Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling.

ABSTRACT: Objective.Real-time imaging is highly desirable in image-guided radiotherapy, as it provides instantaneous knowledge of patients' anatomy and motion during treatments and enables online treatment adaptation to achieve the highest tumor targeting accuracy. Due to extremely limited acquisition time, only one or few x-ray projections can be acquired for real-time imaging, which poses a substantial challenge to localize the tumor from the scarce projections. For liver radiotherapy, such a challenge is further exacerbated by the diminished contrast between the tumor and the surrounding normal liver tissues. Here, we propose a framework combining graph neural network-based deep learning and biomechanical modeling to track liver tumor in real-time from a single onboard x-ray projection.

SUBMITTER: Shao HC 

PROVIDER: S-EPMC9233941 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature