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Abstract
Osteoporosis is a disease characterized by low bone quality and increased risk of fracture. In order to improve osteoporosis treatment, it is essential to monitor bone quality and its changes over time in healthy, diseased and treated bones. With the recent development of in vivo high-resolution peripheral quantitative computed tomography (HR-pQCT) it became possible to capture bone micro-architecture, an important determinant of bone quality, in humans. The aim of this dissertation was to propose new ways to analyze the resulting time series of three-dimensional (3D) image data to gain novel insight into bone behaviour.
In a first step, a novel method for tracking and predicting micro-architectural changes using deformable image registration was validated. Applied to an osteoporotic and healthy pre-clinical model, this study demonstrated successful prediction of 3D architecture based on a time series of images without knowledge of disease state.
Prior to extending the monitoring of changes to human bone, the problem of subject motion artifacts in HR-pQCT imaging was addressed. An automatic, fast and objective method was developed to quantify three separate components of subject motion using projection data. With this tool, guidelines for image quality management in the presence of subject motion were established. Understanding and managing these artifacts is pivotal for guaranteeing consistent image quality in large multi-centre studies. In addition to motion quantification, a novel method for compensating movement artifacts was developed. The proposed method for motion compensation paves the way for future research into improving image quality, potentially increasing viable data benefiting drug trials and studies of rare diseases with small sample sizes.
Lastly, in order to monitor bone micro-architecture changes in humans, an automated registration methodology was devised to align 3D HR-pQCT images and techniques to visualize local architectural changes were developed. It was possible to visualize local changes due to normal bone remodelling, and in response to osteoporosis treatment, aiding interpretation of changes in traditional bone quality parameters. The developed methods form the foundation for tracking bone adaptation over time, ultimately furthering our understanding of bone mechanisms in humans.
