TIGRE v3: Efficient and easy to use iterative computed tomographic reconstruction toolbox for real datasets
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916521807183872 |
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| author | Biguri, Ander Sadakane, Tomoyuki Lindroos, Reuben Liu, Yi Landman, Malena Sabaté Du, Yi Lohvithee, Manasavee Kaser, Stefanie Hatamikia, Sepideh Bryll, Robert Valat, Emilien Wonglee, Sarinrat Blumensath, Thomas Schönlieb, Carola-Bibiane |
| author_facet | Biguri, Ander Sadakane, Tomoyuki Lindroos, Reuben Liu, Yi Landman, Malena Sabaté Du, Yi Lohvithee, Manasavee Kaser, Stefanie Hatamikia, Sepideh Bryll, Robert Valat, Emilien Wonglee, Sarinrat Blumensath, Thomas Schönlieb, Carola-Bibiane |
| contents | Computed Tomography (CT) has been widely adopted in medicine and it is increasingly being used in scientific and industrial applications. Parallelly, research in different mathematical areas concerning discrete inverse problems has led to the development of new sophisticated numerical solvers that can be applied in the context of CT. The Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox was born almost a decade ago precisely in the gap between mathematics and high performance computing for real CT data, providing user-friendly open-source software tools for image reconstruction. However, since its inception, the tools' features and codebase have had over a twenty-fold increase, and are now including greater geometric flexibility, a variety of modern algorithms for image reconstruction, high-performance computing features and support for other CT modalities, like proton CT. The purpose of this work is two-fold: first, it provides a structured overview of the current version of the TIGRE toolbox, providing appropriate descriptions and references, and serving as a comprehensive and peer-reviewed guide for the user; second, it is an opportunity to illustrate the performance of several of the available solvers showcasing real CT acquisitions, which are typically not be openly available to algorithm developers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10129 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TIGRE v3: Efficient and easy to use iterative computed tomographic reconstruction toolbox for real datasets Biguri, Ander Sadakane, Tomoyuki Lindroos, Reuben Liu, Yi Landman, Malena Sabaté Du, Yi Lohvithee, Manasavee Kaser, Stefanie Hatamikia, Sepideh Bryll, Robert Valat, Emilien Wonglee, Sarinrat Blumensath, Thomas Schönlieb, Carola-Bibiane Medical Physics Mathematical Software Optimization and Control Computed Tomography (CT) has been widely adopted in medicine and it is increasingly being used in scientific and industrial applications. Parallelly, research in different mathematical areas concerning discrete inverse problems has led to the development of new sophisticated numerical solvers that can be applied in the context of CT. The Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox was born almost a decade ago precisely in the gap between mathematics and high performance computing for real CT data, providing user-friendly open-source software tools for image reconstruction. However, since its inception, the tools' features and codebase have had over a twenty-fold increase, and are now including greater geometric flexibility, a variety of modern algorithms for image reconstruction, high-performance computing features and support for other CT modalities, like proton CT. The purpose of this work is two-fold: first, it provides a structured overview of the current version of the TIGRE toolbox, providing appropriate descriptions and references, and serving as a comprehensive and peer-reviewed guide for the user; second, it is an opportunity to illustrate the performance of several of the available solvers showcasing real CT acquisitions, which are typically not be openly available to algorithm developers. |
| title | TIGRE v3: Efficient and easy to use iterative computed tomographic reconstruction toolbox for real datasets |
| topic | Medical Physics Mathematical Software Optimization and Control |
| url | https://arxiv.org/abs/2412.10129 |