Tofu: a fast, versatile and user-friendly image processing toolkit for computed tomography
| Autor: | Tomáš Faragó, Sergey Gasilov, Iain Emslie, Marcus Zuber, Lukas Helfen, Matthias Vogelgesang, Tilo Baumbach |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Synchrotron Radiation, Vol 29, Iss 3, Pp 916-927 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1600-5775 16005775 |
| DOI: | 10.1107/S160057752200282X |
| Popis: | Tofu is a toolkit for processing large amounts of images and for tomographic reconstruction. Complex image processing tasks are organized as workflows of individual processing steps. The toolkit is able to reconstruct parallel and cone beam as well as tomographic and laminographic geometries. Many pre- and post-processing algorithms needed for high-quality 3D reconstruction are available, e.g. phase retrieval, ring removal and de-noising. Tofu is optimized for stand-alone GPU workstations on which it achieves reconstruction speed comparable with costly CPU clusters. It automatically utilizes all GPUs in the system and generates 3D reconstruction code with minimal number of instructions given the input geometry (parallel/cone beam, tomography/laminography), hence yielding optimal run-time performance. In order to improve accessibility for researchers with no previous knowledge of programming, tofu contains graphical user interfaces for both optimization of 3D reconstruction parameters and batch processing of data with pre-configured workflows for typical computed tomography reconstruction. The toolkit is open source and extensive documentation is available for both end-users and developers. Thanks to the mentioned features, tofu is suitable for both expert users with specialized image processing needs (e.g. when dealing with data from custom-built computed tomography scanners) and for application-specific end-users who just need to reconstruct their data on off-the-shelf hardware. |
| Databáze: | Directory of Open Access Journals |
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