Advanced x-ray tomography: experiment, modeling, and algorithms

Batenburg, Kees Joost; Carlo, Francesco De; Mancini, Lucia; Sijbers, Jan

doi:10.1088/1361-6501/aacd25

“…From an application's point of view, our proposed method fits in the larger field of sparse tomography [15,30], but moreover to applications with strict dose limitations and obstructions to the imaging region [9]. Such limitations might be due to safety reasons, experimental environment, or even expensive beamtime at synchrotron facilities [8]. For instance, in image-guided radiotherapy angles are chosen to radiate only a cancerous ROI, without damaging essential surrounding tissue, using the knowledge obtained during the measurements, cf [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Sequentially optimized projections in x-ray imaging ^*

Burger¹,

Hauptmann²,

Helin³

et al. 2021

View full text Add to dashboard Cite

This work applies Bayesian experimental design to selecting optimal projection geometries in (discretized) parallel beam X-ray tomography assuming the prior and the additive noise are Gaussian. The introduced greedy exhaustive optimization algorithm proceeds sequentially, with the posterior distribution corresponding to the previous projections serving as the prior for determining the design parameters, i.e. the imaging angle and the lateral position of the sourcereceiver pair, for the next one. The algorithm allows redefining the region of interest after each projection as well as adapting parameters in the (original) prior to the measured data. Both A and D-optimality are considered, with emphasis on efficient evaluation of the corresponding objective functions. Two-dimensional numerical experiments demonstrate the functionality of the approach.

show abstract

“…PyPore3D addresses the processing and analysis of 3D images of porous and/or multiphase media. These types of materials have been gaining increasing interest, especially with the development of multiscale and multimodal 3D and 4D imaging techniques ([ 16 ]). Accordingly, the development of efficient tools that support this type of study are highly demanded to explore microstructural properties in order to optimise manufacturing procedures or for engineering performance tuning [ 17 , 18 ], as well as to interpret and model phenomena in Earth science (see, e.g., LaRue et al, [ 19 ]; Mancini et al, 2020 [ 19 ]) or investigate biomedical specimens ([ 20 , 21 , 22 , 23 ]).…”

Section: Introductionmentioning

confidence: 99%

PyPore3D: An Open Source Software Tool for Imaging Data Processing and Analysis of Porous and Multiphase Media

Aboulhassan

¹

,

Brun

²

,

Kourousias

³

et al. 2022

Self Cite

View full text Add to dashboard Cite

In this work, we propose the software library PyPore3D, an open source solution for data processing of large 3D/4D tomographic data sets. PyPore3D is based on the Pore3D core library, developed thanks to the collaboration between Elettra Sincrotrone (Trieste) and the University of Trieste (Italy). The Pore3D core library is built with a distinction between the User Interface and the backend filtering, segmentation, morphological processing, skeletonisation and analysis functions. The current Pore3D version relies on the closed source IDL framework to call the backend functions and enables simple scripting procedures for streamlined data processing. PyPore3D addresses this limitation by proposing a full open source solution which provides Python wrappers to the the Pore3D C library functions. The PyPore3D library allows the users to fully use the Pore3D Core Library as an open source solution under Python and Jupyter Notebooks PyPore3D is both getting rid of all the intrinsic limitations of licensed platforms (e.g., closed source and export restrictions) and adding, when needed, the flexibility of being able to integrate scientific libraries available for Python (SciPy, TensorFlow, etc.).

show abstract

Sequentially optimized projections in X-ray imaging

Burger,

Hauptmann,

Helin

et al. 2020

Preprint

View full text Add to dashboard Cite

This work applies Bayesian experimental design to selecting optimal projection geometries in (discretized) parallel beam X-ray tomography assuming the prior and the additive noise are Gaussian. The introduced greedy exhaustive optimization algorithm proceeds sequentially, with the posterior distribution corresponding to the previous projections serving as the prior for determining the design parameters, i.e. the imaging angle and the lateral position of the sourcereceiver pair, for the next one. The algorithm allows redefining the region of interest after each projection as well as adapting parameters in the (original) prior to the measured data. Both A and D-optimality are considered, with emphasis on efficient evaluation of the corresponding objective functions. Two-dimensional numerical experiments demonstrate the functionality of the approach.

show abstract

Advanced x-ray tomography: experiment, modeling, and algorithms

Abstract: A modified interval subdividing based geometric calibration method for interior tomography Zhang Feng, Yan Bin, Li Lei et al.

Cited by 3 publications

References 18 publications

Sequentially optimized projections in x-ray imaging ^*

Sequentially optimized projections in x-ray imaging ^*

PyPore3D: An Open Source Software Tool for Imaging Data Processing and Analysis of Porous and Multiphase Media

Sequentially optimized projections in X-ray imaging

Contact Info

Product

Resources

About

Advanced x-ray tomography: experiment, modeling, and algorithms

Abstract: A modified interval subdividing based geometric calibration method for interior tomography Zhang Feng, Yan Bin, Li Lei et al.

Cited by 3 publications

References 18 publications

Sequentially optimized projections in x-ray imaging *

Sequentially optimized projections in x-ray imaging *

PyPore3D: An Open Source Software Tool for Imaging Data Processing and Analysis of Porous and Multiphase Media

Sequentially optimized projections in X-ray imaging

Contact Info

Product

Resources

About

Sequentially optimized projections in x-ray imaging ^*

Sequentially optimized projections in x-ray imaging ^*