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Patient respiratory signal associated with the cone beam CT (CBCT)projections is important for lung cancer radiotherapy. In contrast tomonitoring an external surrogate of respiration, such signal can be extracteddirectly from the CBCT projections. In this paper, we propose a novel localprinciple component analysis (LPCA) method to extract the respiratory signal bydistinguishing the respiration motion-induced content change from the gantryrotation-induced content change in the CBCT projections. The LPCA method isevaluated by comparing with three state-of-the-art projection-based methods,namely, the Amsterdam Shroud (AS) method, the intensity analysis (IA) method,and the Fourier-transform based phase analysis (FT-p) method. The clinical CBCTprojection data of eight patients, acquired under various clinical scenarios,were used to investigate the performance of each method. We found that theproposed LPCA method has demonstrated the best overall performance for casestested and thus is a promising technique for extracting respiratory signal. Wealso identified the applicability of each existing method.

Extracting respiratory signals from thoracic cone beam CT projections