Three-dimensional attenuation map reconstruction using geometrical models and free-form deformations [SPECT application]

Battle, Xavier L.; Rest, C. Le; Turzo, A.; Bizais, Y.

doi:10.1109/42.870251

Cited by 17 publications

(11 citation statements)

References 26 publications

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“…Later, it was used to estimate points on a closed contour between two regions in two dimensions [26]. The approach was formalized in a software package, the Bayesian Inference Engine (BIE), by Cunningham in 1994 [27,28,29], and subsequently applied to three-dimensional objects and real data [30,31,32,33,34]. In these applications, only the interface between the object and external region is estimated.…”

Section: Discussionmentioning

confidence: 99%

Model-based Tomographic Reconstruction Literature Search

Chambers¹,

Lehman²

2005

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Section: Discussionmentioning

confidence: 99%

Model-based Tomographic Reconstruction Literature Search

Chambers¹,

Lehman²

2005

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“…Reconstruction of the attenuation map from TCT projection data with a high noise level or from a small number of directions, due to a short measurement time [7] In this approach, the measurement time can be reduced, which implies the possibility that a new system configuration or measurement protocol can be devised.…”

Section: Introductionmentioning

confidence: 98%

“…Consequently, there have been intensive efforts to reconstruct the attenuation map from incomplete measurement data [1][2][3][4][5][6][7]. These studies can be broadly divided into the following three classes, according to the purpose.…”

Section: Introductionmentioning

confidence: 99%

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Attenuation map reconstruction using topology constrained labeling

Kudo

Nakamura

2003

Electron Comm Jpn Pt II

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SUMMARYIn order to derive a quantitative reconstructed image in various types of emission CT (ECT), such as SPECT and PET, it is necessary to examine the gamma ray attenuation coefficient distribution inside the body, called the attenuation map, by transmission CT (TCT), and to apply attenuation correction. This paper proposes a new method in which the attenuation map is reconstructed from the incomplete TCT projection data or ECT projection data. The conventional attenuation map reconstruction method is performed, assuming that each pixel of the attenuation map takes a continuous value. In the proposed method, in contrast, the attenuation map is reconstructed by region segmentation in which the pixel value takes only discrete values. The problem is formulated as a labeling problem in which the label image minimizing the energy function is to be determined. In order to avoid local minima, the solution is derived by stochastic relaxation. In the conventional formulation of image region segmentation, however, the search space for the solution is too wide, and it is difficult to derive an accurate attenuation map if the measured data are incomplete. Consequently, this paper proposes topology constrained labeling, in which topology constraints, that is, a priori information on the topology (the number of regions and the connection relations among the regions) of the attenuation map to which the region segmentation is applied, are used, and the solution search space is constrained. Simulation experiments, as well as an experiment using the actual PET data, were performed, and the effectiveness of the topology constrained labeling has been demonstrated.

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“…their arc lengths [17]. However, their reconstruction as well as those of polyhedral and cubic spline surfaces [21], [22] require implicit voxel representation for technical purposes. Among nonparametric shape models, the level-set approach has been investigated in the 2-D case but suffers from a huge computation cost due to the storage and update of a 3-D image [14].…”

mentioning

confidence: 99%

Polygonal and Polyhedral Contour Reconstruction in Computed Tomography

Soussen

Mohammad-Djafari

2004

IEEE Trans. on Image Process.

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Abstract-This paper is about three-dimensional (3-D) reconstruction of a binary image from its X-ray tomographic data. We study the special case of a compact uniform polyhedron totally included in a uniform background and directly perform the polyhedral surface estimation. We formulate this problem as a nonlinear inverse problem using the Bayesian framework. Vertice estimation is done without using a voxel approximation of the 3-D image. It is based on the construction and optimization of a regularized criterion that accounts for surface smoothness. We investigate original deterministic local algorithms, based on the exact computation of the line projections, their update, and their derivatives with respect to the vertice coordinates. Results are first derived in the two-dimensional (2-D) case, which consists of reconstructing a 2-D object of deformable polygonal contour from its tomographic data. Then, we investigate the 3-D extension that requires technical adaptations. Simulation results illustrate the performance of polygonal and polyhedral reconstruction algorithms in terms of quality and computation time.Index Terms-Block relaxation algorithms, closed contour and surface reconstruction, deformable template, global and local iterative descent algorithms, polygonal and polyhedral modeling, two-dimensional and three-dimensional binary image reconstruction from projections, X-ray tomography.

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Three-dimensional attenuation map reconstruction using geometrical models and free-form deformations [SPECT application]

Cited by 17 publications

References 26 publications

Model-based Tomographic Reconstruction Literature Search

Model-based Tomographic Reconstruction Literature Search

Attenuation map reconstruction using topology constrained labeling

Polygonal and Polyhedral Contour Reconstruction in Computed Tomography

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