Industrial Computed Tomography using Proximal Algorithm

Handle URI:
http://hdl.handle.net/10754/605208
Title:
Industrial Computed Tomography using Proximal Algorithm
Authors:
Zang, Guangming ( 0000-0003-3038-8985 )
Abstract:
In this thesis, we present ProxiSART, a flexible proximal framework for robust 3D cone beam tomographic reconstruction based on the Simultaneous Algebraic Reconstruction Technique (SART). We derive the proximal operator for the SART algorithm and use it for minimizing the data term in a proximal algorithm. We show the flexibility of the framework by plugging in different powerful regularizers, and show its robustness in achieving better reconstruction results in the presence of noise and using fewer projections. We compare our framework to state-of-the-art methods and existing popular software tomography reconstruction packages, on both synthetic and real datasets, and show superior reconstruction quality, especially from noisy data and a small number of projections.
Advisors:
Wonka, Peter ( 0000-0003-0627-9746 )
Committee Member:
Heidrich, Wolfgang ( 0000-0002-4227-8508 ) ; Sundaramoorthi, Ganesh ( 0000-0003-3471-6384 )
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science
Program:
Computer Science
Issue Date:
14-Apr-2016
Type:
Thesis
Appears in Collections:
Theses; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorWonka, Peteren
dc.contributor.authorZang, Guangmingen
dc.date.accessioned2016-04-14T07:13:26Zen
dc.date.available2016-04-14T07:13:26Zen
dc.date.issued2016-04-14en
dc.identifier.urihttp://hdl.handle.net/10754/605208en
dc.description.abstractIn this thesis, we present ProxiSART, a flexible proximal framework for robust 3D cone beam tomographic reconstruction based on the Simultaneous Algebraic Reconstruction Technique (SART). We derive the proximal operator for the SART algorithm and use it for minimizing the data term in a proximal algorithm. We show the flexibility of the framework by plugging in different powerful regularizers, and show its robustness in achieving better reconstruction results in the presence of noise and using fewer projections. We compare our framework to state-of-the-art methods and existing popular software tomography reconstruction packages, on both synthetic and real datasets, and show superior reconstruction quality, especially from noisy data and a small number of projections.en
dc.language.isoenen
dc.subjectX-Ray imagingen
dc.subjectcomputed tomographyen
dc.subject3D volumeen
dc.subjectvolume reconstructionen
dc.subjectoptimizationen
dc.subjectSARTen
dc.titleIndustrial Computed Tomography using Proximal Algorithmen
dc.typeThesisen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Scienceen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberHeidrich, Wolfgangen
dc.contributor.committeememberSundaramoorthi, Ganeshen
thesis.degree.disciplineComputer Scienceen
thesis.degree.nameMaster of Scienceen
dc.person.id133262en
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