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conference on image reconstruction from incomplete data vI

作者： Rasheed, Sarbast Hajian, Arsen R. Tycner, Christopher vu, Linda Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Cent Michigan Univ Dept Phys Mt Pleasant MI 48859 USA

ISBN: (纸本)9780819482969

In this paper, we describe a practical implementation of an image reconstruction method designed to generate a map of the brightness distribution from data consisting of squared visibilities and complex closure amplitudes resulting from observations of an astronomical target with a broadband, multichannel, spatial optical interferometer. Given the data, the method estimates the true brightness distribution with a model sampled on a rectangular grid of discrete positions on the sky with the assumption that the model intensities in the region not defined by the discrete positions being described by bilinear interpolation of the discrete intensities. The developed image reconstruction method has been applied to real observational data obtained from existing optical interferometer facilities.

关键词： image reconstruction modelling optical interferometry optimization

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Linear projective reconstruction from matching tensors

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image AND vISION COMPUTING 1997年第8期15卷 617-625页

作者： Triggs, B INRIA Rhône-Alpes 655 avenue de l'Europe 38330 Montbonnot St. Martin France

This paper describes initial work on a family of projective reconstruction techniques that compute projection matrices directly and linearly from matching tensors estimated from the image data. The approach is based on ''joint image closure relations'' - bilinear constraints between matching tensors and projection matrices, that express the fact that the former derive from the latter. The simplest methods use fundamental matrices and epipoles, alternative ones use trilinear tensors. It is possible to treat all of the image data uniformly, without reliance on ''privileged'' images or tokens. The underlying theory is discussed, and the performance of the new methods is quantified and compared with that of several existing ones. (C) 1997 Elsevier Science B.v.

关键词： multi-image structure projective reconstruction matching tensors

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3-D signal reconstruction from noisy projection data for stochastic objects as a generalization of Gaussian mixture parameter estimation

3-D signal reconstruction from noisy projection data for sto...

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conference on image reconstruction from incomplete data vI

作者： Zheng, Yili Doerschuk, Peter C. Univ Calif Berkeley Lawrence Berkeley Lab Computat Res Div Berkeley CA 94720 USA

A statistical estimation problem for determining 3-D reconstructions from a single 2-D projection image of each of multiple objects when the objects are heterogeneous is described. The method is based on a Gaussian mi... 详细信息

ISBN: (纸本)9780819482969

关键词： statistical inverse problem statistical signal reconstruction tomography cryo electron microscopy virus structure

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Evaluation of 2D image reconstruction using fan-beam FBP algorithm with no backprojection weight from WLP completed truncated projection data

Evaluation of 2D image reconstruction using fan-beam FBP alg...

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2011 4th International conference on Biomedical Engineering and Informatics, BMEI 2011

作者： Sumith, K. Narasimhadhan, A.v. Rajgopal, Kasi Dept. of Electrical Engg Indian Institute of Science Bangalore India

ISBN: (纸本)9781424493524

This paper presents the image reconstruction using the fan-beam filtered backprojection (FBP) algorithm with no backprojection weight from windowed linear prediction (WLP) completed truncated projection data. The image reconstruction from truncated projections aims to reconstruct the object accurately from the available limited projection data. Due to the incomplete projection data, the reconstructed image contains truncation artifacts which extends into the region of interest (ROI) making the reconstructed image unsuitable for further use. data completion techniques have been shown to be effective in such situations. We use windowed linear prediction technique for projection completion and then use the fan-beam FBP algorithm with no backprojection weight for the 2-D image reconstruction. We evaluate the quality of the reconstructed image using fan-beam FBP algorithm with no backprojection weight after WLP completion. © 2011 IEEE.

关键词： image reconstruction

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A direct approach to estimating surfaces in tomographic data

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MEDICAL image ANALYSIS 2002年第3期6卷 235-249页

作者： Whitaker, RT Elangovan, v Univ Utah Sch Comp Salt Lake City UT 84112 USA Scimagix Inc San Mateo CA 94403 USA

Under ideal circumstances, the inverse of the radon transform is computable, and sequences of measured projections are sufficient to obtain accurate estimates of volume densities. In situations where the sinogram data is incomplete, the radon transform is noninvertable, and attempts to reconstruct greyscale density values result in reconstruction artifacts that can undermine the effectiveness of subsequent processing. This paper presents a direct approach to the segmentation of incomplete tomographic data. The strategy is to impose a fairly simple model on the data, and treat segmentation as a problem of estimating the interface between two substances of somewhat homogeneous density. The segmentation is achieved by simultaneously deforming a surface model and updating density parameters in order to achieve a best fit between the projected model and the measured sinograms. The deformation is implemented with level-set surface models, calculated at the resolution of the input data. Relative to previous work, this paper makes several contributions. First is a proper derivation of the deformation of surface models that move according to a gradient descent on a likelihood measure. We also present a series of computational innovations that make this direct surface-fitting approach feasible with state-of-the-art computers. Another contribution is the demonstration of the effectiveness of this approach on under-constrained tomographic problems, using both simulated and real datasets. (C) 2002 Elsevier Science B.v. All rights reserved.

关键词： tomography surface reconstruction level sets segmentation

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Recovery of missing data in partial geometry PET scanners: Compensation in projection space vs image space

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MEDICAL PHYSICS 2018年第12期45卷 5437-5449页

作者： Shojaeilangari, Seyedehsamaneh Schmidtlein, C. Ross Rahmim, Arman Ay, Mohammad Reza Univ Tehran Med Sci Res Ctr Mol & Cellular Imaging Tehran Iran Inst Res Fundamental Sci IPM Sch Cognit Sci POB 193955746 Tehran Iran Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY 10065 USA Johns Hopkins Univ Hosp Dept Radiol Baltimore MD 21287 USA Univ British Columbia Dept Radiol Vancouver BC V5Z 1M9 Canada Univ British Columbia Dept Phys & Astron Vancouver BC V5Z 1M9 Canada Univ Tehran Med Sci Dept Med Phys & Biomed Engn Tehran *** Iran

Purpose Robust and reliable reconstruction of images from noisy and incomplete projection data holds significant potential for proliferation of cost-effective medical imaging technologies. Since conventional reconstruction techniques can generate severe artifacts in the recovered images, a notable line of research constitutes development of appropriate algorithms to compensate for missing data and to reduce noise. In the present work, we investigate the effectiveness of state-of-the-art methodologies developed for image inpainting and noise reduction to preserve the quality of reconstructed images from undersampled PET data. We aimed to assess and ascertain whether missing data recovery is best performed in the projection space prior to reconstruction or adjoined with the reconstruction step in image space. Methods Different strategies for data recovery were investigated using realistic patient derived phantoms (brain and abdomen) in PET scanners with partial geometry (small and large gap structures). Specifically, gap filling strategies in projection space were compared with reconstruction based compensation in image space. The methods used for filling the gap structure in sinogram PET data include partial differential equation based techniques (PDE), total variation (Tv) regularization, discrete cosine transform(DCT)-based penalized regression, and dictionary learning based inpainting (DLI). For compensation in image space, compressed sensing based image reconstruction methods were applied. These include the preconditioned alternating projection (PAPA) algorithm with first and higher order total variation (HOTv) regularization as well as dictionary learning based compressed sensing (DLCS). We additionally investigated the performance of the methods for recovery of missing data in the presence of simulated lesion. The impact of different noise levels in the undersampled sinograms on performance of the approaches were also evaluated. Results In our first study (brai

关键词： compressed sensing image inpainting partial geometry positron emission tomography

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Multigrid Bayesian methods for optical diffusion tomography

Multigrid Bayesian methods for optical diffusion tomography

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conference on image reconstruction from incomplete data

作者： Millane, RP Ye, JC Bouman, CA Webb, KJ Purdue Univ Computat Sci & Engn Program W Lafayette IN 47907 USA

ISBN: (纸本)0819437689

Optical diffusion imaging is a new imaging modality that promises great potential in applications such as medical imaging, environmental sensing and nondestructive testing. It presents a difficult nonlinear image reconstruction problem however. An inversion algorithm is formulated in a Bayesian framework, and an efficient optimization technique that uses iterative coordinate descent is presented. A general multigrid optimization technique for nonlinear image reconstruction problems is developed and applied to the optical diffusion imaging problem. Numerical results show that this approach improves the quality of reconstructions and dramatically decreases computation times.

关键词： optical diffusion imaging Bayesian image reconstruction generalized Gaussian Markov random field iterative coordinate descent multigrid

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Structural Prior Enhanced Compressed Sensing for CT reconstruction with incomplete data

Structural Prior Enhanced Compressed Sensing for CT Reconstr...

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60th IEEE Nuclear Science Symposium (NSS) / Medical Imaging conference (MIC) / 20th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors

作者： Shen, Le Xing, Yuxiang Jin, Xin Nuctech Co Beijing Peoples R China Tsinghua Univ Dept Engn Phys Beijing Peoples R China Tsinghua Univ Minist Educ Key Lab Particle & Radiat Imaging Beijing 100084 Peoples R China

ISBN: (纸本)9781479905348

CT image reconstruction from incomplete projection data is a challenging problem. Among massive reconstruction methods, iterative reconstruction based on compressed sensing (CS) is a promising one that enables us to accurately recovery signals from highly under-sample data when the signals have a sparse representation, which usually can be done by the constrained l(1) minimization. The total variation (Tv) minimization is a commonly used sparsity constraint, which assumes the target image is piece-wise constant. Tv based CS algorithm has been successfully applied to solve many computed tomography problems, such as few views and interior reconstruction. In this work, we proposed a novel CS algorithm combined with a prior image to enhance the Tv sparsity, namely structural prior enhanced compressed sensing (SPECS). Numerical simulation indicates SPECS is effective and robust for many kinds of incomplete data cases.

关键词： Compressed sensing

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Motion compensation for electro-optical line scanner sensors using incomplete data

Motion compensation for electro-optical line scanner sensors...

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conference on image reconstruction from incomplete data

作者： Power, GJ USAF Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (纸本)0819437689

When imaging the ground from the air, distortions can occur if the imagery was created from an electro-optical line scanner pointing to nadir and mounted on the bottom of all airborne platform. The inability of the aircraft to maintain a perfect trajectory can cause the distortions. In the worst case scenario, camera stabilizers fail, no geographical reference or navigation data is available, and the sensor periodically fails leaving incomplete data for image reconstruction. Motion compensation can restore the images. This paper describes various distortions that can be created for an airborne nadir-aimed line scanner. A motion-compensation technique is introduced that combines multiple cues from geographical reference and navigation data as well as line-scan matched filtering. A semi-automated restoration implementation is introduced followed by the automated line-scan matched filter implementation. These various compensation techniques provide backup for each other thus creating a more efficient motion-compensation system. Even in the worst case scenario, the system continues to attempt motion compensation using an optimal line-scan matched filtering technique. The results of using this automated technique for motion compensation is demonstrated using simulated high-definition imagery and then using actual electro-optical and hyperspectral images that were obtained from the Dynamic data Base (DDB) program sponsored LS the Defense Advanced Research Projects Agency (DARPA).

关键词： matched filtering Motion compensation image reconstruction hyperspectral electro-optical sensors airborne sensors

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Sampling rates and image reconstruction from scattered fields

Sampling rates and image reconstruction from scattered field...

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conference on image reconstruction from incomplete data vI

作者： Shahid, Umer Fiddy, Michael A. Testorf, Markus E. Univ N Carolina Ctr Optoelect & Opt Commun 9201 Univ City Blvd Charlotte NC 28223 USA Dartmouth Coll Hanover NH 03755 USA

ISBN: (纸本)9780819482969

Cepstral filtering is reviewed as a suitable and efficient method to solve the inverse scattering problem in the case of strongly scattering permittivity distributions. The number and distribution of measured scattered field data required is discussed, as is the effective number of degrees of freedom available to describe the scattering structure. The latter is identified as a key parameter determining the performance of the cepstral method. This is of particular importance for strong scattering and nonlinear image processing methods since many data sets are compiled based on the sampling requirements of weakly scattering objects. We find that the domain of the object support and the maximum permittivity contrast are important prior information for determining the minimum number of data samples necessary while maximizing use of the available degrees of freedom;examples are presented.

关键词： Inverse Scattering Diffraction Tomography Sampling image reconstruction Born Approximation Strong Scattering

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