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The purpose was to assess three treatments planning techniques including one in intensity-modulated radiation therapy (IMRT) for cerebral irradiation of pregnant woman, in order to limit the dose delivered to the foetus. The treatment provided was 60 Gy to the planning target volume. Estimated foetal dose was measured using an anthropomorphic phantom, on the upper and middle part of the uterus. The first plan consisted in four beams in conformational technique delivered from a Varian accelerator with a 120 leaves collimator, the second one used non-coplanar fields and the third one assessed IMRT. With the conformational technique, the dose at the upper part of the uterus was 8.3 mGy and 6.3 mGy at the middle part. The dose delivered to the foetus was higher with the non-coplanar fields. In IMRT, the dose at the upper part of the uterus was 23.8 mGy and 14.3 mGy at the middle part. The three plans used 6 MV X-rays. Because of the use of leaves and non-coplanar fields, IMRT does not seem to be the optimal technique for the treatment of pregnant woman. However, the dose delivered to the foetus remains low and below the dose of 100 mGy recommended by the International Commission of Radiological Protection. It seems possible to consider the use of this technique for a better sparing of organs at risk for the mother.

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.