基于低秩矩阵分解的CT图像重建.pdfVIP

Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study Jian-Feng Cai∗ Xun Jia† Hao Gao‡ Steve B. Jiang§ Zuowei Shen¶ Hongkai Zhao Abstract Respiration-correlated CBCT, commonly called 4DCBCT, provides respiratory phase-resolved CBCT images. A typical 4DCBCT consists of a small number (usually 10 or less) of CBCT images, each corresponding to a particular breathing phase and reconstructed using all projections falling in that phase bin over the 4DCBCT scan. Therefore, 4DCBCT represents averaged patient images over one breathing cycle and the 4th dimension is actually breathing phase instead of time. In many clinical applications, it is desirable to obtain true 4DCBCT with the 4th dimension being time, i.e., each constituent CBCT image corresponds to an instantaneous projection. Theoretically it is impossible to reconstruct a CBCT image from a single projection. However, if all the constituent CBCT images of a 4DCBCT scan share a lot of redundant information and can be represented with or approximated by linear combinations of a small number of basis images, it might be possible to make a good reconstruction of these images by exploring sparsity and coherence/redundancy of these CBCT images. These CBCT images are not completely time resolved since they are represented by a small number of basis images, but they can exploit both local and global temporal coherence of the patient anatomy automatically and contain much more temporal variation information of the patient geometry than the conventional 4DCBCT. We propose in this work a computational model and algorit