Abstract
To realize low-dose imaging in X-ray computed tomography (CT) examination, lowering milliampere-seconds (low-mAs) or reducing the required number of projection views (sparse-view) per rotation around the body has been widely studied as an easy and effective approach. In this study, we are focusing on low-dose CT image reconstruction from the sinograms acquired with a combined low-mAs and sparse-view protocol and propose a two-step image reconstruction strategy. Specifically, to suppress significant statistical noise in the noisy and insufficient sinograms, an adaptive sinogram restoration (ASR) method is first proposed with consideration of the statistical property of sinogram data, and then to further acquire a high-quality image, a total variation based projection onto convex sets (TV-POCS) method is adopted with a slight modification. For simplicity, the present reconstruction strategy was termed as "ASR-TV-POCS." To evaluate the present ASR-TV-POCS method, both qualitative and quantitative studies were performed on a physical phantom. Experimental results have demonstrated that the present ASR-TV-POCS method can achieve promising gains over other existing methods in terms of the noise reduction, contrast-to-noise ratio, and edge detail preservation.
Highlights
X-ray computed tomography (CT) has been widely used for clinical diagnosis and imageguided interventions over the past decades
It can be observed that the images reconstructed by the adaptive sinogram restoration (ASR)-filtered back-projection (FBP), total variation based projection onto convex sets (TV-projection onto convex sets (POCS)) and ASR-total variation (TV)-POCS algorithms are visually better than those of FBP in all cases
With the projection-views decreasing, the artifacts induced by the insufficient angular sampling would weaken the effect on artifacts suppression, the noise reduction by ASR-FBP remains appreciable
Summary
X-ray computed tomography (CT) has been widely used for clinical diagnosis and imageguided interventions over the past decades. An alternative strategy models the noise property of measurements by using an objective function in the projection- or sinogramdomain, achieves optimal sinogram data estimation, and performs analytical image reconstruction via the conventional filtered back-projection (FBP) method [10,11,12,13]. The penalized Poisson likelihood (PL) and PWLS objective functions for projection data preprocessing were built based on the assumptions of Poisson and Gaussian statistical characteristics of projection and sinogram data, respectively [10, 12]. These statistical-based low-mAs image reconstruction strategies have remarkable gains in achieving a superior noise-resolution tradeoff over analytical reconstruction techniques. There are very few literatures for discussing CT image reconstruction with a combined low-mAs and sparse-view protocol in detail
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