CBCType

CBCT imaging has become an integral component of photon radiotherapy devices. However, its image quality is restricted in its ability to faacilitate precise patient positioning. The primary objective of the project is to bridge the gap toward achieving image quality comparable to planning CTs, thereby enabling on-couch treatment plan adaptation based solely on CBCTs. 

In this Varian-funded project the team investigates the impact of typical image quality shortcomings of CBCT on the degradation of plan quality. Additionally, generative deep learning models are employed to generate synthetic CTs from the lower-quality CBCT images. 

Team: Goran Stanic(DKFZ), Kristina Giske (DKFZ), Niklas Wahl (DKFZ), Florian Ebert (DKFZ), Oliver Jäkel (DKFZ)

Collaborators: Fabian Weykamp (UKHD, KKE DKFZ), Bálint Kovács (MIC DKFZ)