Characterisation of the guinea pig model of osteoarthritis by in vivo three-dimensional magnetic resonance imaging

AbstractObjective: To characterise longitudinal changes in joint integrity and cartilage volume in vivo in the guinea pig spontaneous osteoarthritis (OA) model by magnetic resonance imaging (MRI).Methods: Guinea pigs knee were imaged in vivo by high-resolution three-dimensional (3D) MRI between the ages of 3 and 12 months. Image analysis was performed to assess qualitative knee joint changes between 3 and 12 months (n=16) and quantitative volumetric changes of the medial tibial cartilage between 9 and 12 months (n=7). After imaging, animals were killed and knees were assessed macroscopically and histologically.Results: From 3 to 6 months qualitative observation by MRI and histopathology indicated localised cartilage swelling on the medial tibial plateau. At 6 months, bone cysts had developed in the epiphysis. At 9 months, we observed by MRI and histopathology, fragmentation of the medial tibial cartilage in areas not protected by the meniscus. Cartilage degeneration had intensified at 12 months with evidence of widespread loss of cartilage throughout the tibial plateau. Segmentation of the MR cartilage images showed a 36% loss of volume between 9 and 12 months.Conclusions: We have achieved 3D image acquisition and segmentation of knee cartilage in a guinea pig model of chronic OA, which permits measurements previously only possible in man. High resolution and short acquisition time allowed qualitative longitudinal characterisation of the entire knee joint and enabled us to quantify for the first time longitudinal tibial cartilage volume loss associated with disease progression

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

Quantitative stereophotogrammetric & MRI evaluation of ankle articular cartilage and ankle joint contact characteristics

Osteoarthritis and degenerative cartilage diseases affect millions of people. Therefore, there is huge interest in developing new therapies to repair, replace and/or regenerate cartilage. This necessitates advances in techniques which make earlier non-invasive diagnosis and objective quantitative evaluations of new therapies possible. Most previous research has focused on the knee and neglected the ankle joint. Hence, the aims of this thesis are to describe and quantify the geometric properties of ankle cartilage, to evaluate joint contact characteristics and develop techniques which allow quantitative measurements to be made in vivo. Chapters 3 and 6 describe the application of a high resolution stereophotography system for making highly accurate 3-D geometric models from which quantitative measurements of cartilage parameters and joint area contact can be made. Chapters 4 and 5 report the testing of image analysis algorithms designed to segment cartilage sensitive MR images. Work focused on initially on a semi-automated 2-D segmentation approach and subsequently on a pilot study of 3-D automated segmentation algorithm. The stereophotographic studies were highly accurately and demonstrated that ankle cartilage thickness is greater than previously reported with the thickest cartilage occurring where cartilage injuries are most commonly seen. Furthermore, joint contact area is larger than previously believed and corresponds to the regions of the thickest cartilage over the talar shoulders. The image analysis studies show that it is possible to accurately and reproducibly segment the thin cartilage layers of the ankle joint using a semi-automated approach. The feasibility of a fully automated 3D method for future clinical use is also shown. In conclusion this thesis presents novel methods for examining ankle articular cartilage in vitro and in vivo, showing that the thickest cartilage occurs in highly curved regions over the shoulders of the talus which correspond to regions of greatest contact. Importantly, the image analysis techniques may be used for future clinical monitoring of patients sustaining cartilage injuries or undergoing cartilage repair therapies

Quantitative stereophotogrammetric & MRI evaluation of ankle articular cartilage and ankle joint contact characteristics

Osteoarthritis and degenerative cartilage diseases affect millions of people. Therefore, there is huge interest in developing new therapies to repair, replace and/or regenerate cartilage. This necessitates advances in techniques which make earlier non-invasive diagnosis and objective quantitative evaluations of new therapies possible. Most previous research has focused on the knee and neglected the ankle joint. Hence, the aims of this thesis are to describe and quantify the geometric properties of ankle cartilage, to evaluate joint contact characteristics and develop techniques which allow quantitative measurements to be made in vivo. Chapters 3 and 6 describe the application of a high resolution stereophotography system for making highly accurate 3-D geometric models from which quantitative measurements of cartilage parameters and joint area contact can be made. Chapters 4 and 5 report the testing of image analysis algorithms designed to segment cartilage sensitive MR images. Work focused on initially on a semi-automated 2-D segmentation approach and subsequently on a pilot study of 3-D automated segmentation algorithm. The stereophotographic studies were highly accurately and demonstrated that ankle cartilage thickness is greater than previously reported with the thickest cartilage occurring where cartilage injuries are most commonly seen. Furthermore, joint contact area is larger than previously believed and corresponds to the regions of the thickest cartilage over the talar shoulders. The image analysis studies show that it is possible to accurately and reproducibly segment the thin cartilage layers of the ankle joint using a semi-automated approach. The feasibility of a fully automated 3D method for future clinical use is also shown. In conclusion this thesis presents novel methods for examining ankle articular cartilage in vitro and in vivo, showing that the thickest cartilage occurs in highly curved regions over the shoulders of the talus which correspond to regions of greatest contact. Importantly, the image analysis techniques may be used for future clinical monitoring of patients sustaining cartilage injuries or undergoing cartilage repair therapies

Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation

Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86

Nocturnal Changes in Knee Cartilage Thickness in Young Healthy Adults

Magnetic resonance imaging (MRI) allows one to analyze cartilage physiology in vivo. Cartilage deforms during loading, but little is known about its recovery after deformation. Here we study `nocturnal' changes in knee cartilage thickness and whether postexercise deformation differs between morning and evening. Axial magnetic resonance (MR) images were acquired in the right knees of 17 healthy volunteers (age 23.5 +/- 3.0 years) after a normal day, and then after 30 deep knee bends. Coronal images were additionally acquired in 8 of these volunteers after a normal day and then after 2 min of static loading of the leg with 150% body weight. The volunteers then remained unloaded overnight and the same protocol was repeated in the morning. A significant increase (p < 0.01) in cartilage thickness was observed between evening (preexercise) and morning (preexercise): +2.4% in the patella, +8.4% in the medial tibia and +6.2% in the lateral tibia. Deformation in the morning (-6.8/-4.6/-5.1%) was generally greater than that in the evening (-5.4/-3.2/-3.7%), but this difference did not reach statistical significance. No significant difference in the nocturnal thickness increase (or postexercise deformation) was observed between men and women. We conclude that knee cartilage (thickness) recovers overnight by approximately 2-8%, independent of sex. Given the lack of `predeformation' after nocturnal periods of unloading, morning postexercise deformation of the cartilage may have a greater magnitude than evening postexercise deformation. Copyright (C) 2012 S. Karger AG, Base

Deep learning-based fully automatic segmentation of wrist cartilage in MR images

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in twenty multi-slice MRI datasets acquired with two different coils in eleven subjects (healthy volunteers and patients). The validation included a comparison with the alternative state-of-the-art CNN methods for the segmentation of joints from MR images and the ground-truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image-based and patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the representative (central coronal) slices with large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter- and intra-observer variability of the manual wrist cartilage segmentation (DSC=0.78-0.88 and 0.9, respectively). The proposed deep-learning-based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy