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LLNL D4DCT Datasets: Dynamic 4DCT Datasets using MPM-based Deformation
Kim, Hyojin
Date Created and/or Issued
2020-05-10 to 2020-10-30
Contributing Institution
UC San Diego, Library, Research Data Curation Program
Lawrence Livermore National Laboratory (LLNL) Open Data Initiative
Rights Information
Under copyright
Constraint(s) on Use: This work is protected by the U.S. Copyright Law (Title 17, U.S.C.). Use of this work beyond that allowed by "fair use" or any license applied to this work requires written permission of the copyright holder(s). Responsibility for obtaining permissions and any use and distribution of this work rests exclusively with the user and not the UC San Diego Library. Inquiries can be made to the UC San Diego Library program having custody of the work.
Use: This work is available from the UC San Diego Library. This digital copy of the work is intended to support research, teaching, and private study.
Rights Holder and Contact
Lawrence Livermore National Laboratory
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).
Laboratory Directed Research and Development (LDRD)-FS: 20-FS-010
Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (
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.
Computed tomography
Material point method (MPM)
Dynamic computed tomography (DCT)
Material deformation
Additive manufacturing
Machine learning
Dynamic 4D CT
Affine transformation

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