Computed tomography(CT) plays an important role in the field of modern medical imaging. Reducing radiation exposure dose without significantly decreasing image's quality is always a crucial issue. Inspired by the ...Computed tomography(CT) plays an important role in the field of modern medical imaging. Reducing radiation exposure dose without significantly decreasing image's quality is always a crucial issue. Inspired by the outstanding performance of total variation(TV) technique in CT image reconstruction, a TV regularization based Bayesian-MAP(MAP-TV) is proposed to reconstruct the case of sparse view projection and limited angle range imaging. This method can suppress the streak artifacts and geometrical deformation while preserving image edges. We used ordered subset(OS) technique to accelerate the reconstruction speed. Numerical results show that MAP-TV is able to reconstruct a phantom with better visual performance and quantitative evaluation than classical FBP,MLEM and quadrate prior to MAP algorithms. The proposed algorithm can be generalized to cone-beam CT image reconstruction.展开更多
基金National Natural Science Foundation of Chinagrant number:30970866+3 种基金Guangzhou Municipal Science and Technology Projectgrant number:llBppZLjj2120029Guangdong Strategic Emerging Industry Core Technology Researchgrant number:2011A081402003
文摘Computed tomography(CT) plays an important role in the field of modern medical imaging. Reducing radiation exposure dose without significantly decreasing image's quality is always a crucial issue. Inspired by the outstanding performance of total variation(TV) technique in CT image reconstruction, a TV regularization based Bayesian-MAP(MAP-TV) is proposed to reconstruct the case of sparse view projection and limited angle range imaging. This method can suppress the streak artifacts and geometrical deformation while preserving image edges. We used ordered subset(OS) technique to accelerate the reconstruction speed. Numerical results show that MAP-TV is able to reconstruct a phantom with better visual performance and quantitative evaluation than classical FBP,MLEM and quadrate prior to MAP algorithms. The proposed algorithm can be generalized to cone-beam CT image reconstruction.
