반복적 연산으로 얻은 Sparse-view CT 영상에 대한 정량적 평가
Quantitative Evaluation of Sparse-view CT Images Obtained with Iterative Image Reconstruction Methods
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Sparse-view CT imaging is considered to be a solution to reduce x-ray dose of CT. Sparse-view CT imaging may have severe streak artifacts that could compromise the image qualities. We have compared quality of sparseview images reconstructed with two representative iterative reconstruction techniques, SIRT and TV-minimization,in terms of image error and edge preservation. In the comparison study, we have used the Shepp-Logan phantom image and real CT images obtained with a micro-CT. In both phantom image and real CT image tests, TV-minimization technique shows the best performance in error reduction and preserving edges. However, the excessive computation time of TV-minimization is a technical challenge for the practical use.
Abstract
Sparse-view CT imaging is considered to be a solution to reduce x-ray dose of CT. Sparse-view CT imaging may have severe streak artifacts that could compromise the image qualities. We have compared quality of sparseview images reconstructed with two representative iterative reconstruction techniques, SIRT and TV-minimization,in terms of image error and edge preservation. In the comparison study, we have used the Shepp-Logan phantom image and real CT images obtained with a micro-CT. In both phantom image and real CT image tests, TV-minimization technique shows the best performance in error reduction and preserving edges. However, the excessive computation time of TV-minimization is a technical challenge for the practical use.
참고문헌 (17)
- B.E. Nett, "Perfusion measurements by micro-CT using prior image constrained compressed sensing(PICCS) : Initial phantom results", Phys. Med. Biol, 55, pp.2333~2350 (2010)
- G.H Chen, "Prior image constrained compressed sensing(PICCS) : A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets", Med. Phys, 35(2), pp.660~663 (2008)
- M.H. Li, "An accurate iterative reconstruction algorithm for sparse objects: application to 3D blood vessel reconstruction from a limited number of projections", Phys. Med. Biol, 47, pp.2599~2609 (2002)
- J. Brian, "Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT", Phys. Med. Biol, 55, pp.6575~6599 (2010)
- Y. Zhang, "A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis", Med. Phys, 33, pp.3781~3795 (2006)
- E.Y. Sidky, "Image reconstruction in circular conebeam computed tomography by constrained, total-variation", Phys. Med. Biol, 53, pp.165~178 (2008)
- H. Yu, "A soft-threshold filtering approach for reconstruction from a limited number of projections", Phys. Med. Biol, 55, pp.3905~3916 (2010)
- J. Velikina, "Limited view angle tomographic image reconstruction via total variation minimization", Proc. Medical Imaging 2009: Physics of Medical Imaging, pp.7258~ (2009)
- J. Gregor, "Computational analysis and improvement of SIRT", IEEE. Trans. Med. Imag, 27(7), pp.918~924 (2008)
- M. Lustig, "Sparse MRI : The Application of Compressed Sensing for Rapid MR Imaging", Mag. Reson. Med, 58, pp.1182~1195 (2007)
- E.Y. Sidky, "Accurate image reconstruction from few-views and limited-angle data in divergentbeam CT", J. X-ray Sci. Techno, 14, pp.119~139 (2009)
- D. Mittal, "Enhancement of the ultrasound images by modified anisotropic diffusion method", Med. Biol. Eng. Comput, 48, pp.1281~1291 (2010)
- I.K. Chun, "Xray micro-tomography system for small-animal imaging with zoom-in imaging capability", Phys. Med. Biol, 49, pp.3889~3902 (2004)
- D.L. Donoho, "Compressed Sensing", IEEE Transactions on information theory, 52(4), pp.1289~1306 (2006)
- J. Tang, "Performance comparison between compressed sensing and statistical iterative reconstruction algorithms", Proc. Medical Imaging 2009: Physics of Medical Imaging (2009)
- A.C. Kak, "Principles of Computerized Tomographic Imaging", pp.275~295 (1999), IEEE Press
- X. Jia, "GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation", Med. Phys, 37, pp.1757~1760 (2010)