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Iterative Reconstruction for QuantitativeTissue Decomposition in Dual-Energy CT
Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Health Sciences. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9072-2204
Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.ORCID iD: 0000-0003-1257-2383
Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL.ORCID iD: 0000-0003-0209-498X
2011 (English)In: Proceedings of the 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011., Springer Berlin/Heidelberg, 2011, 479-488 p.Conference paper, Published paper (Refereed)
Abstract [en]

Quantitative tissue classification using dual-energy CT has the potential to improve accuracy in radiation therapy dose planning as it provides more information about material composition of scanned objects than the currently used methods based on single-energy CT. One problem that hinders successful application of both single-and dualenergy CT is the presence of beam hardening and scatter artifacts in reconstructed data. Current pre-and post-correction methods used for image reconstruction often bias CT numbers and thus limit their applicability for quantitative tissue classification. Here we demonstrate simulation studies with a novel iterative algorithm that decomposes every soft tissue voxel into three base materials: water, protein and adipose. The results demonstrate that beam hardening artifacts can effectively be removed and accurate estimation of mass fractions of all base materials can be achieved. In the future, the algorithm may be developed further to include segmentation of soft and bone tissue and subsequent bone decomposition, extension from 2-D to 3-D and scatter correction.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2011. 479-488 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 6688
Keyword [en]
Iterative reconstruction, Dual energy CT, Tissue classifica
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-75345DOI: 10.1007/978-3-642-21227-7_45ISBN: 978-3-642-21226-0 (print)ISBN: 978-3-642-21227-7 (print)OAI: oai:DiVA.org:liu-75345DiVA: diva2:506005
Conference
Proceedings 17th Scandinavian Conference on Image Analysis (SCIA) 2011, May 23-27, Ystad, Sweden
Available from: 2012-03-01 Created: 2012-02-27 Last updated: 2015-03-20Bibliographically approved

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Magnusson, MariaMalusek, AlexandrMuhammad, ArifAlm Carlsson, Gudrun
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Radiation PhysicsComputer VisionFaculty of Health SciencesThe Institute of TechnologyDepartment of Radiation Physics UHL
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