Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior

Abstract

X-ray computed tomography (CT) iterative image reconstruction from sparse-view projection data has been an important research topic for radiation reduction in clinic. In this paper, to relieve the requirement of misalignment reduction operation of the prior image constrained compressed sensing (PICCS) approach introduced by Chen et al, we present an iterative image reconstruction approach for sparse-view CT using a normal-dose image induced total variation (ndiTV) prior. The associative objective function of the present approach is constructed under the penalized weighed least-square (PWLS) criteria, which contains two terms, i.e., the weighted least-square (WLS) fidelity and the ndiTV prior, and is referred to as ''PWLS-ndiTV''. Specifically, the WLS fidelity term is built based on an accurate relationship between the variance and mean of projection data in the presence of electronic background noise. The ndiTV prior term is designed to reduce the influence of the misalignment between the desired-and prior-image by using a normal-dose image induced non-local means (ndiNLM) filter. Subsequently, a modified steepest descent algorithm is adopted to minimize the associative objective function. Experimental results on two different digital phantoms and an anthropomorphic torso phantom show that the present PWLS-ndiTV approach for sparse-view CT image reconstruction can achieve noticeable gains over the existing similar approaches in terms of noise reduction, resolution-noise tradeoff, and low-contrast object detection.

Balanced Sparse Model for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

“…However, orthogonal wavelets may lead to blocky artifacts in image reconstruction [ 14 – 17 ]. On the contrary, redundant wavelets, such as X-let [ 18 – 24 ] and others [ 25 – 31 ], can significantly improve the image quality [ 14 – 17 ]. Some of these transforms, e.g.…”

Sparse-view tomography via displacement function interpolation

“…The study results using the patient CT data are shown in Figs. 7,8,9,10,11,12. In this study set, measurements from 1200 views over 360°are considered as a full sinogram, and measurements from 400 views over 360°are considered as an under-sampled sinogram. The detector had 896 bins for each view.…”

“…Many systematic studies lead to more efficient sampling criteria [4][5][6][7][8], where more complicated two-dimensional interpolations are discussed. According to compressed sensing theory, sparse-view tomography may still be possible if some image domain constraints are used to compensate for the missing data [9][10][11][12][13][14][15]. However, this paper focuses on analytic filtered backprojection (FBP) reconstruction.…”

Sparse-View Computed Tomography Reconstruction Using an Improved Non-Local Means

“…More reconstruction methods based on NLM have been studied since then Refs. [11][12][13]. However, reconstruction methods using NLM often lead to over-smoothed images.…”

“…5 To solve this problem, many iterative methods have been studied for a long time. [6][7][8][9][10][11][12][13] Among them, total variation (TV) based methods [6][7][8][9] have reported overwhelming success in few-view CT reconstruction. The idea of TV is based on the fact that medical images could be sparsified by taking the gradient transform and the reconstruction could be optimized by solving a constrained minimization problem.…”