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 , 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.
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