156 research outputs found
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
APPLICATION OF THE CONE BEAM COMPUTED TOMOGRAPHY (CBCT) MODALITY WITH WEIGHT BEARING TECHNIQUE TO IDENTIFY OSTEOARTHRITIS (OA) IN THE KNEE JOINT
ABSTRACTBackground : Osteoarthritis (OA) is a degenerative joint disease that causes inflammation of the cartilage due to the load that is often received by the joints. The knee joint is a part that is often affected by OA. Radiographic and CT examinations can be used to check for OA of the knee. Radiographic examination has the advantage of optimally displaying OA because the examination is carried out under weight bearing conditions, and CT is superior in displaying anatomical details due to cross sectional and 3D reconstruction. Technological developments present Cone Beam CT (CBCT) weight bearings that combine the advantages of radiographic and CT examinations. The purpose of this study is to determine the role and benefits of CBCT weight bearing on knee joint image information in cases of OA.Method : This type of research is literature review research with a narrative review approach. The databases used in the review articles include Science Direct, ProQuest, PubMed, DOAJ, Google Scholar, Wiley Online Library, ISI Web of Knowledge, and the Oxford Journal. The articles that have been obtained will be processed in tabulated form for later extraction.Result : The results of this study indicate that weight bearing is able to assess degeneration causing internal rotation in the range of +/- 2.8-3.1o, lateral patellar shift up to +/- 0.4 mm, joint space width (JSW) up to +/- 0.5 mm, meniscal extrusion (ME) up to +/- 10.2 mm. Conclusion : CBCT is used to obtain volumetric and cross sectional 3D knee images, in order to obtain images with high spatial resolution with low doses, detailed bone structure images, short scan times, visualization of narrowing and progression of OA in JSW clearly, visualization of OA in the menisci, as well as visualizing the complexity of the joint and soft tissue images so that OA is easily identified
An EMG-Assisted Muscle-Force Driven Finite Element Analysis Pipeline to Investigate Joint- and Tissue-Level Mechanical Responses in Functional Activities : Towards a Rapid Assessment Toolbox
Publisher Copyright: © 1964-2012 IEEE.Joint tissue mechanics (e.g., stress and strain) are believed to have a major involvement in the onset and progression of musculoskeletal disorders, e.g., knee osteoarthritis (KOA). Accordingly, considerable efforts have been made to develop musculoskeletal finite element (MS-FE) models to estimate highly detailed tissue mechanics that predict cartilage degeneration. However, creating such models is time-consuming and requires advanced expertise. This limits these complex, yet promising, MS-FE models to research applications with few participants and makes the models impractical for clinical assessments. Also, these previously developed MS-FE models have not been used to assess activities other than gait. This study introduces and verifies a semi-automated rapid state-of-the-art MS-FE modeling and simulation toolbox incorporating an electromyography- (EMG) assisted MS model and a muscle-force driven FE model of the knee with fibril-reinforced poro(visco)elastic cartilages and menisci. To showcase the usability of the pipeline, we estimated joint- and tissue-level knee mechanics in 15 KOA individuals performing different daily activities. The pipeline was verified by comparing the estimated muscle activations and joint mechanics to existing experimental data. To determine the importance of the EMG-assisted MS analysis approach, results were compared to those from the same FE models but driven by static-optimization-based MS models. The EMG-assisted MS-FE pipeline bore a closer resemblance to experiments compared to the static-optimization-based MS-FE pipeline. Importantly, the developed pipeline showed great potential as a rapid MS-FE analysis toolbox to investigate multiscale knee mechanics during different activities of individuals with KOA.Peer reviewe
Articular cartilage surface roughness as an imaging‐based morphological indicator of osteoarthritis: A preliminary investigation of osteoarthritis initiative subjects
Current imaging‐based morphometric indicators of osteoarthritis (OA) using whole‐compartment mean cartilage thickness (MCT) and volume changes can be insensitive to mild degenerative changes of articular cartilage (AC) due to areas of adjacent thickening and thinning. The purpose of this preliminary study was to evaluate cartilage thickness‐based surface roughness as a morphometric indicator of OA. 3D magnetic resonance imaging (MRI) datasets were collected from osteoarthritis initiative (OAI) subjects with Kellgren–Lawrence (KL) OA grades of 0, 2, and 4 (n = 10/group). Femoral and tibial AC volumes were converted to two‐dimensional thickness maps, and MCT, arithmetic surface roughness (Sa), and anatomically normalized Sa (normSa) were calculated. Thickness maps enabled visualization of degenerative changes with increasing KL grade, including adjacent thinning and thickening on the femoral condyles. No significant differences were observed in MCT between KL grades. Sa was significantly higher in KL4 compared to KL0 and KL2 in the whole femur (KL0: 0.55 ± 0.10 mm, KL2: 0.53 ± 0.09 mm, KL4: 0.79 ± 0.18 mm), medial femoral condyle (KL0: 0.42 ± 0.07 mm, KL2: 0.48 ± 0.07 mm, KL4: 0.76 ± 0.22 mm), and medial tibial plateau (KL0: 0.42 ± 0.07 mm, KL2: 0.43 ± 0.09 mm, KL4: 0.68 ± 0.27 mm). normSa was significantly higher in KL4 compared to KL0 and KL2 in the whole femur (KL0: 0.22 ± 0.02, KL2: 0.22 ± 0.02, KL4: 0.30 ± 0.03), medial condyle (KL0: 0.17 ± 0.02, KL2: 0.20 ± 0.03, KL4: 0.29 ± 0.06), whole tibia (KL0: 0.34 ± 0.04, KL2: 0.33 ± 0.05, KL4: 0.48 ± 0.11) and medial plateau (KL0: 0.23 ± 0.03, KL2: 0.24 ± 0.04, KL4: 0.40 ± 0.10), and significantly higher in KL2 compared to KL0 in the medial femoral condyle. Surface roughness metrics were sensitive to degenerative morphologic changes, and may be useful in OA characterization and early diagnosis. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2755–2764, 2017.A custom algorithm was used to create two‐dimensional articular cartilage thickness maps of patients from the Osteoarthritis Initiative. Thickness maps demonstrate significantly increased surface roughness as a function of increasing Kellgren–Lawrence (KL) osteoarthritis (OA) grade, particularly in the medial femoral condyle, though mean cartilage thickness was not found to differ significantly between KL grades. Surface roughness‐based metrics have potential utility as morphological indicators of OA.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141486/1/jor23588_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141486/2/jor23588.pd
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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
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