Prediction of biomechanical trabecular bone properties with geometric features using MR imaging

Markus B. Huber; Sarah L. Lancianese; Imoh Ikpot; Mahesh B. Nagarajan; Amy L. Lerner; Axel Wismüller

doi:10.1117/12.845022

9 March 2010 Prediction of biomechanical trabecular bone properties with geometric features using MR imaging

Markus B. Huber, Sarah L. Lancianese, Imoh Ikpot, Mahesh B. Nagarajan, Amy L. Lerner, Axel Wismüller

Proceedings Volume 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging; 76261O (2010) https://doi.org/10.1117/12.845022
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

Trabecular bone parameters extracted from magnetic resonance (MR) images are compared in their ability to predict biomechanical properties determined through mechanical testing. Trabecular bone density and structural changes throughout the proximal tibia are indicative of several musculoskeletal disorders of the knee joint involving changes in the bone quality and the surrounding soft tissue. Recent studies have shown that MR imaging, most frequently applied in soft tissue imaging, also allows non-invasive 3-dimensional characterization of bone microstructure. Sophisticated MR image features that estimate local structural and geometric properties of the trabecular bone may improve the ability of MR imaging to determine local bone quality in vivo. The purpose of the current study is to use whole joint MR images to compare the performance of trabecular bone features extracted from the images in predicting biomechanical strength properties measured on the corresponding ex vivo specimens. The regional apparent bone volume fraction (appBVF) and scaling index method (SIM) derived features were calculated; a Multilayer Radial Basis Functions Network was then optimized to calculate the prediction accuracy as measured by the root mean square error (RSME) for each bone feature. The best prediction result was obtained with a SIM feature with the lowest prediction error (RSME=0.246) and the highest coefficient of determination (R² = 0.769). The current study demonstrates that the combination of sophisticated bone structure features and supervised learning techniques can improve MR imaging as an in vivo imaging tool in determining local trabecular bone quality.

Citation Download Citation

Markus B. Huber, Sarah L. Lancianese, Imoh Ikpot, Mahesh B. Nagarajan, Amy L. Lerner, and Axel Wismüller "Prediction of biomechanical trabecular bone properties with geometric features using MR imaging", Proc. SPIE 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging, 76261O (9 March 2010); https://doi.org/10.1117/12.845022

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