Edge-Preserving PET Image Reconstruction Using Trust Optimization Transfer

• Published in: IEEE transactions on medical imaging Vol. 34; no. 4; pp. 930 - 939
• Main Authors: Wang, Guobao, Qi, Jinyi
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2015
• Subjects: Algorithms; Animals; Approximation algorithms; Brain - diagnostic imaging; Convergence; Edge-preserving regularization; Humans; Image edge detection; Image Processing, Computer-Assisted - methods; Image reconstruction; Linear programming; Optimization; optimization algorithm; optimization transfer; Phantoms, Imaging; Positron emission tomography; positron emission tomography (PET); Positron-Emission Tomography - methods; Primates
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2014.2371392

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 l1 regularization.