DYRECT Computed Tomography: DYnamic Reconstruction of Events on a Continuous Timescale
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866918279418740736 |
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| author | Goethals, Wannes Bultreys, Tom Berg, Steffen Boone, Matthieu N. Aelterman, Jan |
| author_facet | Goethals, Wannes Bultreys, Tom Berg, Steffen Boone, Matthieu N. Aelterman, Jan |
| contents | Time-resolved high-resolution X-ray Computed Tomography (4D $μ$CT) is an imaging technique that offers insight into the evolution of dynamic processes inside materials that are opaque to visible light. Conventional tomographic reconstruction techniques are based on recording a sequence of 3D images that represent the sample state at different moments in time. This frame-based approach limits the temporal resolution compared to dynamic radiography experiments due to the time needed to make CT scans. Moreover, it leads to an inflation of the amount of data and thus to costly post-processing computations to quantify the dynamic behaviour from the sequence of time frames, hereby often ignoring the temporal correlations of the sample structure. Our proposed 4D $μ$CT reconstruction technique, named DYRECT, estimates individual attenuation evolution profiles for each position in the sample. This leads to a novel memory-efficient event-based representation of the sample, using as little as three image volumes: its initial attenuation, its final attenuation and the transition times. This third volume represents local events on a continuous timescale instead of the discrete global time frames. We propose a method to iteratively reconstruct the transition times and the attenuation volumes. The dynamic reconstruction technique was validated on synthetic ground truth data and experimental data, and was found to effectively pinpoint the transition times in the synthetic dataset with a time resolution corresponding to less than a tenth of the amount of projections required to reconstruct traditional $μ$CT time frames. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_00065 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DYRECT Computed Tomography: DYnamic Reconstruction of Events on a Continuous Timescale Goethals, Wannes Bultreys, Tom Berg, Steffen Boone, Matthieu N. Aelterman, Jan Image and Video Processing Computer Vision and Pattern Recognition Time-resolved high-resolution X-ray Computed Tomography (4D $μ$CT) is an imaging technique that offers insight into the evolution of dynamic processes inside materials that are opaque to visible light. Conventional tomographic reconstruction techniques are based on recording a sequence of 3D images that represent the sample state at different moments in time. This frame-based approach limits the temporal resolution compared to dynamic radiography experiments due to the time needed to make CT scans. Moreover, it leads to an inflation of the amount of data and thus to costly post-processing computations to quantify the dynamic behaviour from the sequence of time frames, hereby often ignoring the temporal correlations of the sample structure. Our proposed 4D $μ$CT reconstruction technique, named DYRECT, estimates individual attenuation evolution profiles for each position in the sample. This leads to a novel memory-efficient event-based representation of the sample, using as little as three image volumes: its initial attenuation, its final attenuation and the transition times. This third volume represents local events on a continuous timescale instead of the discrete global time frames. We propose a method to iteratively reconstruct the transition times and the attenuation volumes. The dynamic reconstruction technique was validated on synthetic ground truth data and experimental data, and was found to effectively pinpoint the transition times in the synthetic dataset with a time resolution corresponding to less than a tenth of the amount of projections required to reconstruct traditional $μ$CT time frames. |
| title | DYRECT Computed Tomography: DYnamic Reconstruction of Events on a Continuous Timescale |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.00065 |