4D water droplet collision datasets
We share the simulated water droplet collision datasets modeled with the Navier-Stokes Cahn-Hilliard equations. This data is used to validate our deep-learning 4D reconstruction algorithm, as reported in "4D-ONIX for reconstructing 3D movies from sparse X-ray projections via deep learning" [1]. The datasets are simulated for two scenarios: reproducible processes and quasi-reproducible processes. We share the training datasets and the ground truth files for both scenarios. The ground truth files are 3D movies of droplet collision simulation. The training datasets are simulated projection pairs for the experiment of X-ray Multi-Projection Imaging (XMPI) [2, 3]. The two projections are 23.8° apart.
For the reproducible process, the ground truth file contains a single 3D movie with 75 timestamps. The size of each 3D object is 128×128×128. The training dataset contains 16 XMPI experiments of this dynamical process, measured from 16 random angles. The angles differences among the 16 projection pairs are: 0°,2°,13°,16°,26°,28°,43°,52°,64°,74°,87°,95°,102°,115°,130°,144°.
For the quasi-reproducible process, the ground truth file contains 16 simulations of droplet collision with a 10% variance in collision velocities and a 10% variance in droplet size. A single projection pair with random orientation (same as the reproducible process) of the sample was selected from each simulation to form a training dataset. Please refer to our article for the difference between reproducible and quasi-reproducible processes and details about collision simulation and projection generation.
References:
[1] Zhang, Yuhe, et al. "4D-ONIX: A deep learning approach for reconstructing 3D movies from sparse X-ray projections." arXiv preprint arXiv:2401.09508 (2024).
[2] Villanueva-Perez, Pablo, et al. "Hard x-ray multi-projection imaging for single-shot approaches." Optica 5.12 (2018): 1521-1524.
[3] Villanueva-Perez, Pablo, et al. "Megahertz X-ray Multi-projection imaging." arXiv preprint arXiv:2305.11920 (2023).
