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Visual grading regression with random effects
Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-7750-1917
Linköping University, Department of Clinical and Experimental Medicine, Occupational and Environmental Medicine. Linköping University, Faculty of Health Sciences.
Linköping University, Department of Medical and Health Sciences, Radiology. 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, 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
2012 (English)In: MEDICAL IMAGING 2012: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, SPIE - International Society for Optical Engineering, 2012, Vol. 8318, Art. no. 831805- p.Conference paper, Published paper (Refereed)
Abstract [en]

To analyze visual grading experiments, ordinal logistic regression (here called visual grading regression, VGR) may be used in the statistical analysis. In addition to types of imaging or post-processing, the VGR model may include factors such as patient and observer identity, which should be treated as random effects. Standard software does not allow random factors in ordinal logistic regression, but using Generalized Linear Latent And Mixed Models (GLLAMM) this is possible. In a single-image study, 9 radiologists graded 24 cardiac Computed Tomography Angiography (CTA) images with reduced dose without and after post-processing with a 2D adaptive filter, using five image quality criteria. First, standard ordinal logistic regression was carried out, treating filtering, patient and observer identity as fixed effects. The same analysis was then repeated with GLLAMM, treating filtering as a fixed effect and patient and observer identity as random effects. With both approaches, a significant effect (pless than0.01) of the filtering was found for all five criteria. No dramatic differences in parameter estimates or significance levels were found between the two approaches. It is concluded that random effects can be appropriately handled in VGR using GLLAMM, but no major differences in the results were found in a preliminary evaluation.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2012. Vol. 8318, Art. no. 831805- p.
Series
Proceedings of SPIE, ISSN 0277-786X ; Vol. 8318
Keyword [en]
Image quality; visual grading; post-processing; filtering; ordinal logistic regression; random effects; Generalized Linear Latent And Mixed Models
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-79843DOI: 10.1117/12.913650ISI: 000304905600004ISBN: 978-0-8194-8967-8 (print)OAI: oai:DiVA.org:liu-79843DiVA: diva2:544373
Conference
Conference on Medical Imaging - Image Perception, Observer Performance, and Technology Assessment, San Diego, CA, USA, FEB 08-09, 2012
Available from: 2012-08-14 Created: 2012-08-14 Last updated: 2014-09-24Bibliographically approved

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Smedby, ÖrjanFredrikson, Matsde Geer, JakobSandborg, Michael

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Smedby, ÖrjanFredrikson, Matsde Geer, JakobSandborg, Michael
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RadiologyFaculty of Health SciencesDepartment of Radiology in LinköpingCenter for Medical Image Science and Visualization (CMIV)Occupational and Environmental MedicineRadiation PhysicsDepartment of Radiation Physics
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