Background: Subchondral bone (SCB) undergoes changes in the shape of the articulating bone surfaces and is currently recognized as a key target in osteoarthritis (OA) treatment. The aim of this study was to present an automated system that determines the curvature of the SCB regions of the knee and to evaluate its cross-sectional and longitudinal scan-rescan precision. Methods: Six subjects with OA and six control subjects were selected from the Osteoarthritis Initiative (OAI) pilot study database. As per OAI protocol, these subjects underwent 3T MRI at baseline and every twelve months thereafter, including a 3D DESS WE sequence. We analyzed the baseline and twenty-four month images. Each subject was scanned twice at these visits, thus generating scan-rescan information. Images were segmented with an automated multi-atlas framework platform and then 3D renderings of the bone structure were created from the segmentations. Curvature maps were extracted from the 3D renderings and morphed into a reference atlas to determine precision, to generate population statistics, and to visualize cross-sectional and longitudinal curvature changes. Results: The baseline scan-rescan root mean square error values ranged from 0.006 mm -1 to 0.013 mm -1, and from 0.007 mm -1 to 0.018 mm -1 for the SCB of the femur and the tibia, respectively. The standardized response of the mean of the longitudinal changes in curvature in these regions ranged from -0.09 to 0.02 and from -0.016 to 0.015, respectively. Conclusion: The fully automated system produces accurate and precise curvature maps of femoral and tibial SCB, and will provide a valuable tool for the analysis of the curvature changes of articulating bone surfaces during the course of knee OA.
Bibliographical noteFunding Information:
Subject knees were from the Osteoarthritis Initiative (OAI) pilot study and from OAI public data release. The OAI and OAI pilot study are conducted and supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases in collaboration with the OAI Investigators and Consultants (Nos. N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262). This manuscript does not necessarily reflect the opinions or views of the OAI investigators, the National Institutes of Health , or the private funding partners.
© 2015 Elsevier Ltd.
Copyright 2017 Elsevier B.V., All rights reserved.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Health Informatics