Pelt, D;
Maughan-Jones, C;
Roche I Morgo, O;
Olivo, A;
Hagen, D;
(2020)
Rapid and flexible high-resolution scanning enabled by cycloidal computed tomography and convolutional neural network (CNN) based data recovery.
In:
Proceedings of the 6th International Conference on Image Formation in X-Ray Computed Tomography.
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Abstract
We have combined a recently developed imaging concept (“cycloidal computed tomography”) with convolutional neural network (CNN) based data recovery. The imaging concept is enabled by exploiting, in synergy, the benefits of probing the sample with a structured x-ray beam and applying a cycloidal acquisition scheme by which the sample is simultaneously rotated and laterally translated. The beam structuring provides a means of increasing the in-slice spatial resolution in tomographic images irrespective of the blur imposed by the x-ray source and detector, while the “roto-translation” sampling allows for rapid scanning. Data recovery based on the recently proposed Mixed-Scale Dense (MSD) CNN architecture enables an efficient reconstruction of high-quality, high-resolution images despite the fact that cycloidal computed tomography data are highly incomplete. In the following, we review the basic principles underpinning cycloidal computed tomography, introduce the CNN based data recovery method and discuss the benefit of combining both.
| Type: | Proceedings paper |
|---|---|
| Title: | Rapid and flexible high-resolution scanning enabled by cycloidal computed tomography and convolutional neural network (CNN) based data recovery |
| Event: | 6th International Conference on Image Formation in X-Ray Computed Tomography |
| Location: | Regensburg, Germany |
| Dates: | 3rd-7th August 2020 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.ct-meeting.org/ |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | computed tomography, micro-CT, convolutional neural networks, machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10115881 |
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