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Three material decomposition in dual energy CT for brachytherapy using the iterative image reconstruction algorithm DIRA: Performance of the method for an anthropomorphic phantom
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Brachytherapy is radiation therapy performed by placing a radiation source near or inside a tumor. Difference between the current water-based brachytherapy dose formalism (TG-43) and new model based dose calculation algorithms (MBSCAs) can differ by more than a factor of 10 in the calculated doses. There is a need for voxel-by-voxel cross-section assignment, ideally, both the tissue composition and mass density of every voxel should be known for individual patients.

A method for determining tissue composition via three material decomposition (3MD) from dual energy CT scans was developed at Linköping university. The method (named DIRA) is a model based iterative reconstruction algorithm that utilizes two photon energies for image reconstruction and 3MD for quantitative tissue classification of the reconstructed volumetric dataset.

This thesis has investigated the accuracy of the 3MD method applied on prostate tissue in an anthropomorphic phantom when using two different approximations of soft tissues in DIRA. Also the distributions of CT-numbers for soft tissues in a contemporary dual energy CT scanner have been determined. An investigation whether these distributions can be used for tissue classification of soft tissues via thresholding has been conducted.

It was found that the relative errors of mass energy absorption coefficient (MEAC) and linear attenuation coefficient (LAC) of the approximated mixture as functions of photon energy were less than 6 \% in the energy region from 1 keV to 1 MeV. This showed that DIRA performed well for the selected anthropomorphic phantom and that it was relatively insensitive to choice of base materials for the approximation of soft tissues.

The distributions of CT-numbers of liver, muscle and kidney tissues overlapped. For example a voxel containing muscle could be misclassified as liver in 42 cases of 100. This suggests that pure thresholding is insufficient as a method for tissue classification of soft tissues and that more advanced methods should be used.

Place, publisher, year, edition, pages
2013. , 51 p.
Keyword [en]
DECT, Iterative reconstruction, Tissue decomposition, Numerical stability, Uncertainty propagation
National Category
Medical Image Processing
URN: urn:nbn:se:liu:diva-91297ISRN: LiTH-IMT/BIT30-A-EX--13/510--SEOAI: diva2:616953
Subject / course
Medical Technology
2013-03-22, 10:00 (English)
Available from: 2013-04-23 Created: 2013-04-19 Last updated: 2013-04-23Bibliographically approved

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Westin, Robin
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