We investigate the difference between surface matching and target matching for pelvic radiation image guidance. The uniqueness of our study is that all patients have multiple CT-on-rails (CTOR) scans to compare to corresponding AlignRT images. Ten patients receiving pelvic radiation were enrolled in this study. Two simulation CT scans were performed in supine and prone positions for each patient. Body surface contours were generated in treatment planning system and exported to AlignRT to serve as reference images. During treatment day, the patient was aligned to treatment isocenter with room lasers, and then scanned with both CTOR and AlignRT. Image-guidance shifts were calculated for both modalities by comparison to the simulation CT and the differences between them were analyzed for both supine and prone positions, respectively. These procedures were performed for each patient once per week for five weeks. The difference of patient displacement between AlignRT and CTOR was analyzed. For supine position, five patients had an average difference of displacement between AlignRT and CTOR along any direction (vertical, longitudinal, and lateral) greater than 0.5 cm, and one patient greater than 1 cm. Four patients had a maximum difference greater than 1 cm. For prone position, seven patients had an average difference greater than 0.5 cm, and three patients greater than 1 cm. Nine patients had a maximum difference greater than 1 cm. The difference of displacement between AlignRT and CTOR was greater for the prone position than for the supine position. For the patients studied here, surface matching does not appear to be an advisable image-guidance approach for pelvic radiation therapy for patients with either supine or prone position. There appears to be a potential for large alignment discrepancies (up to 2.25 cm) between surface matching and target matching.

La tomodensitométrie à faisceau conique (CBCT) est répandue à travers les centres de cancérologie pour le positionnement du patient avant chaque traitement de radiothérapie. Ces images pourraient potentiellement être utilisées pour des tâches plus complexes. En radio-oncologie, les techniques de planification adaptative sont en développement et nécessitent l'utilisation des images de tomodensitométrie synthétiques (sCT). Alors, plusieurs groupes de recherche ont proposé différentes techniques pour générer des images sCT à partir des données CBCT. L'objectif principale de ce projet est d'évaluer une nouvelle méthode d'apprentissage profond pour générer des images sCT de pelvis à partir des images CBCT. Onze patients ont été rétrospectivement étudiés. Chaque patient a été imagé en séquence par deux techniques d'imagerie volumétrique dans la même position, soit un scan au CBCT et un autre au CT sur rails (CTr). Afin de pouvoir utiliser les images synthétiques dans un contexte clinique, la qualité de l'image et l'impact dosimétrique entre les sCT et les CTr doivent être évalués. Les nombres CT des images sCT sont comparés à ceux des images CTr. Finalement, pour évaluer l'impact dosimétrique, les plans de traitement optimaux sont recalculés sur les images sCT et CTr. Les différences de dose sont évaluées à l'aide d'une analyse gamma et des histogrammes dose-volume. L'évaluation quantitative montre qu'il y a des différences statistiquement significatives dans les os et les cavités d'air. Tandis que, les différences des tissus adipeux et mous ne sont pas statistiquement significatives. Les doses estimées dans les organes à risque et les PTV à partir des données des sCT sont surestimées comparativement à celles calculées à partir des données des CTr. Cependant, les erreurs de doses sont inférieures à 2% pour la plupart des cas étudiés. Ces erreurs de doses sont probablement causées par le manque de tissus dans la périphérie du patient et les erreurs des nombres CT. Bien que les différences de doses soient cliniquement acceptable, la méthode proposée devrait temporairement être limitée aux validations quotidiennes de plans de traitement pour des cas pelviens. / The cone-beam computed tomography (CBCT) is widely spread in cancer centers for positioning the patient before their radiotherapy treatment. These images could potentially be used for more complex tasks. In radio-oncology, adaptive planning technics are in development and require the use of synthetic CT (sCT) images. So, multiple research groups proposed different methods to generate sCT images from CBCT data. The main purpose of this project is to assess a new deep-learning method to generate sCT images from CBCT images. Eleven patients were retrospectively studied. Each patient was subsequently imaged by two volumetric imaging methods in the same position, one CBCT scan and the other from CT on rails (CTr). In order to clinically use the synthetic images, image quality and dosimetric impact between sCT and CTr must be evaluated. sCT images' CT values are compared to those in CTr images. Finally, to evaluate the dosimetric impact, optimal treatment plans are recalculated with sCT and CTr images. Dose differences are assessed by gamma analysis and dose-volume histograms. The quantitative evaluation shows that differences are statistically different in bones and air cavities. As for adipose and soft tissues, differences were not statistically different. The estimated doses in organs-at-risk and PTVs from sCT data are overestimated compared to those from CTr data. However, dose errors are inferior to 2% in the majority of studied cases. These dose errors are most likely due to missing tissues on the outskirt of the patient and the errors of CT numbers. Although dose differences are clinically acceptable, the proposed method should temporarily be limited to daily validations of pelvic treatment plans.