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 and IEEE
(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), 2017-4-18 - 2017-4-21.

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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.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: B-splines, Dynamic PET, Expectation Maximization, NSCLC, Regularization
Depositing User: Symplectic Admin
Date Deposited: 08 Aug 2017 08:43
Last Modified: 06 Jun 2024 14:03
DOI: 10.1109/isbi.2017.7950729
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3008836