Measurement of synovial tissue volume in knee osteoarthritis using a semiautomated MRI-based quantitative approach.

Research output: Contribution to journalArticle

  • External authors:
  • Thomas Perry
  • Richard Hodgson
  • M J Callaghan
  • N K Arden

Abstract

Synovitis is common in knee osteoarthritis (OA) and associated with both knee pain and progression of disease. Semi-automated methods have been developed for quantitative assessment of structure in knee OA. Our aims were to apply a novel semi-automated assessment method using 3D active appearance modelling (AAM) for the quantification of STV and to compare its performance with conventional manual segmentation.
Methods:
32 sagittal T1-weighted fat suppressed contrast enhanced (CE) MRIs were assessed for STV by a single observer using i) manual segmentation and ii) using a semi-automated approach. We compared the STV analysis using the semi-automated and manual segmentation methods, including the time taken to complete the assessments. We also examined the reliability of STV assessment using the semi-automated method in a subset of 12 patients who had participated in a clinical trial of vitamin D therapy in knee OA (VIDEO study).
Results:
There was no significant difference in STV using the semi-automated quantitative method compared to manual segmentation; mean difference = 207.2 mm3 (95% CI -895.2 to 1309.7). The semi-automated method was significantly quicker than manual segmentation (18 vs. 71 minutes). For the semi-automated method, intra-observer agreement was excellent (ICC (3,1) = 0.99) and inter-observer agreement was very good (ICC (3,1) = 0.83).
Conclusion:
We describe the application of a semi-automated method which 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 also assist investigation of treatment efficacy in knee OA.

Bibliographical metadata

Original languageEnglish
Pages (from-to)3056-3064
JournalMagn Reson Med
Volume81
Issue number5
Early online date15 Feb 2019
DOIs
Publication statusPublished - 2019