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Statistical inversion for medical x-ray tomography with few radiographs:: ii.: Application to dental radiology

PHYSICS IN MEDICINE AND BIOLOGY 2003年第10期48卷 1465-1490页

作者： Kolehmainen, V Siltanen, S Järvenpää, S Kaipio, JP Koistinen, P Lassas, M Pirttilä, J Somersalo, E Univ Kuopio Dept Appl Phys FIN-70211 Kuopio Finland Instrumentat Corp Imaging Div FIN-04301 Tuusula Finland Univ Helsinki Rolf Nevanlinna Inst FIN-00014 Helsinki Finland Invers Ltd FIN-99600 Sodankyla Finland Helsinki Univ Technol Inst Math FIN-02015 Helsinki Finland

Diagnostic and operational tasks in dental radiology often require three-dimensional information that is difficult or impossible to see in a projection image. A CT-scan provides the dentist with comprehensive three-dimensional data. However, often CT-scan is impractical and, instead, only a few projection radiographs with sparsely distributed projection directions are available. Statistical (Bayesian) inversion is well-suited approach for reconstruction from such incomplete data. In statistical inversion, a priori information is used to compensate for the incomplete information of the data. The inverse problem is recast in the form of statistical inference from the posterior probability distribution that is based on statistical models of the projection data and the a priori information of the tissue. In this paper, a statistical model for three-dimensional imaging of dentomaxillofacial structures is proposed. Optimization and MCMC algorithms are implemented for the computation of posterior statistics. Results are given with in vitro projection data that were taken with a commercial intraoral x-ray sensor. Examples include limited-angle tomography and full-angle tomography with sparse projection data. reconstructions with traditional tomographic reconstruction methods are given as reference for the assessment of the estimates that are based on the statistical model.

关键词： Projections and Predictions Tomography statistical Roentgen Rays Radiology Specialty 体层摄影术 Radiology Tomography PROJECTION Projection X-Ray Computed Tomography dental radiology X-rays statistical models (nuclear)

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Dictionary Learning-Based image reconstruction for Terahertz Computed Tomography

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JOURNAL OF INFRARED MILLIMETER AND TERAHERTZ WAVES 2021年第8期42卷 829-842页

作者： Zhong, Fasheng Niu, Liting Wu, Weiwen Liu, Fenglin Chongqing Univ Key Lab Optoelect Technol & Syst Minist Educ Chongqing Peoples R China Chongqing Univ Engn Res Ctr Ind Computed Tomog Nondestruct Testi Minist Educ Chongqing 400044 Peoples R China Guangdong Univ Petrochem Technol Coll Sci Maoming Guangdong Peoples R China

Terahertz computed tomography (THz CT) demonstrates its advantages in aspects of nonmetallic and nonpolar materials penetration, 3D internal structure visualization, etc. To perform satisfied reconstruction results, it is necessary to obtain complete measurements from many different views. However, this process is time-consuming and we usually obtain incomplete projections for THz CT in practice, which generates artifacts in the final reconstructed images. To address this issue, dictionary learning-based THz CT reconstruction (DLTR) model is proposed in this study. Especially, the image patches are extracted from other state-of-the-art reconstructed images to train the initial dictionary by using the K-SVD algorithm. Then, the dictionary can be adaptively updated during THz CT reconstruction. Finally, the updated dictionary is used for further updating reconstructed images. In order to verify the accuracy and quality of DLTR method, the filtered back-projection (FBP), simultaneous algebraic reconstruction technique (SART), and total variation (TV) reconstruction are chosen as comparisons. The experiment results show that the DLTR method has a good capability for noise suppression and structures preservation.

关键词： Terahertz CT image reconstruction Dictionary learning incomplete data

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SHAPE reconstruction ON A VARYING MESH

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1990年第4期12卷 345-362页

作者： WEISS, I Center for Autom. Res. Maryland Univ. College Park MD USA

A central class of image understanding problems is concerned with reconstructing a shape from an incomplete data set, such as fitting a surface to (partially) given contours. A new theory for solving such problems is presented. Unlike the current heuristic methods, the method used starts from fundamental principles that should be followed by any reconstruction method, regardless of its mathematical or physical implementation. A mathematical procedure which conforms to these principles is presented. One major advantage of the method is the ability to handle shapes containing both smooth and sharp parts without using thresholds. A sharp variation, such as a corner, requires a high-resolution mesh for adequate representation, while slowly varying sections can be represented with sparser mesh points. Unlike current methods, this procedure fits the surface on a varying mesh. The mesh is constructed automatically to be more dense at parts of the image that have more rapid variation. Analytical examples are given in simple cases, followed by numerical experiments.

关键词： Shape image reconstruction Surface fitting Noise shaping Surface reconstruction Smoothing methods reconstruction algorithms Curve fitting image converters image processing INSPEC: Controlled Indexing picture processing pattern recognition INSPEC: Non-Controlled Indexing varying mesh picture processing pattern recognition shape reconstruction image understanding high-resolution mesh

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Blind deconvolution of speckle images constrained by wavefront sensing data

Blind deconvolution of speckle images constrained by wavefro...

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2nd Conference on image reconstruction from incomplete data

作者： Leung, WYV Clare, RM Lane, RG Univ Canterbury Dept Elect & Comp Engn Christchurch New Zealand

ISBN: (纸本)0819445592

The predominant effect of the atmosphere on the incoming wavefront of an astronomical object is the introduction of a phase distortion, resulting in,a speckle image at the ground-based telescope. Deconvolution from wavefront sensing is an imaging technique used to compensate for the degradation due to atmospheric turbulence, where the point spread function is estimated from the wavefront sensing data. However in this approach any information in the speckle images regarding the point spread function is not utilised. This paper investigates the joint application of wavefront sensing data and speckle images in reconstructing the point spread function and the object in a Bayesian framework. The results on experimental data demonstrate the feasibility of this approach even under very low light levels.

关键词： blind deconvolution wavefront sensing Shack-Hartmann sensor

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High-quality indoor scene 3D reconstruction with RGB-D cameras:A brief review

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Computational Visual Media 2022年第3期8卷 369-393页

作者： Jianwei Li Wei Gao Yihong Wu Yangdong Liu Yanfei Shen School of Sports Engineering Beijing Sports UniversityBeijing 100084China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100080China University of Chinese Academy of Sciences Beijing 100049China Huawei Technologies Co. Ltd.Beijing 100085China

High-quality 3D reconstruction is an important topic in computer graphics and computer vision with many applications,such as robotics and augmented *** advent of consumer RGB-D cameras has made a profound advance in indoor scene *** the past few years,researchers have spent significant effort to develop algorithms to capture 3D models with RGB-D *** depth images produced by consumer RGB-D cameras are noisy and incomplete when surfaces are shiny,bright,transparent,or far from the camera,obtaining highquality 3D scene models is still a challenge for existing *** here review high-quality 3D indoor scene reconstruction methods using consumer RGB-D *** this paper,we make comparisons and analyses from the following aspects:(i)depth processing methods in 3D reconstruction are reviewed in terms of enhancement and completion,(ii)ICP-based,feature-based,and hybrid methods of camera pose estimation methods are reviewed,and(iii)surface reconstruction methods are reviewed in terms of surface fusion,optimization,and *** performance of state-of-the-art methods is also compared and *** survey will be useful for researchers who want to follow best practices in designing new high-quality 3D reconstruction methods.

关键词： 3D reconstruction image processing camera pose estimation surface fusion

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reconstruction algorithm for 3D Compton scattering imaging with incomplete data

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2021年第7期29卷 967-989页

作者： Rigaud, G. Hahn, B. N. Univ Bremen Ctr Ind Math D-28357 Bremen Germany Univ Wurzburg Dept Math Wurzburg Germany Univ Stuttgart Dept Math Stuttgart Germany

Compton scattering describes the scattering of a photon after its collision with an electron. The recent developments of spectral cameras, able to collect photons in terms of energy, open the way to a new imaging concept: 3D Compton scattering imaging (CSI), which seeks to exploit the scattered radiation as a vector of information while a specimen of interest is illuminated by a monochromatic ionizing source. Focusing on modelling the first-order scattering, image reconstruction from CSI data remains a difficult challenge. In particular, physical constraints (detector and architecture of the scanner) lead to various incompleteness scenario within the data and thus streak artifacts when using filtered backprojection type formulas. This paper addresses the problem of recovering an object under study using CSI data subject to incompleteness and assuming only first-order scattering. The proposed method consists of suitably tuning the multiplicative Kaczmarz algorithm and is implemented and tested for two architectures of the scanner. Furthermore, the modality on CSI considered here presents the advantage of not requiring any rotation of the source or object.

关键词： Compton scattering image reconstruction incomplete data expectation maximization total variation

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Generalized support constraint for three-dimensional reconstruction from incomplete Fourier spectra

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JOURNAL OF ELECTRONIC IMAGING 1997年第4期6卷 426-438页

作者： Payot, E Preteux, FJ Trousset, Y Guillemaud, R CEA LETI DSYS F-38054 Grenoble 9 France Inst Natl Telecommun F-91011 Evry France G E Med Syst F-78533 Buc France

Many imaging systems involve a loss of information that requires the incorporation of prior knowledge in the restoration/reconstruction process. We focus on the typical case of 30 reconstruction from an incomplete set of projections. An approach based an constrained optimization is introduced This approach provides a powerful mathematical framework for selecting a specific solution from the set of feasible solutions;this is done by minimizing some criteria depending on prior densitometric information that can be interpreted through a generalized support constraint. We propose a global optimization scheme using a deterministic relaxation algorithm based on Bregman's algorithm associated with half-quadratic minimization techniques. When used for 30 vascular reconstruction from 2D digital subtracted angiography (DSA) data, such an approach enables the reconstruction of a well-contrasted 30 vascular network in comparison with results obtained using standard algorithms. (C) 1997 SPIE and IS&T.

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Three-dimensional surface approximation from incomplete data using distance transform

Three-dimensional surface approximation from incomplete data...

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2nd Conference on image reconstruction from incomplete data

作者： Lee, DJ Westover, B Eifert, J Brigham Young Univ Dept Elect & Comp Engn Provo UT 84602 USA

ISBN: (纸本)0819445592

Three-dimensional (3-D) object surface reconstruction is an important step toward non-destructive measurements of surface area and volume. Laser triangulation technique has been widely used for obtaining 3-D information. However, the 3-D data obtained from triangulation are not dense enough or usually not complete for surface reconstruction, especially for objects with irregular shape. As a result of fitting surfaces with these sparse 3-D data, inaccuracy in measuring the surface area or calculating the volume of the object is inevitable. A computer vision technique combining laser triangulation and distance transform has been developed to improve the measurement accuracy for objects with irregular shape. A 3-D wire-frame model is generated first with all available 3-D data. Each pixel within the image boundary is given the distance information using distance transform. The distance information of each pixel is then used as the constraints for surface fitting and interpolation. With this additional information from distance transform, more accurate surface approximation can be achieved. The measurement accuracy of this technique is compared with other interpolation techniques for the volume measurement of oyster meats.

关键词： distance transform laser triangulation 3-D wire-frame model machine vision surface fitting surface interpolation oyster

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reconstruction of Missing GPR data Using Multiple-Point Statistical Simulation

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Zhang, Chongmin Gravey, Mathieu Mariethoz, Gregoire Irving, James Univ Lausanne Inst Earth Sci CH-1015 Lausanne Switzerland Austrian Acad Sci Inst Interdisciplinary Mt Res A-6020 Innsbruck Austria Univ Lausanne Inst Earth Surface Dynam CH-1015 Lausanne Switzerland

Ground-penetrating radar (GPR) is a powerful geophysical tool for efficient, high-resolution mapping of the shallow subsurface. Because of physical and economical limitations, a commonly encountered issue is that the corresponding profiles are incomplete in the sense that measurements are desired where they do not exist for data visualization, interpretation, and imaging. Such missing data may result, for example, from regions along the profile where surveying is not possible;from measurements being collected at a regular interval in time but not in space;or from the choice of a large measurement spacing to favor data coverage over quality. Although a number of methods have been proposed for the interpolation of GPR data to tackle this problem, they typically suffer from rather simplistic assumptions that are not satisfied for many GPR datasets. To address these shortcomings, we consider in this article a novel GPR data reconstruction strategy based on multiple-point geostatistics, where missing GPR data are stochastically simulated and conditioned on existing measurements and patterns observed in a representative training image. A key feature in our approach is the consideration of a multivariate image containing both continuous and categorical GPR reflection amplitude data, which helps to guide the simulations toward realistic structures. To demonstrate the power of this single strategy for multiple data reconstruction needs, we show its successful application to a variety of examples in the context of three problems: gap-filling, trace-spacing regularization, and trace densification.

关键词： image reconstruction Interpolation Geophysical measurements Training Three-dimensional displays Geologic measurements Uncertainty Ground-penetrating radar (GPR) interpolation multiple-point statistics (MPS) reconstruction simulation

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Sampling, radiometry, and image reconstruction for polar and geostationary meteorological remote sensing systems

Sampling, radiometry, and image reconstruction for polar and...

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2nd Conference on image reconstruction from incomplete data

作者： Abrams, MC Cain, SC ITT Industries Inc. (United States) Indiana Univ.-Purdue Univ./Fort Wayne (United States)

ISBN: (纸本)0819445592

In this paper, a Bayesian-based image reconstruction scheme is utilized for estimating a high resolution temperature map of the top of the earth's atmosphere using the GOES-8 (Geostationary Operational Environmental Satellite) imager infrared channels. By simultaneously interpolating the image while estimating temperature, the proposed algorithm achieves a more accurate estimate of the sub-pixel temperatures than could be obtained by performing these operations independently of one another. The proposed algorithm differs from other Bayesian-based image interpolation schemes in that it estimates brightness temperature as opposed to image intensity and incorporates a detailed optical model of the GOES multi-channel imaging system. The temperature estimation scheme is compared to deconvolution via pseudo-inverse filtering using two metrics. One metric is the mean squared temperature error. This metric describes the radiometric accuracy of the image estimate. The second metric is the recovered Modulation Transfer Function (MTF) of the image estimate. This method has traditionally been used to evaluate the quality of image recovery techniques. It will be shown in this paper that there is an inconsistency between these two metrics in that an image with high spatial frequency content can be reconstructed with poor radiometric, accuracy. The ramifications of this are discussed in order to evaluate the two metrics for use in quantifying the performance of image reconstruction algorithms.

关键词： Imaging systems image restoration reconstruction algorithms Sensors image resolution Modulation transfer functions Radiometry data modeling Satellite imaging Satellites

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