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Image Reconstruction - Science topic

Reconstruction of CT image from analytical, iterative and statistical algorithms.
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Publications related to Image Reconstruction (10,000)
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Magnetic Resonance Imaging (MRI) is a powerful non-invasive procedure for imaging that offers critical functional, structural, anatomical details about a patient. However, the maximum time needed to scan the whole process causes motion artifacts that may worsen the image quality and result in data distortion and patient discomfort. Hence, an effect...
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Electromagnetic tomography (EMT) is an emerging imaging modality capable of visualizing the distribution of electrically conductive or magnetically permeable materials within the vessels and pipelines. Image reconstruction is the crucial step of EMT inverse problem, which is one of the main challenges in the promotion and application of EMT to indu...
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This work aims to numerically investigate the performance of the multiquadric (MQ) radial basis function in more general formats for image reconstruction applications. Desired features, i.e., accuracy and shape parameter sensitivity, of each form is numerically compared and explored. The famous Lena image is damaged using two levels of damage: 20%...
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Background: To assess the performance of prospectively accelerated and deep learning (DL) reconstructed T2-weighted (T2w) imaging in volunteers and patients with histologically proven prostate cancer (PCa). Methods: Prospectively undersampled T2w datasets were acquired with acceleration factors of 1.7 (reference), 3.4 and 4.8 in 10 healthy voluntee...
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The introduction of the first whole-body CT scanner in 1974 marked the beginning of cross-sectional spine imaging. In the last decades, the technological advancement, increasing availability and clinical success of CT led to a rapidly growing number of CT examinations, also of the spine. After initially being primarily used for trauma evaluation, n...
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Image reconstruction enhanced by regularizers, e.g., to enforce sparsity, low rank or smoothness priors on images, has many successful applications in vision tasks such as computer photography, biomedical and spectral imaging. It has been well accepted that non-convex regularizers normally perform better than convex ones in terms of the reconstruct...
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We propose a new variational model in Sobolev–Orlicz spaces with non-standard growth conditions of the objective functional and discuss its applications to image processing. The characteristic feature of the proposed model is that the variable exponent, which is associated with non-standard growth, is unknown a priori and it depends on a particular...
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X-ray computed tomography is a versatile technique for 3D structure characterization. However, conventional reconstruction algorithms require that the sample not change throughout the scan, and the timescale of sample dynamics must be longer than the data acquisition time to fulfill the stable sample requirement. Meanwhile, concerns about X-ray-ind...
Preprint
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Advancements in radiation therapy technologies are characterized by personalized treatments plans and increased conformality of the radiation dose to the tumour. Gel dosimeters are a potential tool for measuring these complex dose distributions. Here we develop a method to reduce the storage size of optical CT system matrices through use of polar c...
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Iterative image reconstruction algorithms have considerable advantages over transform methods for computed tomography, but they each have their own drawbacks. In particular, the maximum-likelihood expectation-maximization (MLEM) algorithm reconstructs high-quality images even with noisy projection data, but it is slow. On the other hand, the simult...
Article
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Disruptive phenomena are known for damaging the original appearance of artworks, even when the pieces are carefully and meticulously put together. Machine Learning and Computer vision techniques may be effective support tools during the works in an excavation site to resolve the issues of reconstructing frescoes, painting or sculptures. Not infrequ...
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Now a day's transmission of image and video data is gradually increasing. Compression of image data with acceptable image quality is the objective of this paper. To achieve higher Compression Ratio combination of halftone and Kekre's Fast Codebook Generation (KFCG) Vector Quantization algorithm is used. For Vector Quantization KFCG algorithm is use...
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Reaching out the function of the brain in perceiving input data from the outside world is one of the great targets of neuroscience. Neural decoding helps us to model the connection between brain activities and the visual stimulation. The reconstruction of images from brain activity can be achieved through this modelling. Recent studies have shown t...
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Cell division, or mitosis, guarantees the accurate inheritance of the genomic information kept in the cell nucleus. Malfunctions in this process cause a threat to the health and life of the organism, including cancer and other manifold diseases. It is therefore crucial to study in detail the cell-cycle in general and mitosis in particular. Conseque...
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This paper presents a computationally simple diagnostic algorithm for breast cancer using a non-invasive Digital Image Elasto Tomography (DIET) system. N=14 women (28 breasts, 13 cancerous) underwent a clinical trial using the DIET system following mammography diagnosis. The screening involves steady state sinusoidal vibrations applied to the free...
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A trained-based Born iterative method (TBIM) is developed for electromagnetic imaging (EMI) applications. The proposed TBIM consists of a nested loop; the outer loop executes TBIM iteration steps, while the inner loop executes a trained iterative shrinkage thresholding algorithm (TISTA). The applied TISTA runs linear Landweber iterations implemente...
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The increasing need of restoring high-resolution Hyper-Spectral (HS) images is determining a growing reliance on Computer Vision-based processing to enhance the clarity of the image content. HS images can, in fact, suffer from degradation effects or artefacts caused by instrument limitations. This paper focuses on a procedure aimed at reducing the...
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A systematic approach for understanding how the urban environment and regeneration influence neighborhood image is lacking, particularly involving aesthetic perspective. This study considers image reform under cultural regeneration and quantifies neighborhood image based on its aesthetic characteristics. Two Shanghai conservation areas (SCAs), also...
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Due to its high resolution and deep penetration depth, microwave ultrawideband radar is a promising tool for the non-destructive testing (NDT) of transportation infrastructure. Microwave radiation can also be used to reconstruct the dielectric properties of objects and therefore can be used to detect an air cavity or metallic rust in concrete. We u...
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Cloud removal is often required to construct time-series sets of optical images for environmental monitoring. In regression-based cloud removal, the selection of an appropriate regression model and the impact analysis of the input images significantly affect the prediction performance. This study evaluates the potential of Gaussian process (GP) reg...
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The traditional greedy algorithm requires the signal sparsity as a known condition, and in most application scenarios, the signal sparsity is unknown, resulting in poor signal reconstruction accuracy. In order to solve such problems, this paper proposes an adaptive dual threshold matching pursuit algorithm based on a variable-step backtracking stra...
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Time-of-flight diffraction (TOFD) has become a widely used nondestructive testing (NDT) technique, owing to its wide coverage, fast detection speeds, and high defect detection rates. However, compared with nondestructive radiographic testing images, TOFD image analysis requires more technicians and more difficult defect analysis. Owing to the impro...
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X-ray computed tomography (CT) is an invaluable imaging technique for non-invasive medical diagnosis. However, for soft tissue in the human body the difference in attenuation is inherently small. Grating-based X-ray phase-contrast is a relatively novel imaging method which detects additional interaction mechanisms between photons and matter, namely...
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Image contrast in multispectral optoacoustic tomography (MSOT) can be severely reduced by electrical noise and interference in the acquired optoacoustic signals. Previously employed signal processing techniques have proven insufficient to remove the effects of electrical noise because they typically rely on simplified models and fail to capture com...
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Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice sequences where anatomical information in different slices may be subject to different deformations, contrast variations or artifacts. Volumetric reconstruction formulatio...
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Image reconstruction of low-count positron emission tomography (PET) data is challenging. Kernel methods address the challenge by incorporating image prior information in the forward model of iterative PET image reconstruction. The kernelized expectation-maximization (KEM) algorithm has been developed and demonstrated to be effective and easy to im...
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In Single-Photon Emission Computed Tomography (SPECT), the reconstructed image has insufficient contrast, poor resolution and inaccurate volume of the tumor size due to physical degradation factors. Generally, nonsta-tionary filtering of the projection or the slice is one of the strategies for correcting the resolution and therefore improving the q...
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Ultrasound is one of the most important imaging modalities in medical practice. It is the most technique development with a lot benefits and with little challenges that include low imaging quality and high variability. Medical field is the most application that exploit the ultrasonic technique widely in body imaging, especially the real time Featur...
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Measuring the radiation dose reaching a patient’s body is difficult. Here we report a technique for the tomographic reconstruction of the location of photon pairs originating from the annihilation of positron–electron pairs produced by high-energy X-rays travelling through tissue. We used Monte Carlo simulations on pre-recorded data from tissue-mim...
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Recent deep learning is superior in providing high-quality images and ultra-fast reconstructions in accelerated magnetic resonance imaging (MRI). Faithful coil sensitivity estimation is vital for MRI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccuracy, resulting in the signifi...
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The three-dimensional characterization of internal features, via metrics such as orientation, porosity, and connectivity, is important to a wide variety of scientific questions. Many spatial and morphological metrics only can be measured accurately through direct in situ three-dimensional observations of large (i.e., big enough to be statistically...
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This study aimed to evaluate the value of the deep learning image reconstruction (DLIR) algorithm (GE Healthcare’s TrueFidelity™) in improving the image quality of low-dose computed tomography (LDCT) of the chest. First, we retrospectively extracted raw data of chest LDCT from 50 patients and reconstructed them by using model-based adaptive statist...
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In recent years, it has become increasingly popular to solve inverse problems of varioustomography methods with deep learning techniques. Here, a deep residual neural network (ResNet) isintroduced to reconstruct the conductivity distribution of a biomedical, voluminous body in magneticinduction tomography (MIT). MIT is a relatively new, contactless...
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Background: Deep learning image reconstruction (DLIR) improves image quality. We aimed to compare the measured diameter of pulmonary lesions and lymph nodes between DLIR-based ultra-low-dose CT (ULDCT) and contrast-enhanced CT. Methods: The consecutive adult patients with noncontrast chest ULDCT (0.07-0.14 mSv) and contrast-enhanced CT (2.38 mSv...
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Deep learning (DL) image quality improvement has been studied for application to 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). It is unclear, however, whether DL can increase the quality of images obtained with semiconductor-based PET/CT scanners. This study aimed to compare the quality of semiconductor-b...
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Human-robot collaborative disassembly (HRCD) has gained much interest in the disassembly tasks of end-of-life products, integrating both robot’s high efficiency in repetitive works and human’s flexibility with higher cognition. Explicit human-object perceptions are significant but remain little reported in the literature for adaptive robot decision...
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The detection of chemiluminescence from various radicals and molecules in a hydrocarbon flame can provide valuable information on the rate of local heat release, combustion stability, and combustion completeness. In this study, chemiluminescence from the combustion process is detected using a high-speed color camera within the broadband spectrum of...
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Neurogenic intestinal care is currently a major research topic in the medical field. In order to improve the accuracy of neurogenic intestinal care and simplify the process of neurogenic intestinal care, this paper adopts a three-dimensional analysis theory to extract the key indicators in neurogenic intestinal care. Through the analysis and calcul...
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In the structure from motion, the viewing graph is a graph where the vertices correspond to cameras (or images) and the edges represent the fundamental matrices. We provide a new formulation and an algorithm for determining whether a viewing graph is solvable, i.e., uniquely determines a set of projective cameras. The known theoretical conditions e...
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We show three-dimensional reconstructions of a region of an integrated circuit from a 130 nm copper process. The reconstructions employ x-ray computed tomography, measured with a new and innovative high-magnification x-ray microscope. The instrument uses a focused electron beam to generate x rays in a 100 nm spot and energy-resolving x-ray detector...
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A new image reconstruction (IR) algorithm from multiscale interest points in the discrete wavelet transform (DWT) domain was proposed based on a modified conditional generative adversarial network (CGAN). The proposed IR-DWT-CGAN model generally integrated a DWT module, an interest point extraction module, an inverse DWT module, and a CGAN. First,...
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This work presents Sigma-FP, a novel 3D reconstruction method to obtain the floor plan of a multi-room environment from a sequence of RGB-D images captured by a wheeled mobile robot. For each input image, the planar patches of visible walls are extracted and subsequently characterized by a multivariate Gaussian distribution in the convenient Plane...
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Cardiac electrophysiology is an effective treatment for atrial fibrillation, in which a long, steerable catheter is inserted into the heart chamber to conduct radio frequency ablation. Magnetic resonance imaging (MRI) can provide enhanced intraoperative monitoring of the ablation progress as well as the localization of catheter position. However, a...
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Particle field measurement is an important topic in many industrial branches. However, there are always complex imaging scenes in the engineering experiments, resulting in severe imaging artifact, noise and blur, such as the optical holography. In this paper, we propose a primary-auxiliary coupled neural network (PANet) for 3D holographic particle...
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Depth estimation using monocular camera sensors is an important technique in computer vision. Supervised monocular depth estimation requires a lot of data acquired from depth sensors. However, acquiring depth data is an expensive task. We sometimes cannot acquire data due to the limitations of the sensor. View synthesis-based depth estimation resea...
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Magnetic particle imaging (MPI) is an emerging molecular imaging technique with high sensitivity and temporal-spatial resolution. Image reconstruction is an important research topic in MPI, which converts an induced voltage signal into the image of superparamagnetic iron oxide particles concentration distribution. MPI reconstruction primarily invol...
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Despite that existing deep-learning-based super resolution methods for satellite images have achieved great performance, these methods are generally designed to stack unaccountable and dense modules (i.e., residual blocks and dense blocks) to reach an optimal mapping function between low-resolution and high-resolution patches/images at the expense...
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Deep learning (DL)-based compressed sensing (CS) has been applied for better performance of image reconstruction than traditional CS methods. However, most existing DL methods utilize the block-by-block measurement and each measurement block is restored separately, which introduces harmful blocking effects for reconstruction. Furthermore, the neuro...
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Introduction Deep learning–based MRI reconstruction has recently been introduced to improve image quality. This study aimed to evaluate the performance of deep learning reconstruction in pediatric brain MRI. Methods A total of 107 consecutive children who underwent 3.0 T brain MRI were included in this study. T2-weighted brain MRI was reconstructe...
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Microwave imaging holds the potential to bring an alternative to ionizing and expensive imaging modalities, such as mammography, magnetic resonance imaging (MRI), and computerized tomography (CT) scan. The absence of a high-resolution and non-model-based image reconstruction technique is one of the reasons that limited microwaves-based imaging. We...
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The concept of structure engineering has been proposed for exploring the next generation of radiation detectors with improved performance. A TOF-PET geometry with het-erostructured scintillators with a pixel size of 3.0 × 3.1 × 15 mm 3 was simulated using Monte Carlo. The heterostructures consisted of alternating layers of BGO as a dense material w...
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We introduce a new class of iterative image reconstruction algorithms for radio interferometry, at the interface of convex optimization and deep learning, inspired by plug-and-play methods. The approach consists in learning a prior image model by training a deep neural network (DNN) as a denoiser, and substituting it for the handcrafted proximal re...