Imaging technologies for preclinical models of bone and joint disorders

Preclinical models for musculoskeletal disorders are critical for understanding the pathogenesis of bone and joint disorders in humans and the development of effective therapies. The assessment of these models primarily relies on morphological analysis which remains time consuming and costly, requiring large numbers of animals to be tested through different stages of the disease. The implementation of preclinical imaging represents a keystone in the refinement of animal models allowing longitudinal studies and enabling a powerful, non-invasive and clinically translatable way for monitoring disease progression in real time. Our aim is to highlight examples that demonstrate the advantages and limitations of different imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), single-photon emission computed tomography (SPECT) and optical imaging. All of which are in current use in preclinical skeletal research. MRI can provide high resolution of soft tissue structures, but imaging requires comparatively long acquisition times; hence, animals require long-term anaesthesia. CT is extensively used in bone and joint disorders providing excellent spatial resolution and good contrast for bone imaging. Despite its excellent structural assessment of mineralized structures, CT does not provide in vivo functional information of ongoing biological processes. Nuclear medicine is a very promising tool for investigating functional and molecular processes in vivo with new tracers becoming available as biomarkers. The combined use of imaging modalities also holds significant potential for the assessment of disease pathogenesis in animal models of musculoskeletal disorders, minimising the use of conventional invasive methods and animal redundancy

Quantitative Magnetic Resonance Imaging and Analysis of Articular Cartilage and Osteoarthritis

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.PhD Studentship funded by GlaxoSmithKlin

APPLICATIONS OF MODERN IMAGING TECHNOLOGY IN ORTHOPAEDIC TRAUMA SURGERY

Orthopaedic trauma surgery is a complex surgical speciality in which anatomy, physiology and physics are mixed. Proper diagnosing and based on that planning and performing surgery is of crucial matter. This article briefly summarizes available radiological modalities used for diagnostics and for surgical planning. It focuses on utility of rapid prototyping process in trauma surgery. Moreover, a case study in which this technique was used is described. Rapid prototyping proved its usefulness and in future it may become a modality of choice for planning complex trauma procedures.&nbsp

Breast Cancer: Modelling and Detection

This paper reviews a number of the mathematical models used in cancer modelling and then chooses a specific cancer, breast carcinoma, to illustrate how the modelling can be used in aiding detection. We then discuss mathematical models that underpin mammographic image analysis, which complements models of tumour growth and facilitates diagnosis and treatment of cancer. Mammographic images are notoriously difficult to interpret, and we give an overview of the primary image enhancement technologies that have been introduced, before focusing on a more detailed description of some of our own recent work on the use of physics-based modelling in mammography. This theoretical approach to image analysis yields a wealth of information that could be incorporated into the mathematical models, and we conclude by describing how current mathematical models might be enhanced by use of this information, and how these models in turn will help to meet some of the major challenges in cancer detection

Imaging of Osteoarthritis

Osteoarthritis (OA) is the most prevalent joint disorder in the elderly, and there is no effective treatment. Imaging is essential for evaluating the synovial joint structures (including cartilage, meniscus, subchondral bone marrow and synovium) for diagnosis, prognosis, and follow-up. This article describes the roles and limitations of both conventional radiography and magnetic resonance (MR) imaging, and considers the use of other modalities (eg, ultrasonography, nuclear medicine, computed tomography [CT], and CT/MR arthrography) in clinical practice and OA research. The emphasis throughout is on OA of the knee. This article emphasizes research developments and literature evidence published since 2008