongqing Univ Technol Chongqing Key Lab Opt Fiber Sensor & Photoelect De Chongqing 400054 Peoples R China

In this paper, an Ordered Subsets Expectation Maximization (OSEM) reconstruction algorithm based on Total Variation (TV) constraint was applied for sparse reconstruction of X-ray fluorescence CT. First, the Geant4 Monte Carlo code was used to simulate the imaging process of fan beam X-ray fluorescence CT imaging system based on parallel hole collimator. Then, the reconstructed image quality of the proposed algorithm with varying numbers of projections was evaluated using RMSE and CNR. Finally, the relationship between the number of subsets of the algorithm and the quality of the reconstructed image and the reconstruction time was explored. The results demonstrated that, compared with the conventional OSEM algorithm, the proposed OSEM algorithm based on Total Variation constraint has higher quality of reconstructed images at different projection numbers, and the reconstruction time of the algorithm decreases with the increase of subset, which achieves the purpose of improving the quality of the reconstructed image and reducing the reconstruction time when sparse reconstruction.

关键词： Image reconstruction Fluorescence X-ray imaging Phantoms Computed tomography TV Photonics Fans Imaging reconstruction algorithms X-ray fluorescence CT image reconstruction Monte Carlo simulation sparse projection total variation

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