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We create a dataset of clinically realistic Monte-Carlo respiratory PET data.We validated the realism of the simulated data.We evaluated the lesion detectability regarding their location, volume and contrast.Respiratory-gating of 18F-FDG PET images improves the accuracy of volume estimates. In PET/CT thoracic imaging, respiratory motion reduces image quality. A solution consists in performing respiratory gated PET acquisitions. The aim of this study was to generate clinically realistic Monte-Carlo respiratory PET data, obtained using the 4D-NCAT numerical phantom and the GATE simulation tool, to assess the impact of respiratory motion and respiratory-motion compensation in PET on lesion detection and volume measurement. To obtain reconstructed images as close as possible to those obtained in clinical conditions, a particular attention was paid to apply to the simulated data the same correction and reconstruction processes as those applied to real clinical data. The simulations required 140,000h (CPU) generating 1.5 To of data (98 respiratory gated and 49 ungated scans). Calibration phantom and patient reconstructed images from the simulated data were visually and quantitatively very similar to those obtained in clinical studies. The lesion detectability was higher when the better trade-off between lesion movement limitation (compared to ungated acquisitions) and image statistic preservation is considered (respiratory cycle sampling in 3 frames). We then compared the lesion volumes measured on conventional PET acquisitions versus respiratory gated acquisitions, using an automatic segmentation method and a 40%-threshold approach. A time consuming initial manual exclusion of noisy structures needed with the 40%-threshold was not necessary when the automatic method was used. The lesion detectability along with the accuracy of tumor volume estimates was largely improved with the gated compared to ungated PET images.

International audience; An iterative generic algorithm has been developed to compare three thresholding models used to delineate gross tumour volume on (18)F-FDG PET images. 3D volume was extracted and characteristic parameters were measured. Three fitting models using different parameters were studied: model 1 (volume, contrast), model 2 (contrast) and model 3 (SUV). The calibration was performed using a cylindrical phantom filled with hot spheres. To validate the models, two other phantoms were used. The calibration procedure showed a better fitting model for model 1 (R(2) from 0.94 to 1.00) than for model 3 (0.95) and model 2 (0.69). The validation study shows that model 3 yielded large volume measurement errors. Models 1 and 2 gave close results with no significant differences. Model 2 was preferred because it presents less error dispersion and needs fewer characteristic parameters, making it easier to implement. Our results show the importance of developing a generic algorithm to compare the performances of fitting models objectively and to validate results on other phantoms than the ones used during the calibration process to avoid methodological biases.