A Bayesian-MAP Method Based on TV for CT Image Reconstruction from Sparse and Limited Data
QI Hong-liang
ZHOU Ling-hong
XU Yuan
HONG Hong
摘要：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.
机标关键词：image reconstructionquantitative evaluationradiation exposuretotal variationlimited angle
分类号：TN911.7(通信)
论文发表日期：2017-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：7( 82-88 )
英文信息