Edge-Preserving PET Image Reconstruction Using Trust Optimization Transfer

作     者：Wang, Guobao Qi, Jinyi 

作者机构：Univ Calif Davis Dept Biomed Engn Davis CA 95616 USA 

出 版 物：《IEEE TRANSACTIONS ON MEDICAL IMAGING》 (IEEE Trans. Med. Imaging)

年 卷 期：2015年第34卷第4期

页      面：930-939页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 1009[医学-特种医学] 

基　　金：National Institutes of Health (NIH) [R01EB00194, R01EB014894] GE Healthcare 

主　　题：Edge-preserving regularization image reconstruction optimization algorithm optimization transfer positron emission tomography (PET) 

摘      要：Iterative image reconstruction for positron emission tomography can improve image quality by using spatial regularization. The most commonly used quadratic penalty often oversmoothes sharp edges and fine features in reconstructed images, while nonquadratic penalties can preserve edges and achieve higher contrast recovery. Existing optimization algorithms such as the expectation maximization (EM) and preconditioned conjugate gradient (PCG) algorithms work well for the quadratic penalty, but are less efficient for high-curvature or nonsmooth edge-preserving regularizations. This paper proposes a new algorithm to accelerate edge-preserving image reconstruction by using two strategies: trust surrogate and optimization transfer descent. Trust surrogate approximates the original penalty by a smoother function at each iteration, but guarantees the algorithm to descend monotonically;Optimization transfer descent accelerates a conventional optimization transfer algorithm by using conjugate gradient and line search. Results of computer simulations and real 3-D data show that the proposed algorithm converges much faster than the conventional EM and PCG for smooth edge-preserving regularization and can also be more efficient than the current state-of-art algorithms for the nonsmooth l(1) regularization.