High‐quality initial image‐guided 4D CBCT reconstruction

Purpose Four‐dimensional cone‐beam computed tomography (4D CBCT) has been developed to provide a sequence of phase‐resolved reconstructions in image‐guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts because the 4D CBCT reconstruction process is an extreme sparse‐view CT procedure wherein only under‐sampled projections are used for the reconstruction of each phase. To obtain a set of 4D CBCT images achieving both high spatial and temporal resolution, we propose an algorithm by providing a high‐quality initial image at the beginning of the iterative reconstruction process for each phase to guide the final reconstructed result toward its optimal solution. Methods The proposed method consists of three steps to generate the initial image. First, a prior image is obtained by an iterative reconstruction method using the measured projections of the entire set of 4D CBCT images. The prior image clearly shows the appearance of structures in static regions, although it contains blurring artifacts in motion regions. Second, the robust principal component analysis (RPCA) model is adopted to extract the motion components corresponding to each phase‐resolved image. Third, a set of initial images are produced by the proposed linear estimation model that combines the prior image and the RPCA‐decomposed motion components. The final 4D CBCT images are derived from the simultaneous algebraic reconstruction technique (SART) equipped with the initial images. Qualitative and quantitative evaluations were performed by using two extended cardiac‐torso (XCAT) phantoms and two sets of patient data. Several state‐of‐the‐art 4D CBCT algorithms were performed for comparison to validate the performance of the proposed method. Results The image quality of phase‐resolved images is greatly improved by the proposed method in both phantom and patient studies. The results show an outstanding spatial resolution, in which streaking artifacts are suppressed to a large extent, while detailed structures such as tumors and blood vessels are well restored. Meanwhile, the proposed method depicts a high temporal resolution with a distinct respiratory motion change at different phases. For simulation phantom, quantitative evaluations of the simulation data indicate that an average of 36.72% decrease at EI phase and 42% decrease at EE phase in terms of root‐mean‐square error (RMSE) are achieved by our method when comparing with PICCS algorithm in Phantom 1 and