Automatic Optimization of Alignment Parameters for Tomography Datasets

Bleichrodt, Folkert; Batenburg, Kees Joost

doi:10.1007/978-3-642-38886-6_46

Cited by 11 publications

(23 citation statements)

References 17 publications

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“…At this stage, the proposed reconstruction method relies on a good estimation of the relative alignment parameters between the sample and the detector at each scanning coordinate. In the presence of misalignments, if a reasonable tomographic reconstruction guess is available from the proposed reconstruction method, additional alignment refinement algorithms such as those in [5], [7], [9], [21], [22] could be applied.…”

Section: Figure 1: Schematic Representation Of the Overlap Between Comentioning

confidence: 99%

Direct three-dimensional tomographic reconstruction and phase retrieval of far-field coherent diffraction patterns

et al. 2019

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We present an alternative numerical reconstruction algorithm for direct tomographic reconstruction of a sample's refractive indices from the measured intensities of its far-field coherent diffraction patterns. We formulate the well-known phase-retrieval problem in ptychography in a tomographic framework which allows for simultaneous reconstruction of the illumination function and the sample's refractive indices in three dimensions. Our iterative reconstruction algorithm is based on the Levenberg-Marquardt algorithm and we demonstrate the performance of our proposed method with simulated and real datasets.Ptychography or CDI phase retrieval algorithms replace the purpose of an image forming lens by recovering the unknown phase numerically, using iterative algorithms mostly based on some type of optimization scheme. A few exceptions to these methods exist with closed-form solutions [2], [3] but their high sampling requirements make their application practically infeasible.

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Section: Figure 1: Schematic Representation Of the Overlap Between Comentioning

confidence: 99%

Direct three-dimensional tomographic reconstruction and phase retrieval of far-field coherent diffraction patterns

et al. 2019

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“…(9) and estimate x and the geometrical parameters simultaneously. This approach has been proposed in [5,26]. Since the inverse problem in Eq.…”

Section: Projection Matchingmentioning

confidence: 99%

“…However, the experimental results in Section 4 suggest that the finite difference approach is effective and is not hampered by the fact that W is not differentiable in every point. The step size of the finite difference method h is selected automatically, following the same procedure as proposed in [5]. Here we found that the step size is important for the accuracy of the gradient.…”

Section: Projection Matching With Discrete Tomographymentioning

confidence: 99%

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Aligning Projection Images from Binary Volumes

Bleichrodt

Beenhouwer

Sijbers

et al. 2014

Fundamenta Informaticae

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In tomography, slight differences between the geometry of the scanner hardware and the geometric model used in the reconstruction lead to alignment artifacts. To exploit high-resolution detectors used in many applications of tomography, alignment of the projection data is essential.Markerless alignment algorithms are the preferred choice over alignment with markers, in case a fully automatic tomography pipeline is required. Moreover, marker based alignment is often not feasible or even possible. At the same time, markerless alignment methods often fail in scenarios where only a small number of projections are available. In this case, the angular separation between projection images is large and therefore the correlation between them is low. This is a property that most markerless algorithms rely on. The intermediate reconstruction problem of alignment by projection matching is highly underdetermined in the limited data case. Therefore, we propose a projection matching method that incorporates prior knowledge of the ground truth. We focus on reconstructing binary volumes. A discrete tomography algorithm is employed to generate intermediate reconstructions. This type of reconstruction algorithm does not rely heavily on correlated projection images. Our numerical results suggest that alignment using discrete tomography projection matching produces much better results in the limited angle case, when compared to a projection matching method that employs an algebraic reconstruction method.

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“…In the past decades, various methods for alignment of tomography projections were proposed, such as marker-based methods [10][11][12][13], cross-correlation [14][15][16][17], bootstrap methods [18][19][20][21][22][23][24][25][26], common-line [9,[27][28][29], and feature matching [30,31]. However, each of the methods has some drawbacks that hinder their application for high-accuracy alignment of large X-ray datasets.…”

Section: Introductionmentioning

confidence: 99%

Alignment methods for nanotomography with deep subpixel accuracy

et al. 2019

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As the resolution of X-ray tomography improves, the limited long-term stability and accuracy of nanoimaging tools does not allow computing artifact-free three-dimensional (3D) reconstructions without an additional step of numerical alignment of the measured projections. However, the common iterative alignment methods are significantly more computationally demanding than a simple tomographic reconstruction of the acquired volume. Here, we address this issue and present an alignment toolkit, which exploits methods with deep-subpixel accuracy combined with a multi-resolution scheme. This leads to robust and accurate alignment with significantly reduced computational and memory requirements. The performance of the presented methods is demonstrated on simulated and measured datasets for tomography and also laminography acquisition geometries. A GPU accelerated implementation of our alignment framework is publicly available.

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Automatic Optimization of Alignment Parameters for Tomography Datasets

Cited by 11 publications

References 17 publications

Direct three-dimensional tomographic reconstruction and phase retrieval of far-field coherent diffraction patterns

Direct three-dimensional tomographic reconstruction and phase retrieval of far-field coherent diffraction patterns

Aligning Projection Images from Binary Volumes

Alignment methods for nanotomography with deep subpixel accuracy

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