Project detail

Overlap2Overlap: Self-supervised learning towards high quality tiled X-ray computed tomography imaging

Duration: 01.03.2022 — 28.02.2023

Funding resources

Brno University of Technology - Vnitřní projekty VUT

- whole funder (2022-01-01 - 2023-12-31)

On the project

Tiled CT scans of large objects are typically performed with an overlap, that allows stitching of the individual scan tiles into a single volume. Regions of the complete CT scan are thus captured two times. In this project, a novel methodology for noise reduction in tiled CT scans will be developed. By training a convolutional neural network on noise-degraded images from the overlapped regions of the tiled CT scan, we can obtain an image noise reduction model, that can be used to improve the overall quality of the entire CT scan.

Mark

CEITEC VUT-J-22-8022

Default language

Czech

People responsible

Kaiser Jozef, prof. Ing., Ph.D. - fellow researcher
Matula Jan, Ing. - principal person responsible

Units

Advanced instrumentation and methods for material characterization
- internal (2022-01-01 - 2022-12-31)
Central European Institute of Technology BUT
- beneficiary (2022-01-01 - 2022-12-31)

Results

MATULA, J.; PELT, D.; VAN LEEUWEN, T.; ZIKMUND, T.; KAISER, J. Self-supervised learning for high quality tiled X-ray computed tomography imaging: a simulation study. Fifteenth International Conference on Machine Vision (ICMV 2022). Rome: 2022.
Detail

Responsibility: Matula Jan, Ing.