Decoupling Respiratory and Angular Variation in Rotational X-ray Scans Using a Prior Bilinear Model
This work addresses a specific problem in radiation therapy for medical imaging, enabling more accurate respiratory motion modeling during treatments with rotating gantries, though it is incremental as it builds on prior knowledge and existing methods.
The paper tackled the challenge of separating respiratory and angular variations in rotational X-ray scans by proposing a bilinear model based on prior 4D CT data, achieving a mean estimation error of 3.01% in gray values for unseen viewing angles.
Data-driven respiratory signal extraction from rotational X-ray scans is a challenge as angular effects overlap with respiration-induced change in the scene. In this paper, we use the linearity of the X-ray transform to propose a bilinear model based on a prior 4D scan to separate angular and respiratory variation. The bilinear estimation process is supported by a B-spline interpolation using prior knowledge about the trajectory angle. Consequently, extraction of respiratory features simplifies to a linear problem. Though the need for a prior 4D CT seems steep, our proposed use-case of driving a respiratory motion model in radiation therapy usually meets this requirement. We evaluate on DRRs of 5 patient 4D CTs in a leave-one-phase-out manner and achieve a mean estimation error of 3.01 % in the gray values for unseen viewing angles. We further demonstrate suitability of the extracted weights to drive a motion model for treatments with a continuously rotating gantry.