Kasai, Ryosuke Tokushima University Tokushima University Educator and Researcher Directory
Yamaguchi, Yusaku Shikoku Medical Center for Children and Adults
Kojima, Takeshi Tokushima University Tokushima University Educator and Researcher Directory
Abou Al-Ola, Omar M. Tanta University
maximum-likelihood expectation-maximization method
continuous-time image reconstruction
Thesis or Dissertation
The problem of tomographic image reconstruction can be reduced to an optimization problem of finding unknown pixel values subject to minimizing the difference between the measured and forward projections. Iterative image reconstruction algorithms provide significant improvements over transform methods in computed tomography. In this paper, we present an extended class of power-divergence measures (PDMs), which includes a large set of distance and relative entropy measures, and propose an iterative reconstruction algorithm based on the extended PDM (EPDM) as an objective function for the optimization strategy. For this purpose, we introduce a system of nonlinear differential equations whose Lyapunov function is equivalent to the EPDM. Then, we derive an iterative formula by multiplicative discretization of the continuous-time system. Since the parameterized EPDM family includes the Kullback–Leibler divergence, the resulting iterative algorithm is a natural extension of the maximum-likelihood expectation-maximization (MLEM) method. We conducted image reconstruction experiments using noisy projection data and found that the proposed algorithm outperformed MLEM and could reconstruct high-quality images that were robust to measured noise by properly selecting parameters.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
|DOI (Published Version)|
|URL ( Publisher's Version )|
k3606_abstract.pdf 96.1 KB
k3606_review.pdf 147 KB
k3606_fulltext.pdf 2.1 MB
|MEXT report number||
Doctor of Health Science