| Literature DB >> 34029189 |
John W Hayes, Juan Montoya, Adam Budde, Chengzhu Zhang, Yinsheng Li, Ke Lia, Jiang Hsieh, Guang-Hong Chen.
Abstract
To avoid severe limited-view artifacts in reconstructed CT images, current multi-row detector CT (MDCT) scanners with a single x-ray source-detector assembly need to limit table translation speeds such that the pitch p (viz., normalized table translation distance per gantry rotation) is lower than 1.5. When p > 1.5, it remains an open question whether one can reconstruct clinically useful helical CT images without severe artifacts. In this work, we show that a synergistic use of advanced techniques in conventional helical filtered backprojection, compressed sensing, and more recent deep learning methods can be properly integrated to enable accurate reconstruction up to p = 4 without significant artifacts for single source MDCT scans.Entities:
Year: 2021 PMID: 34029189 DOI: 10.1109/TMI.2021.3083210
Source DB: PubMed Journal: IEEE Trans Med Imaging ISSN: 0278-0062 Impact factor: 10.048