Globally Convergent Ordered Subsets Algorithms: Application to Tomography
نویسندگان
چکیده
We present new algorithms for penalized-likelihood image reconstruction: modified BSREM (block sequential regularized expectation maximization) and relaxed OS-SPS (ordered subsets separable paraboloidal surrogates). Both of them are globally convergent to the unique solution, easily incorporate convex penalty functions, and are parallelizable—updating all voxels (or pixels) simultaneously. They belong to a class of relaxed ordered subsets algorithms. We modify the scaling function of the existing BSREM (De Pierro and Yamagishi, 01) so that we can prove global convergence without previously imposed assumptions. We also introduce a diminishing relaxation parameter into the existing OS-SPS (Erdoğan and Fessler, 99) to achieve global convergence. We also modify the penalized-likelihood function to enable the algorithms to cover a zerobackground-event case. Simulation results show that the algorithms are both globally convergent and fast.
منابع مشابه
Fast Globally Convergent Reconstruction in Emission Tomography Using COSEM, an Incremental EM Algorithm
We present globally convergent incremental EM algorithms for reconstruction in emission tomography, COSEMML for maximum likelihood and COSEM-MAP for maximum a posteriori reconstruction. The COSEM (Complete data Ordered Subsets Expectation Maximization) algorithms use ordered subsets (OS) for fast convergence, but unlike other globally convergent OS-based ML and MAP algorithms such as RAMLA (Bro...
متن کاملAn accelerated convergent ordered subsets algorithm for emission tomography.
We propose an algorithm, E-COSEM (enhanced complete-data ordered subsets expectation-maximization), for fast maximum likelihood (ML) reconstruction in emission tomography. E-COSEM is founded on an incremental EM approach. Unlike the familiar OSEM (ordered subsets EM) algorithm which is not convergent, we show that E-COSEM converges to the ML solution. Alternatives to the OSEM include RAMLA, and...
متن کاملAn overview of fast convergent ordered-subsets reconstruction methods for emission tomography based on the incremental EM algorithm.
Statistical reconstruction has become popular in emission computed tomography but suffers slow convergence (to the MAP or ML solution). Methods proposed to address this problem include the fast but non-convergent OSEM and the convergent RAMLA [1] for the ML case, and the convergent BSREM [2], relaxed OS-SPS and modified BSREM [3] for the MAP case. The convergent algorithms required a user-deter...
متن کاملAccelerated image reconstruction using ordered subsets of projection data
The authors define ordered subset processing for standard algorithms (such as expectation maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass through all the subsets, in each subset using the current estimate to initialize applicatio...
متن کاملOn the efficiency of iterative ordered subset reconstruction algorithms for acceleration on GPUs
Expectation Maximization (EM) and the Simultaneous Iterative Reconstruction Technique (SIRT) are two iterative computed tomography reconstruction algorithms often used when the data contain a high amount of statistical noise, have been acquired from a limited angular range, or have a limited number of views. A popular mechanism to increase the rate of convergence of these types of algorithms ha...
متن کامل