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 <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> (viz., normalized table translation distance per gantry rotation) is lower than 1.5. When <inline-formula> <tex-math notation="LaTeX">${p}>{1.5}$ </tex-math></inline-formula>, 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 <inline-formula> <tex-math notation="LaTeX">${p}={4}$ </tex-math></inline-formula> without significant artifacts for single source MDCT scans.