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International Conference on Advances in Computing and Communications (ICACC)

作者： Anju George Ajay Kumar Dept. of Electronics &Communication ASIET Kalady India

The MRI imaging is an efficient tool for disease diagnosis, and the features of the taken image has much importance. The reconstruction of such images needs much attention. The main limitation of MRI imaging is its long scan time. The time taken for acquisition can be reduced by non Cartesian acquisition. Spiral MRI is a non Cartesian MRI imaging and is less affected by motion and flow artifacts. Still, reconstruction of images from non-Cartesian MRI data will suffer from reconstruction errors and in-homogeneity artifacts. This will adversely affect the disease diagnosis. algorithms like conjugate phase method, Field map calculating Methods, iterative Next Neighbor re-gridding algorithms etc were proposed for solving such issues. Among these, iterative Next Neighbor re-gridding algorithm proved efficient. The main focus of this paper is on efficient reconstruction of spiral MRI image with an improved Iterative next neighbor algorithm which will result in comparatively better picture quality.

关键词： Projection on to convex sets reconstruction spiral MRI Magnetic Resonance Imaging C7orf49 gene images Disease diagnosis reconstructions image reconstruction Reconstructive Surgical Procedures Iterative Spiral scanning time Neighbor reconstruction algorithm Imagery (Psychotherapy)

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Unlabeled sensing: reconstruction algorithm and theoretical guarantees

Unlabeled sensing: Reconstruction algorithm and theoretical ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Golnoosh Elhami Adam Scholefield Benjamin Bejar Haro Martin Vetterli School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne (EPFL) CH-1015 Switzerland School of Computer and Communication Sciences ecole Polytechnique Federale de Lausanne (EPFL) CH-1015 Switzerland

ISBN: (纸本)9781509041183

It often happens that we are interested in reconstructing an unknown signal from partial measurements. Also, it is typically assumed that the location (temporal or spatial) of each sample is known and that the only distortion present in the observations is due to additive measurement noise. However, there are some applications where such location information is lost. In this paper, we consider the situation in which the order of noisy samples, taken from a linear measurement system, is missing. Previous work on this topic has only considered the noiseless case and exhaustive search combinatorial algorithms. We propose a much more efficient algorithm based on a geometrical viewpoint of the problem. We also study the uniqueness of the solution under different choices of the sampling matrix and its robustness to noise for the case of two-dimensional signals. Finally we provide simulation results to confirm the theoretical findings of the paper.

关键词： Unlabeled sensing Linear sampling Compressed sensing SLAM Compressed sensing supersonic low altitude missile reconstruction algorithm POSITION INFORMATION Noise robustness Noise measurement Linear measurement uniqueness

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Study on reconstruction algorithm of Two-Dimensional Temperature Field Based on Simulation of Sound Propagation Path

Study on Reconstruction Algorithm of Two-Dimensional Tempera...

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2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI'2009)

作者： Feng Tian Weijun Zhang Song Liu Changhui Zhang College of Computer Science Shenyang Institute of Aeronautical EngineeringShenyang 110134 Automatization College Shenyang Institute of Aeronautical EngineeringShenyang110134

ISBN: (纸本)9781424438631

The acoustic ray refraction on measuring temperature field of large-scale coal-fired power plant furnace is an important factor affects the accuracy of measuring the temperature field,If can simulate the propagation of the true path of the acoustic waves will further enhance the accuracy of the reconstruction of temperature field. This paper proposes a two-dimensional temperature field reconstruction algorithm simulates the sound propagation path based on the least-squares algorithm. This algorithm considers the bending effect of the sound propagation path,through the interpolation method to simulate the actual sound propagation path. The simulation result shows that the algorithm can corrected the curved sound path automatically and reconstructed the temperature field simultaneously. Compare with the least-squares algorithm,this algorithm can significantly improve measurement accuracy.

关键词： acoustic temperature measurement sound path simulation reconstruction algorithm sound wave refraction.

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Ultrasound reconstruction Tomography Using Neural Networks Trained with Simulated Data: A Case of Theoretical Gradient Damage in Concrete

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4273-4273页

作者： Gallardo-Llopis, Carles Gosalbez, Jorge Morell-Monzo, Sergio Vazquez, Santiago Font, Alba Paya, Jordi Univ Politecn Valencia Inst Telecommun & Multimedia Applicat iTEAM 8B BldgCamino VeraS-N Valencia 46022 Spain Univ Politecn Valencia IGIC Inst Invest Gest Integrada Zonas Costeras Paranimf 1 Gandia 46730 Spain Univ Politecn Valencia Inst Ciencia & Tecnol Hormigon GIQUIMA Grp ICITECH 4N Bldg Camino Vera S-N Valencia 46022 Spain

Gradient damage processes in cementitious materials are generally produced by chemical and/or physical processes that travel from outside to inside. Depending on the type of damage, it can cause different effects such as decreased porosity, cracking, or steel corrosion in the case of carbonation, or increased porosity, micro-cracks, expansion, and spalling (also present in thermal damage) in the case of external attack by sulphates or acid attack. Therefore, estimating the boundaries of this damage is an essential task for concrete quality assessment. The first objective of this work was to use neural networks (NNs) for ultrasound tomographic reconstruction of concrete samples in order to estimate the advance front in gradient damage. Unlike the usual X-ray tomography, ultrasound tomography is affected by diffraction, among other factors. NNs can learn to compensate for these effects;however, they require a large amount of training data to achieve accurate results. In the case of cement-based materials, obtaining and measuring a real training database could be complicated, expensive, and time-consuming. For this purpose, a training process using simulated measurements was carried out. The second objective of this work was to demonstrate the feasibility of training neural networks through simulations, which reduces costs. Finally, the trained neural network for tomographic reconstruction was evaluated using real cylindrical concrete specimens. Each specimen consisted of an outer cylinder, representing externally exposed cement, and an inner cylinder, simulating the unaffected core. The Structural Similarity Index (SSIM) was used as a metric to assess the reconstruction accuracy, achieving values of 0.95 for simulated signals and up to 0.82 for real signals.

关键词： ultrasound tomography reconstruction algorithm neural network NDT concrete damage

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A nonlocal prior in iterative CT reconstruction

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MEDICAL PHYSICS 2025年第3期52卷 1436-1453页

作者： Shu, Ziyu Entezari, Alireza SUNY Stony Brook Dept Radiat Oncol New York NY 11794 USA Univ Florida CISE Dept Gainesville FL 32611 USA

BackgroundComputed tomography (CT) reconstruction problems are always framed as inverse problems, where the attenuation map of an imaged object is reconstructed from the sinogram measurement. In practice, these inverse problems are often ill-posed, especially under few-view and limited-angle conditions, which makes accurate reconstruction challenging. Existing solutions use regularizations such as total variation to steer reconstruction algorithms to the most plausible result. However, most prevalent regularizations rely on the same priors, such as piecewise constant prior, hindering their ability to collaborate effectively and further boost reconstruction *** study aims to overcome the aforementioned challenge a prior previously limited to discrete tomography. This enables more accurate reconstructions when the proposed method is used in conjunction with most existing regularizations as they utilize different priors. The improvements will be demonstrated through experiments conducted under various *** by the discrete algebraic reconstruction technique (DART) algorithm for discrete tomography, we find out that pixel grayscale values in CT images are not uniformly distributed and are actually highly clustered. Such discovery can be utilized as a powerful prior for CT reconstruction. In this paper, we leverage the collaborative filtering technique to enable the collaboration of the proposed prior and most existing regularizations, significantly enhancing the reconstruction *** experiments show that the proposed method can work with most existing regularizations and significantly improve the reconstruction quality. Such improvement is most pronounced under limited-angle and few-view conditions. Furthermore, the proposed regularization also has the potential for further improvement and can be utilized in other image reconstruction *** propose improving the performance of iterative CT reconstruction

关键词： compressed sensing computed tomography few-view CT limited-angle CT low-dose CT reconstruction algorithm

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Unleashing the potency of multi-segmental DCIA flap in mandibular reconstruction with the aid of virtual surgery-A retrospective cohort study

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JOURNAL OF CRANIO-MAXILLOFACIAL SURGERY 2025年第5期53卷 484-490页

作者： Lin, Hao Deng, Weiwei Zhang, Linzhou Ge, Zhixiang Ma, Chunyue Wu, Tianfu Liu, Bing Wuhan Univ Sch & Hosp Stomatol Key Lab Oral Biomed Minist EducHubei Key Lab StomatolState Key Lab O Wuhan Peoples R China Wuhan Univ Sch & Hosp Stomatol Dept Oral & Maxillofacial Head Neck Oncol Wuhan Peoples R China Wuhan Joye Appl 3D Technol Co Ltd Wuhan Peoples R China Shanghai Jiao Tong Univ Peoples Hosp 9 Natl Ctr Stomatol Dept Oral & Maxillofacial Head & Neck OncolSch Me 639 Zhi Zao Ju Rd Shanghai 200011 Peoples R China

Although the deep circumflex iliac artery (DCIA) flap is a mainstay in mandibular reconstruction, its multisegmental utilization is infrequently reported, primarily due to concerns regarding the variable cutaneous component and potentially inadequate vascular supply to multi-block segments. This retrospective study analyzed the outcomes of 86 patients undergoing mandibular reconstruction with multi-segmental DCIA flaps, compared to 167 patients who received conventional single-segmental flaps. The survival rate for multisegmental flaps was comparable to that of single-segmental flaps (100% vs. 98.2%, p = 0.553). Multisegmental flaps were used to reconstruct longer bony defects (median: 9 cm vs. 6.5 cm, p < 0.001). Despite the longer operation times in the multi-segmental group (368.8 vs. 326.8 min, p < 0.001), there was no significant increase in the incidence of surgical complications. Furthermore, postoperative QoL and Harris Hip scores did not show significant differences, despite the increased graft lengths and segmentation in the multisegmental group. The study underscores the underrecognized efficacy of multi-segmental DCIA flaps in complex mandibular reconstructions, particularly for specific types of defects such as Brown III, Brown I (Ic), and Brown II (IIc), which can be effectively and reliably reconstructed with the aid of virtual surgical techniques. This research also confirms that concerns regarding soft tissue management with multi-segmental DCIA flaps can be effectively addressed.

关键词： Mandibular reconstruction DCIA flap Multi-segmental design reconstruction algorithm

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Shape measurement using a multicore optical fiber sensor with asymmetric dual cores

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第1期36卷

作者： Wang, Jianjian Tang, Rui Shang, Qiufeng North China Elect Power Univ Dept Elect & Commun Engn Baoding Peoples R China North China Elect Power Univ Hebei Key Lab Power Internet Things Technol Baoding Peoples R China

Shape measurement using multicore optical fiber sensors has attracted more attention in many fields due to the good consistency of the fiber cores. Three symmetrically arranged cores in a multicore fiber are usually used to reconstruct shapes by calculating the bending vectors, which will not be achieved when one of the cores is damaged or occupied in actual application. A shape measurement method using a multicore optical fiber sensor with asymmetric dual cores is proposed in this paper. Based on the analysis of the principle of shape reconstruction and the geometric relationship of the asymmetric dual cores in the multicore fiber sensor, the bending vector is decomposed. The mathematical expressions for the bending curvature and orientation of the asymmetric dual cores are derived. The two-dimensional (2D) and three-dimensional (3D) shape reconstruction results in both finite element modeling simulation and shape measurement experiments based on optical frequency domain reflectometry have shown that the proposed method using a multicore fiber sensor with asymmetric dual cores is able to achieve shape measurement;its performance is comparable and even equivalent to the traditional method that uses three symmetrically arranged cores. In the experiment, the maximum relative errors of 2D and 3D reconstructed shapes are 2.653% and 5.139%, respectively. The proposed method, which only needs asymmetric dual cores in the multicore optical fiber sensor for shape reconstruction, will be conducive to solving the limitations of multicore optical fiber sensors in shape measurement applications.

关键词： asymmetric dual cores multicore optical fiber reconstruction algorithm shape measurement

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Enhancing gas-liquid two-phase stratified flow imaging with EIGEN-NET: a fusion of deep learning and measurement matrices

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第3期36卷 035903-035903页

作者： Yang, Xuanxuan Chen, Haofeng Xu, Gengchen Ma, Gang Wang, Xiaojie Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Peoples R China Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei 230026 Anhui Peoples R China

Gas-liquid two-phase stratified flow is a common occurrence in various industries, demanding real-time monitoring and precise measurement for safety and operational efficiency. Current methods primarily rely on sensors or limited imaging technologies, leaving room for improvement. In this paper, we introduce the EIGEN-Net framework, specifically designed for real-time monitoring of gas-liquid stratified flow within pipelines, utilizing electrical impedance tomography (EIT). EIT, a noninvasive imaging method, shows potential despite challenges from gas affecting voltage stability in image reconstruction. EIGEN-Net capitalizes on measurement matrices and deep learning to enhance measurement accuracy. It extracts crucial physical properties characterizing liquid fractions and employs acquired voltage data to pre-reconstruct a tensor. By fusing these tensors with prior information, it significantly improves imaging outcomes, effectively addressing data insufficiency issues. The incorporation of adaptive information into deep learning networks eliminates the necessity for data classification. Moreover, this approach accommodates scenarios of continuous flow variation, enhancing imaging resolution. EIGEN-Net's versatility extends to other two-phase flow scenarios and sensor measurement matrices, holding promise for applications in various industrial inspection ***-Net:Deep Learning Fusion Network Guided by Leading Eigenvalues of Measurement Matrix

关键词： electrical impedance tomography deep learning reconstruction algorithm two-phase stratified flow

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Orthogonal Wavefield Synthesis for Nonnegative Photoacoustic Imaging

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IEEE SENSORS JOURNAL 2025年第7期25卷 11665-11671页

作者： Chen, Liang Ren, Peng Luo, Fengchen Liu, Siyu Nanjing Univ Sci & Technol Sch Phys Nanjing 210094 Peoples R China Southwest Inst Tech Phys Chengdu 610041 Sichuan Peoples R China

Photoacoustic tomography (PAT) has developed rapidly in recent years and holds great potential for applications in the biomedical field. In PAT, the quality of the final reconstructed image mainly depends on the choice of reconstruction algorithm. However, traditional filtered backprojection algorithms may introduce negative artifacts into the final reconstructed images due to the finite bandwidth of ultrasound transducers. To address the issue of negative artifacts, we propose a photoacoustic imaging method based on the virtual orthogonal wave field. This method converts the original photoacoustic signals into pairs of virtual orthogonal signals, constructs the real and imaginary parts of the photoacoustic complex signal from them, and extracts the virtual photoacoustic envelope, thereby obtaining a reconstruction image without negative artifacts. In this work, we propose the theory of the virtual orthogonal wave field. By combining the point spread function (PSF) based on spherical harmonics with the theory of the virtual orthogonal wave field, the PSF of the virtual orthogonal wave field method is derived, we explain the theoretical basis for nonnegative imaging using this method. The result of simulations and experiments demonstrates that this method can effectively eliminate negative artifacts and improve image fidelity.

关键词： Image reconstruction Imaging Transducers Training Data mining Artificial intelligence Intelligent sensors Bandwidth Recording Photoacoustic imaging Negative artifacts photoacoustic tomography reconstruction algorithm

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Artifact reduction in rotational computed laminography using a deep learning method

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OPTICS AND LASERS IN ENGINEERING 2025年 187卷

作者： Zou, Xiang Shi, Wuliang Du, Muge Xing, Yuxiang Tsinghua Univ Dept Engn Phys Beijing 100084 Peoples R China Tsinghua Univ Key Lab Particle & Radiat Imaging Minist Educ Beijing 100084 Peoples R China

Computed laminography (CL) is widely used in imaging plate-like object. Due to lacking projection data along non-thickness direction, images reconstructed from CL contain severe interlayer aliasing artifacts. These artifacts can greatly affect later object identification and information extraction from the CL images, restricting their utility value. To reduce aliasing artifact in CL, we develop a deep learning method to post-process the FDKreconstructed image. Firstly, we analyze the characteristics of aliasing artifacts in CL images. Based on that, a modified U-Net convolutional neural network (CNN), which takes 2.5D radial slice as input, is proposed. Then, the effectiveness of the proposed method is tested and compared with other strategies, including the methods using 2D (z direction, x direction, and radical direction) slice and 2.5D (z direction, x direction) slice as input. Experimental results on ball grid array (BGA) specimens shows that the proposed method give the best performance in the CL aliasing artifact reduction in all comparison strategies.

关键词： Computed laminography reconstruction algorithm Artifact Deep learning FDK

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