Neural Network PET Reconstruction using Scattered Data in Energy-dependent Sinograms

Gabrielle Fontaine, Peter Lindstrom, Stephen Pistorius, Stephen Pistorius

2022

Abstract

PET image reconstruction largely relies on pre-reconstruction data correction, which may add noise and remove information. This loss is particularly notable when correcting for scattered coincidences, which are useful for image reconstruction, though algorithmic scatter reconstructions require a detector energy resolution that exceeds the current state-of-the-art. Preliminary research has demonstrated the feasibility of using convolutional neural networks (CNNs) to reconstruct images directly from sinogram data. We have extended this approach to reconstruct images from data containing scattered coincidences. Monte Carlo simulations were performed to simulate PET data from digital phantoms. Data were modeled using 15% FWHM energy resolution detectors. Energy-dependent sinograms (EDSs), containing true and scattered coincidences, were constructed from the data. After data augmentation, 210,000 sinograms were obtained. A CNN was trained on the EDS-activity pairs for image reconstruction. A second network was trained on sinograms containing only photopeak coincidences. Images were also reconstructed using FBP, and MLEM approaches. The EDS trained network outperformed the photopeak trained network, with a higher mean structural similarity index (0.69 ± .05 vs. 0.63 ± .05) and lower average mean square error (0.16 ± .04 vs. 0.20 ± .04). Our work demonstrates that CNNs have the potential to extract useful information from scattered coincidences, even for data containing significant energy uncertainties.

Paper Citation

in Harvard Style

Fontaine G., Lindstrom P. and Pistorius S. (2022). Neural Network PET Reconstruction using Scattered Data in Energy-dependent Sinograms. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING; ISBN 978-989-758-552-4, SciTePress, pages 35-42. DOI: 10.5220/0010782100003123

in Bibtex Style

@conference{bioimaging22,
author={Gabrielle Fontaine and Peter Lindstrom and Stephen Pistorius},
title={Neural Network PET Reconstruction using Scattered Data in Energy-dependent Sinograms},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING},
year={2022},
pages={35-42},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010782100003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING
TI - Neural Network PET Reconstruction using Scattered Data in Energy-dependent Sinograms
SN - 978-989-758-552-4
AU - Fontaine G.
AU - Lindstrom P.
AU - Pistorius S.
PY - 2022
SP - 35
EP - 42
DO - 10.5220/0010782100003123
PB - SciTePress