# Single-shot Tomography of Discrete Dynamic Objects

arXiv: https://arxiv.org/abs/2311.05269 · HTML: https://mlsanity.com/paper/2311.05269/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 89 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [Learned Primal-dual Reconstruction](https://mlsanity.com/paper/1707.06474/) (2017)
- [Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods](https://mlsanity.com/paper/2003.04989/) (2020)
- [Core Imaging Library -- Part II: Multichannel reconstruction for dynamic and spectral tomography](https://mlsanity.com/paper/2102.06126/) (2021)
- [A New Variational Model for Joint Image Reconstruction and Motion Estimation in Spatiotemporal Imaging](https://mlsanity.com/paper/1812.03446/) (2018)
- [A Variational Model for Joint Motion Estimation and Image Reconstruction](https://mlsanity.com/paper/1607.03255/) (2016)
- [A Variational Reconstruction Method for Undersampled Dynamic X-ray Tomography based on Physical Motion Models](https://mlsanity.com/paper/1705.06079/) (2017)
- [Image reconstruction in dynamic inverse problems with temporal models](https://mlsanity.com/paper/2007.10238/) (2020)
- [Spatiotemporal Besov Priors for Bayesian Inverse Problems](https://mlsanity.com/paper/2306.16378/) (2023)
- [Salt Reconstruction in Full Waveform Inversion with a Parametric Level-Set Method](https://mlsanity.com/paper/1610.00251/) (2016)
- [Sparse dynamic tomography. A shearlet-based approach for iodine perfusion in plant stems](https://mlsanity.com/paper/2002.12343/) (2020)
- [On Optical Flow Models for Variational Motion Estimation](https://mlsanity.com/paper/1512.00298/) (2015)
- [A parametric level-set method for partially discrete tomography](https://mlsanity.com/paper/1704.00568/) (2017)
- [Convex relaxation of discrete vector-valued optimization problems](https://mlsanity.com/paper/2108.10077/) (2021)

## Abstract (arXiv)

This paper presents a novel method for the reconstruction of high-resolution temporal images in dynamic tomographic imaging, particularly for discrete objects with smooth boundaries that vary over time. Addressing the challenge of limited measurements per time point, we propose a technique that synergistically incorporates spatial and temporal information of the dynamic objects. This is achieved through the application of the level-set method for image segmentation and the representation of motion via a sinusoidal basis. The result is a computationally efficient and easily optimizable variational framework that enables the reconstruction of high-quality 2D or 3D image sequences with a single projection per frame. Compared to current methods, our proposed approach demonstrates superior performance on both synthetic and pseudo-dynamic real X-ray tomography datasets. The implications of this research extend to improved visualization and analysis of dynamic processes in tomographic imaging, finding potential applications in diverse scientific and industrial domains.