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4D-PET reconstruction of dynamic non-small cell lung cancer [18-F]-FMISO-PET data using adaptive-knot cubic B-splines

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 Green open access

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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 > 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
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Oncology
URI: https://discovery.ucl.ac.uk/id/eprint/10069948
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