Improving iterative 4D CBCT through the use of motion information
Résumé
In Image-Guided RadioTherapy (IGRT) of lung tumors, patients undergo a 4D CT, on the basis of which their treatment is planned. It is implicitely assumed that their breathing motion will not change much throughout the treatment, and remain close to what it was during the 4D CT acquisition. During the treatment, several cone beam CT acquisitions are performed, and used to re-position the patient. Obtaining a 4D reconstruction from this cone beam data would allow the therapists to check whether the breathing motion of the day still matches that of the planning CT, and if not, take appropriate corrective actions. Unfortunately, most tomography methods currently available are inadequate for such a task: static 3D reconstructions are pointless for motion assessment, respirationcorrelated reconstructions are affected by streak artifacts, and regularization techniques only bring limited improvement. Recently, regularized 4D methods have been proposed, in which the whole respiratory cycle is reconstructed at once. As these methods allow to explicitely enforce similarity between consecutive frames, they considerably improve image quality. In the case of IGRT, the motion information extracted from the 4D planning CT can be used to further improve the 4D reconstruction results. We describe a recent 4D reconstruction method (ROOSTER), propose its motion-compensated counterpart (MC-ROOSTER), and compare their results.