Adaptive Image Reconstruction Applied to X-Ray Tomography / Adaptive Bildrekonstruktion in der Tomographie

Artemiev, V. M.; Naumov, A. O.; Tillack, Gerd-Rüdiger

doi:10.1515/mt-1998-400904

Cited by 2 publications

(1 citation statement)

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“…One of the advantages of the proposed technique is that the reconstruction performance can be investigated in advance. Previous results are published in [9].…”

Section: Introductionmentioning

confidence: 99%

Recursive tomographic image reconstruction using a Kalman filter approach in the time domain

Artemiev

Naumov

Tillack

2001

J. Phys. D: Appl. Phys.

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The task of this work is to develop a technique for optimal linear recursive tomographic image reconstruction allowing the combination of the reconstruction process with projection data acquisition. The image supposes to be a discrete random field given by a set of linear stochastic difference equations with time as an independent variable. The proposed technique is applicable those tomographic modalities, scan geometries, and acquisition patterns that allow the introduction of a linear observation with an additive noise component. As a result the Kalman filter approach in the time domain is employed and the reconstruction process is represented as the optimal linear recursive estimation procedure with the optimal solution on each reconstruction step. The recursive properties of the proposed algorithm allow the parallelization of the data acquisition process and the reconstruction task. The main restrictions for the application of the Kalman filter approach are given by the huge dimension of the problem and the strong requirements to the amount of prior knowledge introduced. To overcome these restrictions a pseudo Kalman filter approach is investigated. This approach is based on replacing the prior covariance matrix with an empirical one. The reduction of the amount of prior knowledge decreases the dimensionality of the problem as well as the convergence velocity of the algorithm. Introducing an optimized scheme for the data acquisition procedure can partially compensate the degradation of the convergence process.

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“…One of the advantages of the proposed technique is that the reconstruction performance can be investigated in advance. Previous results are published in [9].…”

Section: Introductionmentioning

confidence: 99%

Recursive tomographic image reconstruction using a Kalman filter approach in the time domain

Artemiev

Naumov

Tillack

2001

J. Phys. D: Appl. Phys.

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PARALLEL IMAGE RECONSTRUCTION USING THE MAXIMUM LIKELIHOOD METHOD USING A GRAPHICS PROCESSOR AND THE OpenGL LIBRARY

Zolotarev,

Taruat

2024

Defektoskopiâ

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The creation of fast parallel iterative statistical algorithms based on the use of graphics accelerators is an important and urgent task of great scientific and practical importance. An algorithm based on the method of maximizing the mathematical expectation of maximum likelihood (maximum likelihood expectation MLEM) is considered. MLEM is a numerical method for determining maximum likelihood estimates and, since its first application in the field of image reconstruction in 1982, remains one of the most popular statistical methods of image reconstruction, being the foundation for many other approaches. A new version of the MLEM parallel algorithm is proposed, which provides global convergence of the iterative algorithm. To parallelize the algorithm, the texture mapping method is used using the OpenGL graphics library. The parallel algorithm is described in as much detail as possible. Examples of several reconstructions of images of aluminum casting products are given The obtained result can be used for non-destructive testing of various industrial products, including testing of foundry products.

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Adaptive Image Reconstruction Applied to X-Ray Tomography / Adaptive Bildrekonstruktion in der Tomographie

Cited by 2 publications

References 0 publications

Recursive tomographic image reconstruction using a Kalman filter approach in the time domain

Recursive tomographic image reconstruction using a Kalman filter approach in the time domain

PARALLEL IMAGE RECONSTRUCTION USING THE MAXIMUM LIKELIHOOD METHOD USING A GRAPHICS PROCESSOR AND THE OpenGL LIBRARY

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