MRI plays an important role in the continuing search for a sensitive osteoarthritis (OA) imaging biomarker able to detect early, pre-morphological alterations in cartilage composition. Determining the compositional recovery pattern of cartilage following acute joint loading could potentially present a more sensitive biomarker for defining cartilage health [1]. However, only a limited amount of studies have assessed both the immediate effect of joint loading on cartilage, as well as its post-loading recovery. In addition, when assessing the compositional responses of cartilage to joint loading, previous studies usually did not incorporate the measurement error of the used quantitative MRI technique into their analysis. Therefore, an uncertainty persists whether or not compositional MRI techniques are sensitive enough to measure changes in water and macromolecular content of cartilage, or if previous studies were merely measuring noise. Consequently, an objective of this thesis is to increase our understanding of and reliability in quantitative T2 and T1ρ relaxation time mapping to detect compositional responses of cartilage following a joint loading activity.

Furthermore, to obtain the quantitative morphological and compositional measures of cartilage, detailed region-specific delineation of cartilage is required. This delineation (or segmentation) of cartilage is laborious and time-consuming as it is usually performed manually by an expert observer. Many new advances in image analysis, particularly those in convolutional neural networks (CNNs) and deep learning, have enabled a time-efficient semi- or fully-automated alternative to this process [2, 3]. This thesis explores the utility of deep CNNs generated segmentations for accurate surface-based analysis of cartilage morphology and composition from knee MRIs as well as of cortical bone thickness from knee CTs.

Chapter 1 will provide an introduction into the structure and biomechanics of articular cartilage and the role of MRI in imaging the degenerative joint disorder, osteoarthritis as well as the effects of different joint loading activities on cartilage morphology and composition.

Chapter 2 explains the principle of MRI and the pulse sequences used in the following chapter for the morphometric and compositional assessment of articular cartilage.

Chapter 3 describes the use of 3D Cartilage Surface Mapping (3D-CaSM) [3] to assess variations in cartilage T1ρ and T2 relaxation times of young, healthy participants following a mild, unilateral stepping activity. By evaluating and incorporating the intrasessional repeatability of the T1ρ and T2 mapping techniques, I aim to highlight those cartilage areas experiencing exercise-induced compositional changes greater than measurement error.

A significant amount of time is needed to manually segment the regions-of-interest required to perform the 3D-CaSM used in Chapter 3. Therefore, in Chapter 4, I assessed the use of deep convolutional neural networks for automating the segmentation process for multiple knee joint tissues simultaneous and increase the time-efficiency for evaluating knee MR datasets. I evaluated the use of a conditional Generative Adversarial Network (cGAN) as a potentially improved method for automated segmentation compared to the widely used convolutional neural network, U-Net.

In Chapter 5 I combined the 3D-CaSM and automated segmentation methods presented in Chapters 3 and 4, respectively to assess the use of fully automatic segmentations of femoral and tibial bone-cartilage structures for accurate surface-based analysis of cartilage morphology and composition on knee MR images. This was performed on publicly available data from the Osteoarthritis Initiative, a multicentre observational study with expert manual segmentations provided by the Zuse Institute in Berlin.

Chapter 6 describes an automated pipeline for subchondral cortical bone thickness mapping from knee CT data. I developed a method of using automated segmentations of articular cartilage and bone from knee MRI data to determine the periarticular bone surface which is covered by cartilage. This surface was then used to perform cortical bone thickness measurements on corresponding CT data. I validated this pipeline using data from the EU-funded, multi-centre observational study called Applied Private-Public partneRship enabling OsteoArthritis Clinical Headway (APPROACH).

Chapter 7 summarises the main conclusions and contributions of the works presented in this thesis as well as providing directions for future work.