Related Experiment Video
Updated: May 9, 2026
In situ Compressive Loading and Correlative Noninvasive Imaging of the Bone-periodontal Ligament-tooth Fibrous Joint
Published on: March 7, 2014
Predicting physiological hip joint loads with inverse bone remodeling using clinically available QCT images
Sebastian Bachmann1, Gianluca Iori2, Kay Raum3
1Institute of Lightweight Design and Structural Biomechanics, TU Wien, Gumpendorfer Straße 7, Vienna 1060, Austria.
Background And Objective:
Assessing joint-level loading conditions in vivo is challenging due to invasive measurement or complex computation. Inverse bone remodeling (IBR) offers a different approach by recovering the loading conditions directly from computed tomography (CT) images of the bone microstructure by finding the magnitudes to a set of load cases that load the bone optimally, i.e., maximally homogeneously. An efficient IBR method was recently proposed based on homogenized finite element (hFE) models. This study compared the hip joint load predictions of hFE-based IBR with clinically feasible CT scans to those obtained with the current gold standard, micro-FE-based IBR.
Methods:
A set of 20 proximal femora was scanned ex vivo, both with a clinical quantitative CT (QCT) scanner (0.3 mm resolution) and an Xtreme CT II (XCT2) scanner (0.03 mm resolution). Finite element (FE) models with decreasing complexity were automatically created from those images. Micro-FE (µFE) models based on XCT2 images served as a baseline. hFE models based on the QCT images were created as clinically feasible models. Further intermediate models were created to trace sources of errors. IBR was applied to predict the optimal scaling factors of twelve unit load cases distributed over the femoral head.
Results:
The predicted loads of the newly developed workflow for QCT images within IBR followed a trend seen previously with hFE models created from high-resolution images, such as XCT2. The peak load magnitudes of µFE and hFE-based IBR were well correlated (R²=76.8 %), and the overall distribution of the loads was similar. However, an additional peak load calibration was required to obtain quantitative agreement (CCC=82.8 %).
Conclusions:
A thorough comparison of µFE-based IBR and hFE-based IBR using QCT data was performed for the first time. A clinically feasible workflow, including a peak calibration, is presented, allowing for fast prediction of physiological peak hip joint loads.
