Combined Motion Estimation and Reconstruction in Tomography

Eyndhoven, G. Van; Sijbers, Jan; Batenburg, Joost

doi:10.1007/978-3-642-33863-2_2

Cited by 13 publications

(9 citation statements)

References 15 publications

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“…In order to do so, an common strategy [1,7,9,10,11,15,16,18,21,24,25,30] is to reconstruct one initial image I 0 and a trajectory of deformations t → ϕ t such that a each time t the image φ t · I 0 (deformation of I 0 by φ t ) matches the observed data. Then the framework has two intertwined components, estimation of I 0 and estimation of t → ϕ t , that can be alternatively performed in an iterative optimization scheme.…”

Section: Introductionmentioning

confidence: 99%

Incorporation of a Deformation Prior in Image Reconstruction

Gris

2019

J Math Imaging Vis

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This article presents a method to incorporate a deformation prior in image reconstruction via the formalism of deformation modules. The framework of deformation modules allows to build diffeomorphic deformations that satisfy a given structure. The idea is to register a template image against the indirectly observed data via a modular deformation, incorporating this way the deformation prior in the reconstruction method. We show that this is a well-defined regularization method (proving existence, stability and convergence) and present numerical examples of reconstruction from 2-D tomographic simulations and partially-observed images.

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Section: Introductionmentioning

confidence: 99%

Incorporation of a Deformation Prior in Image Reconstruction

Gris

2019

J Math Imaging Vis

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“…If the deformation model is known a priori, it can be integrated into Filtered BackProjection (FBP) or Simultaneous Iterative Reconstruction Technique (SIRT) to produce artifact-free reconstructions [4,5]. Other techniques have been proposed to estimate the deformation parameters [6,7], in case no a priori deformation model is available.…”

Section: Introductionmentioning

confidence: 99%

“…This leads to more accurate reconstruction quality in the dynamic region as well. Unlike the methods proposed in [4,5,6,7], our method works without the need of estimating a deformation model, which is often error-prone and time-consuming. The proposed algorithm is validated with phantom data and applied to a real cardiac µCT dataset.…”

Section: Introductionmentioning

confidence: 99%

Region based 4D tomographic image reconstruction: Application to cardiac x-ray CT

Eyndhoven

Batenburg

Sijbers

2015

2015 IEEE International Conference on Image Processing (ICIP)

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X-ray computed tomography (CT) is a powerful tool for noninvasive cardiac imaging. However, radiation dose is a major issue. In this paper, we propose an iterative reconstruction method that reduces the radiation dose without compromising image quality. This is achieved by exploiting prior knowledge in two ways: the reconstructed object is assumed to consist of both stationary and dynamic regions over time and the dynamic region is assumed to have sparse structures after a proper sparsifying space-time transform. Experiments on simulation data and a real µCT cardiac mouse dataset show that, with comparable image quality, the radiation dose can be substantially reduced compared to conventional acquisition/reconstruction protocols.

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“…For global rotations and translations, an estimation procedure using data consistency conditions is proposed in [48]. Iterative procedures are, for example, based on edge entropy [28], or perform estimation and reconstruction step simultaneously [45].…”

mentioning

confidence: 99%

Using the Navier-Cauchy equation for motion estimation in dynamic imaging

Hahn¹,

Kienle-Garrido²,

Klingenberg³

et al. 2020

Preprint

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Tomographic image reconstruction is well understood if the specimen being studied is stationary during data acquisition. However, if this specimen changes during the measuring process, standard reconstruction techniques can lead to severe motion artefacts in the computed images. Solving a dynamic reconstruction problem therefore requires to model and incorporate suitable information on the dynamics in the reconstruction step to compensate for the motion.Many dynamic processes can be described by partial differential equations which thus could serve as additional information for the purpose of motion compensation. In this article, we consider the Navier-Cauchy equation which characterizes small elastic deformations and serves, for instance, as a model for respiratory motion. Our goal is to provide a proof-of-concept that by incorporating the deformation fields provided by this PDE, one can reduce the respective motion artefacts in the reconstructed image. To this end, we solve the Navier-Cauchy equation prior to the image reconstruction step using suitable initial and boundary data. Then, the thus computed deformation fields are incorporated into an analytic dynamic reconstruction method to compute an image of the unknown interior structure. The feasibility is illustrated with numerical examples from computerized tomography.

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Combined Motion Estimation and Reconstruction in Tomography

Cited by 13 publications

References 15 publications

Incorporation of a Deformation Prior in Image Reconstruction

Incorporation of a Deformation Prior in Image Reconstruction

Region based 4D tomographic image reconstruction: Application to cardiac x-ray CT

Using the Navier-Cauchy equation for motion estimation in dynamic imaging

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