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

Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis

The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). Bilateral posterior-anterior knee radiographs were analyzed from the baseline of OAI and MOST datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. We used an oversegmentation strategy for partitioning knee images into the compact regions that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick features, Shannon entropy, and HOG methods were computed within the standard ROI and within the proposed adaptive ROIs. Subsequently, we built logistic regression models to identify and compare the performances of each texture descriptor and each ROI placement method using 5-fold cross validation setting. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset.We used area under the receiver operating characteristic (ROC) curve (AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. We found that the adaptive ROI improves the classification performance (OA vs. non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, LBP yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA

CartiMorph: a framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient $\rho \in [0.82,0.97]$), surface area ($\rho \in [0.82,0.98]$) and volume ($\rho \in [0.89,0.98]$) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.Comment: To be published in Medical Image Analysi

Quantitative Cartilage Imaging in Knee Osteoarthritis

Quantitative measures of cartilage morphology (i.e., thickness) represent potentially powerful surrogate endpoints in osteoarthritis (OA). These can be used to identify risk factors of structural disease progression and can facilitate the clinical efficacy testing of structure modifying drugs in OA. This paper focuses on quantitative imaging of articular cartilage morphology in the knee, and will specifically deal with different cartilage morphology outcome variables and regions of interest, the relative performance and relationship between cartilage morphology measures, reference values for MRI-based knee cartilage morphometry, imaging protocols for measurement of cartilage morphology (including those used in the Osteoarthritis Initiative), sensitivity to change observed in knee OA, spatial patterns of cartilage loss as derived by subregional analysis, comparison of MRI changes with radiographic changes, risk factors of MRI-based cartilage loss in knee OA, the correlation of MRI-based cartilage loss with clinical outcomes, treatment response in knee OA, and future directions of the field

Subject-specific Finite Element Models of the Human Knee for Transtibial Amputees to Analyze Tibial Cartilage Pressure for Gait, Cycling, and Elliptical Training

It is estimated that approximately 10-12% of the adult population suffers from osteoarthritis (OA), with long reaching burdens personally and socioeconomically. OA also causes mild discomfort to severe pain in those suffering from the disease. The incidence rate of OA for individuals with transtibial amputations is much than average in the tibiofemoral joint (TF). It is well understood that abnormal articular cartilage stress, whether that be magnitude or location, increases the risk of developing OA. Finite element (FE) simulations can predict stress in the TF joint, many studies throughout the years have validated the technology used for this purpose. This thesis is the first to successfully validate a procedure for creating subject-specific FE models for transtibial amputees to simulate the TF joint in gait, cycling and elliptical exercises. Maximum tibial cartilage pressure was extracted post-simulation and compared to historical data. The body weight normalized contact pressure on the tibial articular cartilage for the two amputee participants was larger in magnitude than the control participant in all but the medial compartment in cycling. Additionally, cycling exercise produced the smallest values of contact pressure with elliptical and gait producing similar max values but different areas of effect. The results from this thesis align with the body of work preceding it and further the goal of a FE model that predicts in-vivo articular cartilage stress in the TF joint. Future studies can further refine this methodology and create additional subject-specific models to allow for a statistical analysis of the observed differences to find if the results are significantly different. Refining the methodology could include investigating the full effect of the damping factor on contact pressure and exploring alternative methods of mesh generation

ADDRESSING PARTIAL VOLUME ARTIFACTS WITH QUANTITATIVE COMPUTED TOMOGRAPHY-BASED FINITE ELEMENT MODELING OF THE HUMAN PROXIMAL TIBIA

Quantitative computed tomography (QCT) based finite element modeling (FE) has potential to clarify the role of subchondral bone stiffness in osteoarthritis. The limited spatial resolution of clinical CT systems, however, results in partial volume (PV) artifacts and low contrast between the cortical and trabecular bone, which adversely affect the accuracy of QCT-FE models. Using different cortical modeling and partial volume correction algorithms, the overall aim of this research was to improve the accuracy of QCT-FE predictions of stiffness at the proximal tibial subchondral surface. For Study #1, QCT-FE models of the human proximal tibia were developed by (1) separate modeling of cortical and trabecular bone (SM), and (2) continuum models (CM). QCT-FE models with SM and CM explained 76%-81% of the experimental stiffness variance with error ranging between 11.2% and 20.2%. SM did not offer any improvement relative to CM. The segmented cortical region indicated densities below the range reported for cortical bone, suggesting that cortical voxels were corrupted by PV artifacts. For Study #2, we corrected PV layers at the cortical bone using four different methods including: (1) Image Deblurring of all of the proximal tibia (IDA); (2) Image Deblurring of the cortical region (IDC); (3) Image Remapping (IR); and (4) Voxel Exclusion (VE). IDA resulted in low predictive accuracy with R2=50% and error of 76.4%. IDC explained 70% of the measured stiffness variance with 23.3% error. The IR approach resulted in an R2 of 81% with 10.6% error. VE resulted in the highest predictive accuracy with R2=84%, and 9.8% error. For Study #3, we investigated whether PV effects could be addressed by mapping bone’s elastic modulus (E) to mesh Gaussian points. Corresponding FE models using the Gauss-point method converged with larger elements when compared to the conventional method which assigned a single elastic modulus to each element (constant-E). The error at the converged mesh was similar for constant-E and Gauss-point; though, the Gauss-point method indicated this error with larger elements and less computation time (30 min vs 180 min). This research indicated that separate modeling of cortical and trabecular bone did not improve predictions of stiffness at the subchondral surface. However, this research did indicate that PV correction has potential to improve QCT-FE models of subchondral bone. These models may help to clarify the role of subchondral bone stiffness in knee OA pathogenesis with living people

Analysis, Segmentation and Prediction of Knee Cartilage using Statistical Shape Models

Osteoarthritis (OA) of the knee is one of the leading causes of chronic disability (along with the hip). Due to rising healthcare costs associated with OA, it is important to fully understand the disease and how it progresses in the knee. One symptom of knee OA is the degeneration of cartilage in the articulating knee. The cartilage pad plays a major role in painting the biomechanical picture of the knee. This work attempts to quantify the cartilage thickness of healthy male and female knees using statistical shape models (SSMs) for a deep knee bend activity. Additionally, novel cartilage segmentation from magnetic resonance imaging (MRI) and estimation algorithms from computer tomography (CT) or x-rays are proposed to facilitate the efficient development and accurate analysis of future treatments related to the knee. Cartilage morphology results suggest distinct patterns of wear in varus, valgus, and neutral degenerative knees, and examination of contact regions during the deep knee bend activity further emphasizes these patterns. Segmentation results were achieved that were comparable if not of higher quality than existing state-of-the-art techniques for both femoral and tibial cartilage. Likewise, using the point correspondence properties of SSMs, estimation of articulating cartilage was effective in healthy and degenerative knees. In conclusion, this work provides novel, clinically relevant morphological data to compute segmentation and estimate new data in such a way to potentially contribute to improving results and efficiency in evaluation of the femorotibial cartilage layer