Dynamic Tomographic Reconstruction of Deforming Volumes

Jailin, Clément; Roux, Stéphane

doi:10.3390/ma11081395

Cited by 20 publications

(14 citation statements)

References 55 publications

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“…Another variant is to use a projection-based DVC (P-DVC) to estimate the deformation of a known reference shape (template) from only few projections [Leclerc et al 2015;Taillandier-Thomas et al 2016]. Finally, Jailin et al [2018] proposed a combination between a variant of the ART reconstruction and the basis functions P-DVC approach to reconstruct a deforming volume and retrieve its motion, in a multi-scale scheme. The main limitation of these approaches is their specificity to certain types of deforming objects, where it is easy to define the basis function and track the motion through them.…”

Section: Related Workmentioning

confidence: 99%

Warp-and-project tomography for rapidly deforming objects

et al. 2019

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Computed tomography has emerged as the method of choice for scanning complex shapes as well as interior structures of stationary objects. Recent progress has also allowed the use of CT for analyzing deforming objects and dynamic phenomena, although the deformations have been constrained to be either slow or periodic motions. In this work we improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations. Our method uses a warp-and-project approach that allows us to introduce an essentially continuous time axis where consistency of the reconstructed shape with the projection images is enforced for the specific time and deformation state at which the image was captured. The method uses an efficient, time-adaptive solver that yields both the moving geometry as well as the deformation field. We validate our method with extensive experiments using both synthetic and real data from a range of different application scenarios.

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Section: Related Workmentioning

confidence: 99%

Warp-and-project tomography for rapidly deforming objects

et al. 2019

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“…This issue has been addressed before in other medical imaging fields, such as magnetic resonance imaging, 13,14 and computed tomography scan. [15][16][17][18] To the best of the authors' knowledge, this article is the first, which broaches this subject in non-ionized EIST. Radar techniques, such as synthetic aperture radar, which are capable of detecting moving targets, are not the subject of this article.…”

Section: Introductionmentioning

confidence: 98%

Addressing dynamics in radio frequency tomography

Saliani

Pourziad

2022

Int J RF Mic Comp-Aid Eng

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Tomographic imaging of a dynamic scene is addressed for the first time in this article. The effect of dynamics of the imaged object is studied for some deformation scenarios using the conventional truncated singular value decomposition (TSVD) method, which is applied in numerous static inverse scattering problems, together with variational data assimilation (DA) methods including three-dimensional variational (3D-VAR) and four-dimensional variational (4D-VAR) techniques. Results show that the DA frameworks outperform the TSVD method, which assumes the scene static, especially for objects with higher contrast. Also, it is seen that applying the conventional TSVD method makes the image blurred and distorts it for high-speed dynamic changes, particularly when the contrast is increased. However, the proposed DA methods seem to be robust against large deformation distortions and can still detect the object.

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“…The ill-posedness of the inverse problem is addressed by incorporating an equilibrium-gap regularizer, registration is evaluated from the projection-based residuals only. In [46] tomographic reconstruction is directly coupled with the P-DVC problem in a way that two subproblems are solved sequentially without coordinating variables. In addition, the method involves a multiscale Gaussian convolution procedure to properly correct displacements of different size and correct finer features.…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimization for Nonrigid Nano-Tomography

Никитин

Andrade

Slyamov

et al. 2021

IEEE Trans. Comput. Imaging

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Resolution level and reconstruction quality in nanocomputed tomography (nano-CT) are in part limited by the stability of microscopes, because the magnitude of mechanical vibrations during scanning becomes comparable to the imaging resolution, and the ability of the samples to resist radiation induced deformations during data acquisition. In such cases, there is no incentive in recovering the sample state at different time steps like in time-resolved reconstruction methods, but instead the goal is to retrieve a single reconstruction at the highest possible spatial resolution and without any imaging artifacts. Here we propose a distributed optimization solver for tomographic imaging of samples at the nanoscale. Our approach solves the tomography problem jointly with projection data alignment, nonrigid sample deformation correction, and regularization. Projection data consistency is regulated by dense optical flow estimated by Farneback's algorithm, leading to sharp sample reconstructions with less artifacts. Synthetic data tests show robustness of the method to Poisson and lowfrequency background noise. We accelerated the solver on multi-GPU systems and validated the method on three nano-imaging experimental data sets.

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Dynamic Tomographic Reconstruction of Deforming Volumes

Cited by 20 publications

References 55 publications

Warp-and-project tomography for rapidly deforming objects

Warp-and-project tomography for rapidly deforming objects

Addressing dynamics in radio frequency tomography

Distributed Optimization for Nonrigid Nano-Tomography

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