Decoupling Respiratory and Angular Variation in Rotational X-ray Scans Using a Prior Bilinear Model
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Data-driven respiratory signal extraction from rotational X-ray scans has always been challenging due to angular effects overlapping with respiration-induced change in the scene. In the context of motion modelling their main drawback is the fact that most of these methods only extract a 1D signal, that, at best, can be decomposed into amplitude and phase. In this paper, we use the linearity of the Radon operator to propose a bilinear model based on a prior 4D scan to separate angular and respiratory variation. Out-of-sample extension is enhanced by a B-spline interpolation using prior knowledge about the trajectory angle to extract respiratory feature weights independent of the acquisition angle. Though the prerequisite of a prior 4D scan seems steep, our proposed application of respiratory motion estimation in radiation therapy usually meets this requirement. We test our approach on DRRs of a patient's 4D CT in a leave-one-out manner and achieve a mean estimation error of 5.2
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