Kim, Hyojin; Kang, Jingu; Champley, Kyle; Anirudh, Rushil; Mohan, Aditya (2022). LLNL D4DCT Datasets: Dynamic 4DCT Datasets using MPM-based Deformation, 256^3 volume resolution. In Lawrence Livermore National Laboratory (LLNL) Open Data Initiative. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0765FG7

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).

The MPM deformable objects for the task of dynamic 4D computed tomography are provided as two datasets. The first dataset contains 80^3 ground-truth images with 10 frames whereas the second dataset contains 256^3 ground-truth images with 60 frames. The second version of the dataset is useful to demonstrate dynamic CT reconstruction at a higher resolution with more time frames. Another difference is that the second dataset uses the multi-dimensional array format used in the NumPy package (npy).

• 2020-05-10 to 2022-04-30

• 2022

• Kim, Hyojin

• Anirudh, Rushil
• Champley, Kyle

• Kang, Jingu
• Mohan, Aditya

Laboratory Directed Research and Development (LDRD)-FS: 20-FS-010, LDRD-ER: 22-ERD-032

• Additive manufacturing
• Affine transformation
• Computed tomography
• Deformation
• Dynamic 4D CT
• Dynamic computed tomography (DCT)
• Machine learning
• Material deformation
• Material point method (MPM)
• Reconstruction

• data

• English

Aditya Mohan: https://orcid.org/0000-0002-0921-6559

Hyojin Kim: https://orcid.org/0000-0001-7032-0999

Kyle Champley: https://orcid.org/0000-0002-8653-9562

Rushil Anirudh: https://orcid.org/0000-0002-4186-3502

Primary associated publication

• 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
• 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

Software

• Dynamic CT Reconstruction from Limtied Views with Implicit Neural Representations and Parametric Motion Fields GitHub repository: https://github.com/awreed/DynamicCTReconstruction

Related data

• Kim, Hyojin; Kang, Jingu; Champley, Kyle; Anirudh, Rushil; Mohan, Aditya (2020). LLNL D4DCT Datasets: Dynamic 4DCT Datasets using MPM-based Deformation, 80^3 volume resolution. In Lawrence Livermore National Laboratory (LLNL) Open Data Initiative. UC San Diego Library Digital Collections. https://doi.org/10.6075/J00R9MZF

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• Lawrence Livermore National Laboratory

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2022-06-09