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Adaptive Discretization for Computerized Tomography
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Indian Inst Technol Kanpur, India.
Indian Inst Technol Kanpur, India.
Indian Inst Technol Roorkee, India.
2018 (English)In: Research in nondestructive evaluation (Print), ISSN 0934-9847, E-ISSN 1432-2110, Vol. 29, no 2, p. 78-94Article in journal (Refereed) Published
Abstract [en]

Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.

Place, publisher, year, edition, pages
TAYLOR & FRANCIS INC , 2018. Vol. 29, no 2, p. 78-94
Keywords [en]
Adaptive discretization; Bayesian statistics; Bergman space interpolation; convex optimization; limited data tomography
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-147457DOI: 10.1080/09349847.2016.1261212ISI: 000428786900002OAI: oai:DiVA.org:liu-147457DiVA, id: diva2:1206235
Available from: 2018-05-16 Created: 2018-05-16 Last updated: 2018-05-16

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Shakya, Snehlata
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