Work

Superiorization of incremental optimization algorithms for statistical tomographic image reconstruction

Year: 2017

Type: article

Abstract: We propose the superiorization of incremental algorithms for tomographic image reconstruction. The resulting methods follow a better path in its way to finding the optimal solution for the maximum likelihood problem in the sense that they are closer to the Pareto optimal curve than the non-superiorized techniques. A new scaled gradient iteration is proposed and three superiorization schemes are evaluated. Theoretical analysis of the methods as well as computational experiments with both synthetic and real data are provided.

Source: Inverse Problems

DOI: 10.1088/1361-6420/33/4/044010

Authors: E S Helou, M V W Zibetti, E X Miqueles

Institutions: Universidade Estadual Paulista (Unesp), Universidade Tecnológica Federal do Paraná, Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials

Language: English

Topic: Medical Imaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funders: Fundação de Amparo à Pesquisa do Estado de São Paulo, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Laboratório Nacional de Luz Síncrotron