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Measurement of synovial tissue volume in knee osteoarthritis using a semiautomated MRI‐based quantitative approach
Author(s) -
Perry Thomas A.,
Gait Andrew,
O’Neill Terence W.,
Parkes Matthew J.,
Hodgson Richard,
Callaghan Michael J.,
Arden Nigel K.,
Felson David T.,
Cootes Timothy F.
Publication year - 2019
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27633
Subject(s) - intraclass correlation , osteoarthritis , medicine , segmentation , nuclear medicine , confidence interval , magnetic resonance imaging , radiology , computer science , artificial intelligence , pathology , clinical psychology , alternative medicine , psychometrics
Purpose Synovitis is common in knee osteoarthritis and is associated with both knee pain and progression of disease. Semiautomated methods have been developed for quantitative assessment of structure in knee osteoarthritis. Our aims were to apply a novel semiautomated assessment method using 3D active appearance modeling for the quantification of synovial tissue volume (STV) and to compare its performance with conventional manual segmentation. Methods Thirty‐two sagittal T 1 ‐weighted fat‐suppressed contrast‐enhanced MRIs were assessed for STV by a single observer using 1) manual segmentation and 2) a semiautomated approach. We compared the STV analysis using the semiautomated and manual segmentation methods, including the time taken to complete the assessments. We also examined the reliability of STV assessment using the semiautomated method in a subset of 12 patients who had participated in a clinical trial of vitamin D therapy in knee osteoarthritis. Results There was no significant difference in STV using the semiautomated quantitative method compared to manual segmentation, mean difference = 207.2 mm 3 (95% confidence interval −895.2 to 1309.7). The semiautomated method was significantly quicker than manual segmentation (18 vs. 71 min). For the semiautomated method, intraobserver agreement was excellent (intraclass correlation coefficient (3,1) = 0.99) and interobserver agreement was very good (intraclass correlation coefficient (3,1) = 0.83). Conclusion We describe the application of a semiautomated method that is accurate, reliable, and quicker than manual segmentation for assessment of STV. The method may help increase efficiency of image assessment in large imaging studies and may also assist investigation of treatment efficacy in knee osteoarthritis.