Online Positron Emission Tomography by Online Portfolio Selection
Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Journal Volume
2020-May
Pages
1110-1114
Date Issued
2020
Author(s)
Abstract
The number of measurement outcomes in positron emission tomography (PET) is typically large, rendering signal reconstruction computationally expensive. We propose an online algorithm to address this computational issue. The per-iteration computational complexity of the proposed algorithm is independent of the number of measurement outcomes and linear√ in the signal dimension. The algorithm has a rigorous Oleft( {1/sqrt k } right) convergence rate guarantee, where k denotes the iteration counter. Numerical experiments on synthetic data-sets show that the algorithm can be significantly faster than expectation maximization and stochastic primal-dual hybrid gradient method. The proposed algorithm is based on an equivalent stochastic optimization formulation, the Soft-Bayes algorithm for online portfolio selection, and standard online-to-batch conversion. © 2020 IEEE.
Subjects
online portfolio selection; online-to-batch conversion; Positron emission tomography; Soft-Bayes; stochastic optimization
Other Subjects
Audio signal processing; Gradient methods; Maximum principle; Numerical methods; Optimization; Positrons; Signal reconstruction; Speech communication; Computational issues; Expectation - maximizations; Iteration counters; Numerical experiments; On-line algorithms; Positron emission tomography (PET); Stochastic optimizations; Synthetic datasets; Positron emission tomography
Type
conference paper