中国物理B ›› 2016, Vol. 25 ›› Issue (3): 38701-038701.doi: 10.1088/1674-1056/25/3/038701
• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇 下一篇
Zhao Jin(金朝), Han-Ming Zhang(张瀚铭), Bin Yan(闫镔), Lei Li(李磊), Lin-Yuan Wang(王林元), Ai-Long Cai(蔡爱龙)
Zhao Jin(金朝), Han-Ming Zhang(张瀚铭), Bin Yan(闫镔), Lei Li(李磊), Lin-Yuan Wang(王林元), Ai-Long Cai(蔡爱龙)
摘要: Sparse-view x-ray computed tomography (CT) imaging is an interesting topic in CT field and can efficiently decrease radiation dose. Compared with spatial reconstruction, a Fourier-based algorithm has advantages in reconstruction speed and memory usage. A novel Fourier-based iterative reconstruction technique that utilizes non-uniform fast Fourier transform (NUFFT) is presented in this work along with advanced total variation (TV) regularization for a fan sparse-view CT. The proposition of a selective matrix contributes to improve reconstruction quality. The new method employs the NUFFT and its adjoin to iterate back and forth between the Fourier and image space. The performance of the proposed algorithm is demonstrated through a series of digital simulations and experimental phantom studies. Results of the proposed algorithm are compared with those of existing TV-regularized techniques based on compressed sensing method, as well as basic algebraic reconstruction technique. Compared with the existing TV-regularized techniques, the proposed Fourier-based technique significantly improves convergence rate and reduces memory allocation, respectively.
中图分类号: (X-ray imaging)