Image gradient L₀-norm based PICCS for swinging multi-source CT reconstruction

Abstract

Dynamic computed tomography (CT) is usually employed to image motion objects, such as beating heart, coronary artery and cerebral perfusion, etc. Recently, to further improve the temporal resolution for aperiodic industrial process imaging, the swinging multi-source CT (SMCT) systems and the corresponding swinging multi-source prior image constrained compressed sensing (SM-PICCS) method were developed. Since the SM-PICCS uses the L 1 -norm of image gradient, the edge structures in the reconstructed images are blurred and motion artifacts are still present. Inspired by the advantages in terms of image edge preservation and fine structure recovering, the L 0 -norm of image gradient is incorporated into the prior image constrained compressed sensing, leading to an L 0 -PICCS algorithm. The experimental results confirm that the L 0 -PICCS outperforms the SM-PICCS in both visual inspection and quantitative analysis.2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement Recently, a swinging multi-source CT (SMCT) scheme was proposed for aperiodic dynamic imaging. This system consists of Q ( Q is an odd integer) X-ray source/detector pairs, and they are distributed uniformly along a circumference. Different from other multi-source CT systems in medical applications [1,7-9], the SMCT system adopts the bearing technique rather than the complicated slip ring. The bearing has advantages in terms of concise and precise structure, easy manufacture and low price, and it is suitable for large-scale serial

Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction

“…In the numerical tests, a digital thorax mouse phantom with 1.2% injected iodine contrast agent (Figure 2) (41) is employed. A polychromatic 50 kVp X-ray source is assumed, and its spectrum is divided into eight energy bins: [16,22), [22,25), [25,28), [28,31), [31,34), [34,37), [37,41), and [41,50) keV. A total of 640 projections are uniformly collected in an equidistant fan-beam geometry, where the distances from the X-ray source to PCD and object are set as 180 and 132 mm, respectively.…”

“…To tackle this issue, Wang et al adopted a high-quality full-spectrum prior image as a supervision information to optimize the multi-energy CT reconstruction images ( 14 ). Meanwhile, to obtain narrow energy bin images with lower noise, Yu et al applied the prior image constrained compressed sensing (PICCS) framework to photon-counting CT, generating the spectral PICCS (SPICCS) ( 15 , 16 ). Although these channel-wise reconstruction methods exploit both local sparsity and the correlation priors to a certain degree, they take a great deal of time to optimize the parameters in practice due to the large number of energy bins.…”

Spectral CT Reconstruction Based on PICCS and Dictionary Learning

“…As a nondestructive imaging technology, X-ray computed tomography (CT) has been widely used in clinical diagnosis, industrial testing and safety inspection since it was innovated in 1971 [1][2][3]. However, the traditional CT detectors integrate photon energies in the range of whole spectrum, and they ignore energy dependent information.…”

A sequential regularization based image reconstruction method for limited-angle spectral CT

“…On the one hand, anisotropic TV constrains the sparsity of reconstructed image in appointed directions, and leads to higher image quality than its TV counterpart in limited-angle (Chen et al 2013) and exterior problem (Guo et al 2017) CT reconstruction. On the other hand, l 0 gradient sparsity can suppress artifacts and preserve edges in limited-angle (Yu and Zeng 2015, Yu et al 2017a, Xu et al 2019, few view (Yu et al 2017b) and swinging multi-source (Yu et al 2019) CT reconstruction. Even though the above regularization terms are developed for standard CT, they can be generalized to spectral CT as well.…”