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A model-based iterative reconstruction algorithm DIRA using patient-specific tissue classification via DECT for improved quantitative CT in dose planning
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.ORCID iD: 0000-0003-1257-2383
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9072-2204
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0003-3352-8330
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0003-0209-498X
2017 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 44, no 6, 2345-2357 p.Article in journal (Refereed) Published
Abstract [en]

Purpose: To develop and evaluate-in a proof-of-concept configuration-a novel iterative reconstruction algorithm (DIRA) for quantitative determination of elemental composition of patient tissues for application to brachytherapy with low energy (amp;lt; 50 keV) photons and proton therapy. Methods: DIRA was designed as a model-based iterative reconstruction algorithm, which uses filtered backprojection, automatic segmentation and multimaterial tissue decomposition. The evaluation was done for a phantom derived from the voxelized ICRP 110 male phantom. Soft tissues were decomposed to the lipid, protein and water triplet, bones were decomposed to the compact bone and bone marrow doublet. Projections were derived using the Drasim simulation code for an axial scanning configuration resembling a typical DECT (dual-energy CT) scanner with 80 kV and Sn140 kV x-ray spectra. The iterative loop produced mono-energetic images at 50 and 88 keV without beam hardening artifacts. Different noise levels were considered: no noise, a typical noise level in diagnostic imaging and reduced noise level corresponding to tenfold higher doses. An uncertainty analysis of the results was performed using type A and B evaluations. The two approaches were compared. Results: Linear attenuation coefficients averaged over a region were obtained with relative errors less than 0.5% for all evaluated regions. Errors in average mass fractions of the three-material decomposition were less than 0.04 for no noise and reduced noise levels and less than 0.11 for the typical noise level. Mass fractions of individual pixels were strongly affected by noise, which slightly increased after the first iteration but subsequently stabilized. Estimates of uncertainties in mass fractions provided by the type B evaluation differed from the type A estimates by less than 1.5% for most cases. The algorithm was fast, the results converged after 5 iterations. The algorithmic complexity of forward polyenergetic projection calculation was much reduced by using material doublets and triplets. Conclusions: The simulations indicated that DIRA is capable of determining elemental composition of tissues, which are needed in brachytherapy with low energy (amp;lt; 50 keV) photons and proton therapy. The algorithm provided quantitative monoenergetic images with beam hardening artifacts removed. Its convergence was fast, image sharpness expressed via the modulation transfer function was maintained, and image noise did not increase with the number of iterations. c 2017 American Association of Physicists in Medicine

Place, publisher, year, edition, pages
WILEY , 2017. Vol. 44, no 6, 2345-2357 p.
Keyword [en]
quantitative dual energy computed tomography; tissue composition
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-140809DOI: 10.1002/mp.12238ISI: 000408033400028PubMedID: 28369941OAI: oai:DiVA.org:liu-140809DiVA: diva2:1140801
Note

Funding Agencies|Swedish Cancer Foundation [CAN 2012/764, CAN 2014/691]; ALF Grants Region Ostergotland [LiO-439731, LiO-528791, LiO-602731]; Medical Faculty at Linkoping University

Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2017-09-13

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Malusek, AlexandrMagnusson, MariaSandborg, MichaelAlm Carlsson, Gudrun
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Division of Radiological SciencesFaculty of Medicine and Health SciencesComputer VisionFaculty of Science & EngineeringCenter for Medical Image Science and Visualization (CMIV)Department of Radiation Physics
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Medical physics (Lancaster)
Medical Image Processing

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