Singh, Imraj RD;
Barbano, Riccardo;
Twyman, Robert;
Kereta, Željko;
Jin, Bangti;
Arridge, Simon;
Thielemans, Kris;
(2024)
Deep Image Prior PET Reconstruction using a SIRF-Based Objective.
In:
2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).
(pp. pp. 1-2).
IEEE: Milano, Italy.
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Abstract
Widespread adoption of deep learning in medical imaging has been hampered, in part, due to a lack of integration with clinically applicable software. In this work, we establish a direct connection between an established PET reconstruction suite, SIRF, and PyTorch. This allows for advanced reconstruction methodologies to be deployed on clinical data with an unsupervised deep learning approach: the Deep Image Prior. Results show consistent quality metrics for DIP in comparison to OSMAP.
| Type: | Proceedings paper |
|---|---|
| Title: | Deep Image Prior PET Reconstruction using a SIRF-Based Objective |
| Event: | 2022 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference |
| Location: | Milan |
| Dates: | 5 Nov 2022 - 12 Nov 2022 |
| ISBN-13: | 978-1-6654-8872-3 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/NSS/MIC44845.2022.10399292 |
| Publisher version: | https://doi.org/10.1109/NSS/MIC44845.2022.10399292 |
| 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: | Deep learning; Measurement; Software; Image reconstruction; Biomedical imaging; Electronics packaging |
| 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 Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10176077 |
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