Noise-robust Mojette reconstruction using sparse-view CT projections.
Sparse-view Computed Tomography (CT) has important significance in industrial inspection and medical diagnosis. Mojette transform is a kind of discrete Radon transform that can yield exact reconstructions instead of an approximate solution due to finite Radon sampling. However, the image is iteratively reconstructed pixel by pixel from corner to center, and the image error is proportional to the number of iterations. In this paper, we propose that there exist different sets of projection combinations to recover the original image within the close-to-minimal iterations. And a scheme is given to obtain multiple projection sets, each of which has the same number of minimum iterations and can recover a CT image with a similar level of small noise but different distributions. These images can be used further to restore the final CT image by counteracting noise with each other. The accuracy and validity of the proposed algorithm are verified by comparison with both other Mojette inversion algorithms and the classical SART algorithm.
['Jiang|Min|M|', 'Qu|Zhiping|Z|', 'Sun|Yi|Y|']
D000465:Algorithms D006225:Hand D006257:Head D006801:Humans D007091:Image Processing, Computer-Assisted D019047:Phantoms, Imaging D059629:Signal-To-Noise Ratio D014057:Tomography, X-Ray Computed
Mojette transform Radon transform different sets of projection combinations close-to-minimal iterations counteracting-noise