Comparative Evaluation of Conventional and Deep Learning Methods for Respiratory Signal Extraction From Clinical 3D CBCT Projections.

IntroductionRecent advances in deep learning have significantly improved the ability to solve ill-posed problems, making 4D cone-beam CT (CBCT) reconstruction from projections of 3D CBCT imaging achievable. However, extracting respiratory signal from CBCT projections for 4D CBCT phase sorting remains a challenge. This study aims to evaluate conventional and deep learning methods for extracting respiratory signal from projections of clinical 3D CBCT imaging.MethodsThis study analyzed 70 sets of projections from clinical 3D CBCT imaging, involving thoracic and abdominal cancer patients with regular and irregular respiratory motion patterns. Using the labeled apex of the diaphragm as a reference, respiratory signals extracted using conventional methods-including intensity analysis (IA), Fourier transform (FT), Amsterdam Shroud (AS), and local principal component analysis (LPCA)-as well as a deep learning-based method (U-Net) were compared through correlation analysis and phase-sorting capability.ResultsThe U-Net significantly outperformed the conventional methods across varying conditions, achieving a correlation coefficient of 0.93 ± 0.07. Among the conventional methods, LPCA and AS outperformed IA and FT, with LPCA is considered superior because the AS method is influenced by the cutoff frequencies of the bandpass filter.ConclusionThe U-Net demonstrates superiority in extracting respiratory signals from clinical 3D CBCT projections, highlighting its potential to enhance respiratory phase sorting and 4D CBCT reconstruction.