Optimal gating compared to 3D and 4D PET reconstruction for characterization of lung tumours

Abstract

Purpose: We investigated the added value of a new respiratory amplitude-based PET reconstruction method called optimal gating (OG) with the aim of providing accurate image quantification in lung cancer.

Methods: FDG-PET imaging was performed in 26 lung cancer patients during free breathing using a 24-min list-mode acquisition on a PET/CT scanner. The data were reconstructed using three methods: standard 3D PET, respiratory-correlated 4D PET using a phase-binning algorithm, and OG. These datasets were compared in terms of the maximum SUV (SUVmax) in the primary tumour (main endpoint), noise characteristics, and volumes using thresholded regions of SUV 2.5 and 40% of the SUVmax.

Results: SUVmax values from the 4D method (13.7 ± 5.6) and the OG method (14.1 ± 6.5) were higher (4.9 ± 4.8%, p < 0.001 and 6.9 ± 8.8%, p < 0.001, respectively) than that from the 3D method (13.1 ± 5.4). SUVmax did not differ between the 4D and OG methods (2.0 ± 8.4%, p = NS). Absolute and relative threshold volumes did not differ between methods, except for the 40% SUVmax volume in which the value from the 3D method was lower than that from the 4D method (-5.3 ± 7.1%, p = 0.007). The OG method exhibited less noise than the 4D method. Variations in volumes and SUVmax of up to 40% and 27%, respectively, of the individual gates of the 4D method were also observed.

Conclusion: The maximum SUVs from the OG and 4D methods were comparable and significantly higher than that from the 3D method, yet the OG method was visibly less noisy than the 4D method. Based on the better quantification of the maximum and the less noisy appearance, we conclude that OG PET is a better alternative to both 3D PET, which suffers from breathing averaging, and the noisy images of a 4D PET.

Fig. 1

Example breathing pattern (left) over 30 s showing the optimal gating window. The histogram (right) shows the amount of breathing amplitudes for the entire 24-min list-mode acquisition. The OG method selects the narrowest bandwidth (shaded area) containing 35% of the respiratory signal

Fig. 2

Phantom experiment showing 4D PET reconstructed images together with the motion blurred (static) 3D PET and the OG PET images

Fig. 3

Axial images from an example patient (patient 18) comparing OG, 3D and 4D PET reconstruction methods: top row CT images, middle row PET images, bottom row fused PET/CT images; left column OG reconstruction, middle column 3D reconstruction, right column 4D reconstruction

Fig. 4

SUVmax values for the OG, 3D and 4D PET reconstruction methods classified according to tumour location inside the lung

Fig. 5

Top row: box plot of the SUVmax values normalized to the mean value for the different gates of the 4D PET method. Bottom row: box plot of the volumes defined by 40% of the SUVmax normalized to the mean volume for the individual patients. In both plots, the edges of the boxes represent the 25th and 75th percentiles, and the whiskers represent the extreme data points. Outliers are plotted individually (circles)

Fig. 6

Standard deviations of the SUV values inside the VOI in the contralateral lung tissue for all patients relative to the value of the 3D PET reconstruction

Fig. 7

Processing for respiratory gating. a Data flow in ordinary 4D PET. b Data flow with amplitude-based respiratory gating. c A 60-s trace showing gates before synchronization, triggers, and respiratory amplitudes. d Time correlation between triggers and gates. e Gates and triggers after synchronization

Fig. 8

a Histogram of a patient’s respiration amplitudes. The amplitude ranges found by the OG algorithm for sensitivities from 5% to 95% are shown in blue. It was assumed that tumour excursion is 30 mm (black arrow). The 35% sensitivity value (red arrow) was used in this research. b For each of the 26 patients in our study, the PET FWHM resolution modelled by Eq. B.6 is plotted as a function of OG sensitivity (red arrow 35% sensitivity)