LLNL D4DCT Datasets: Dynamic 4DCT Datasets using MPM-based Deformation
Additional dataset description (LLNL_D4DCT_Datasets.pdf)
Describes MPM-based deformation, CT simulation setup, and data categories.
Dynamic 4DCT Dataset of MPM-based Deformation (D4DCT-DFM)
Contains 157 different deformation sets, each of which has 20 different data samples.
- Cite This Work
Kim, Hyojin; Kang, Jingu; Champley, Kyle; Anirudh, Rushil; Mohan, Aditya (2020). LLNL D4DCT Datasets: Dynamic 4DCT Datasets using MPM-based Deformation. In Lawrence Livermore National Laboratory (LLNL) Open Data Initiative. UC San Diego Library Digital Collections. https://doi.org/10.6075/J00R9MZF
Dynamic computed tomography (DCT) refers to reconstruction of moving or non-rigid objects over time while x-ray projections are acquired over a range of angles. The measured x-ray sinogram data represents a time-varying sequence of dynamic scenes, where a small angular range of the sinogram will correspond to a static or quasi-static scene, depending on the amount of motion or deformation as well as the system setup. The reconstruction of DCT is widely applicable to the study of object deformation and dynamics in a number of industrial and clinical applications (e.g., heart CT). In the material science and additive manufacturing applications, the DCT capabilities aid in the study of damage evolution due to dynamic thermal loads and mechanical stresses over time which provides crucial information about their overall performance and safety.
We provide two dynamic CT datasets (D4DCT-DFM, D4DCT-AFN) where the sinogram data represent a time-varying object deformation to demonstrate damage evolution due to several mechanical stresses (compression). The provided datasets enable training and evaluation of the data driven machine learning methods for DCT reconstruction. To build the datasets, we used Material Point Method (MPM)-based methods to simulate deformation of objects under mechanical loading, and then simulated CT sinogram data using Livermore Tomography Tools (LTT).
- Creation Date
- 2020-05-10 to 2020-10-30
- Date Issued
- Principal Investigator
- Co Principal Investigators
- Data Contributors
Laboratory Directed Research and Development (LDRD)-FS: 20-FS-010
View formats within this collection
Aditya Mohan: http://orcid.org/0000-0002-0921-6559
Hyojin Kim: https://orcid.org/0000-0001-7032-0999
Rushil Anirudh: https://orcid.org/0000-0002-4186-3502
- Related Resources
- Albert W. Reed, Hyojin Kim, Rushil Anirudh, K. Aditya Mohan, Kyle Champley, Jingu Kang, Suren Jayasuriya; Dynamic CT Reconstruction From Limited Views With Implicit Neural Representations and Parametric Motion Fields. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. https://openaccess.thecvf.com/content/ICCV2021/html/Reed_Dynamic_CT_Reconstruction_From_Limited_Views_With_Implicit_Neural_Representations_ICCV_2021_paper.html
- Albert W. Reed, Hyojin Kim, Rushil Anirudh, K. Aditya Mohan, Kyle Champley, Jingu Kang, Suren Jayasuriya (2021). Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fields. arXiv. https://arxiv.org/abs/2104.11745
- Dynamic CT Reconstruction from Limtied Views with Implicit Neural Representations and Parametric Motion Fields GitHub repository: https://github.com/awreed/DynamicCTReconstruction
Primary associated publication
- Rights Holder
- Lawrence Livermore National Laboratory
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- Digital Object Made Available By
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