Singh, Imraj;
Barbano, Riccardo;
Kereta, Zeljko;
Jin, Bangti;
Thielemans, Kris;
Arridge, Simon;
(2023)
3D PET-DIP Reconstruction with Relative Difference Prior Using a SIRF-Based Objective.
In:
Proceedings of the 17th International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine.
(pp. pp. 378-381).
Stony Brook Medicine: NY, USA.
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Abstract
Deep Image Prior (DIP) is an unsupervised deep learning technique that does not require ground truth images. For the first time, 3D PET reconstruction with DIP is cast as a single optimisation via penalised maximum likelihood estimation, with a log-likelihood data-fit and an optional Relative Difference Prior term. Experimental results show that although unpenalised DIP optimisation trajectory performs well in high count data, it can fail to adequately resolve lesions in lower count settings. Introducing the Relative Difference Prior into the objective function the DIP trajectory can yield notable improvements.
| Type: | Proceedings paper |
|---|---|
| Title: | 3D PET-DIP Reconstruction with Relative Difference Prior Using a SIRF-Based Objective |
| Event: | 17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine |
| Location: | Stony Brook |
| Dates: | 16 Jul 2023 - 21 Jul 2023 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://renaissance.stonybrookmedicine.edu/Fully3D... |
| 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. |
| 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/10176076 |
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