Abstract
Sparse-view x-ray computed tomography (CT) imaging still is an interesting topic in CT field. In this paper, a new iterative image reconstruction approach for sparse-view CT with a normal-dose image was presented. The proposed cost-function which is under the criteria of penalized weighed least-square (PWLS) for CT image reconstruction mainly contains two terms, i.e., fidelity term and prior term. For the fidelity term, the weights of weighed least-square term are determined by considering the relationship between the variance and mean of the projection data in the presence of electronic background noise. For the prior term, a normal-dose image induced total variation (ndiTV) prior is proposed as an extension of the PICCS algorithm introduced by Chen et al 2008, which can relieve the requirement of misalignment reduction of the PICCS algorithm. For simplicity, the present approach is referred to as “PWLS-ndiTV”. Qualitative and quantitative evaluations were carried out on the present PWLS-ndiTV approach. Experimental results show that the present PWLS-ndiTV approach can achieve significant gains than the existing similar methods in noise and artifacts suppression.
Highlights
Radiation risk in x-ray computed tomography (CT) examinations has caused significant concerns to patients due to the negative effects of x-ray exposure [1,2]
Overview of the present normal-dose image induced total variation (ndiTV) prior Inspired by the prior image constrained compressed sensing (PICCS) algorithm introduced by Chen et al [15], in this paper, we propose a ndiTV prior by incorporating the normal-dose image induced non-local means (ndiNLM) filter proposed by Ma et al [38], which is expressed as follows: RndiTV(m)~aTV(m{mndiNLM)z(1{a)TV(m) where a [ 1⁄20,1 is a scalar factor and TV(:) denotes the total variation operator and is defined as follows: X qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
It can be clearly seen that the penalized weighed least-square (PWLS)-ndiTV achieves remarkable gains than the PWLSPICCS in terms of maintaining the structure information of ROI
Summary
Radiation risk in x-ray computed tomography (CT) examinations has caused significant concerns to patients due to the negative effects of x-ray exposure [1,2]. It is known that lowering the milliampere-seconds (mAs) [9,10,11,12,13] or reducing the number of projections per rotation around the body [14,15,16,17,18,19,20] is an important means for reducing radiation dose. Due to insufficient sampling with sparse-view measurements, conventional filtered back-projection (FBP) approach cannot yield highdiagnostic image quality. To address this problem, Sidky et al [22] formulated an innovative algorithm based on projection onto convex sets (POCS), called TV-POCS, by adapting total variation (TV) minimization of the desired-image with piecewise constant assumption.
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