Ralli, GP;
McGowan, DR;
Chappell, MA;
Sharma, RA;
Higgins, GS;
Fenwick, JD;
(2017)
4D-PET reconstruction of dynamic non-small cell lung cancer [18-F]-FMISO-PET data using adaptive-knot cubic B-splines.
In:
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
(pp. pp. 1189-1192).
IEEE
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Abstract
4D-PET reconstruction has the potential to significantly increase the signal-to-noise ratio in dynamic PET by fitting smooth temporal functions during the reconstruction. However, the optimal choice of temporal function remains an open question. A 4D-PET reconstruction algorithm using adaptive-knot cubic B-splines is proposed. Using realistic Monte-Carlo simulated data from a digital patient phantom representing an [18-F]-FMISO-PET scan of a non-small cell lung cancer patient, this method was compared to a spectral model based 4D-PET reconstruction and the conventional MLEM and MAP algorithms. Within the entire patient region the proposed algorithm produced the best bias-noise trade-off, while within the tumor region the spline- and spectral model-based reconstructions gave comparable results.
| Type: | Proceedings paper |
|---|---|
| Title: | 4D-PET reconstruction of dynamic non-small cell lung cancer [18-F]-FMISO-PET data using adaptive-knot cubic B-splines |
| Event: | IEEE 14th International Symposium on Biomedical Imaging (ISBI), 18-21 April 2017, Melbourne, VIC, Australia |
| Location: | Melbourne, AUSTRALIA |
| Dates: | 18 April 2017 - 21 April 2017 |
| ISBN-13: | 978-1-5090-1172-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ISBI.2017.7950729 |
| Publisher version: | https://doi.org/10.1109/ISBI.2017.7950729 |
| 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: | Image reconstruction , Splines (mathematics) , Reconstruction algorithms , Phantoms , Adaptation models , Cancer , Mathematical model, B-splines , Dynamic PET , Expectation Maximization , NSCLC , Regularization |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10069948 |
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