Low-dose X-ray CT Image Reconstruction Method Based on Adaptive Total Variation

doi:10.19596/j.cnki.1001-246x.8639

Abstract

Abstract:

The radiation dose of X-ray CT is closely related to the abnormal metabolism of the living body and the induction of diseases such as cancer. At this stage, how to reduce the radiation dose of CT scans and ensure the quality of image reconstruction is a huge challenge for CT image reconstruction technology. The traditional total variation regularization method is not sensitive to the direction of the image. This paper introduces directional gradient information and establishes an adaptive directional total variation sparse angle CT image reconstruction model, which aims to better preserve image edges and improve image reconstruction quality. To adaptively estimate the direction information of the image gradient, based on the geometric moment of the image, an ellipse steerable kernel is used to estimate the main direction of the image edge block. Simulation and actual data results show that the proposed algorithm has good performance in image detail preservation, artifact reduction, and noise suppression.

Key words: low-dose, X-ray CT, sparse-view reconstruction, gradient direction, total variation

Zhaoyan QU, Ximing YAN, Xiaojing XI. Low-dose X-ray CT Image Reconstruction Method Based on Adaptive Total Variation[J]. Chinese Journal of Computational Physics, 2023, 40(5): 614-621.

Figures/Tables 9

Fig.1 Reconstruction results of TV algorithm (a) traditional TV; (b) TV gradient directions of 45°; (c) TV gradient directions of 100°; (d) TV gradient directions of 130°; (f) TV gradient directions of 160°; (f) adaptive TV

Fig.2 Adaptive gradient difference direction

Fig.3 Estimation of the direction of the house image block (a) house image; (b) left diagonal edge; (c) vertical edge; (d) right diagonal edge; (e) horizontal edge; (f) smooth area

Fig.4 Graph of directional gradient operator

Fig.5 Comparison of medical reconstruction results

Fig.6 PSNR of four different algorithms with the number of sparse views of (a) 30; (b) 60; (c) 90

Fig.7 Phantom, grayscale range [0, 0.035]

Fig.8 Image profifiles marked by green lines in Fig. 7 with the number of sparse views of (a) 30; (b) 60; (c) 90

Fig.9 Image profifiles marked by red lines in Fig. 7 with the number of sparse views of (a) 30; (b) 60; (c) 90

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