Automatic Quantitative Analysis of MR Images of the Knee in Osteoarthritis

描述(由申请人提供)：骨关节炎(OA)是美国最常见的关节疾病，也是导致长期残疾的主要原因。据估计，2.5%的成年人口有膝关节或髋关节骨性关节炎的症状。在美国780万寻求治疗的OA患者中，超过三分之二的人有中度到重度的关节损害，并将从阻止或延缓软骨丢失的治疗中受益。骨性关节炎的病因仍有一部分不清楚：虽然遗传因素被认为是相当大一部分骨性关节炎病例的基础，但大多数的发生可能不是遗传上预先决定的。骨性关节炎会受到饮食、身体状况或身体压力(由于关节受伤或过度使用)的影响。因此，通过早期识别OA的进展，结合有效的治疗，患者的病情可能会得到改善或进一步阻止进展。然而，目前的骨性关节炎疗法只是缓解了骨性关节炎的炎症和疼痛症状，但并不能抑制正在进行的退变过程。目前尚无治疗骨性关节炎的已知疗法，进一步的药物研究对于帮助骨性关节炎患者至关重要。软骨丢失被认为是骨性关节炎的主要因素。虽然标准的基于放射学的分析方法依赖于关节间隙宽度作为软骨厚度的替代测量，但越来越多的文献支持使用MRI作为评估骨关节炎进展的主要成像方法。磁共振成像能够直接测量软骨的体积和厚度。作为一种三维成像方式，与X射线投影图像不同，它允许在全三维空间范围内对成像数据进行局部分析。磁共振成像的显著进展使得量化软骨形态的能力成为可能，从而为评估药物干预对骨性关节炎进展的潜在效果提供了一种手段。为了帮助药物开发和帮助随后的监管批准，需要准确、定量的方法来快速筛选磁共振成像数据。为了节省时间和成本，计算机辅助的3D图像分析是必不可少的。然而，大多数骨关节炎的图像分析方法仍然需要大量的人工干预，排除了对大型数据库的全面分析，例如骨关节炎倡议获得的数据。在对大脑的研究中，一种有益的策略是使用地图集来辅助数据分析。在取得这样的成功之后，我们建议创建基于群体的骨和软骨图谱，以促进骨和软骨的分割，并通过在公共解剖坐标系中表示成像数据来允许局部数据分析。我们将使用开发的方法来分析软骨厚度，并与临床变量进行相关性。开发的软件工具将以开放源码的形式分发。 与公共卫生相关：骨关节炎(OA)是一种使人虚弱的疾病，仅在美国就有数百万人受到影响。尽管骨关节炎倡议和辉瑞公司的大规模骨关节炎研究已经获得了令人惊叹的丰富数据，但由于无法获得强大的、全自动的计算机分析方法，对成像数据的全面分析已被证明是困难的。该项目将开发这样的自动图像分析工具，以允许从磁共振图像中有效地提取定量措施，最终帮助药物开发，以帮助患有目前无法治愈的致残性疾病的患者。

DESCRIPTION (provided by applicant): Osteoarthritis (OA) is the most common form of joint disease and a major cause of long-term disability in the United States (US). It is estimated that 2.5% of the adult population have symptomatic knee or hip OA. Over two-thirds of the 7.8 million OA patients in the US who seek treatment have moderate to severe joint involvement and would benefit from a therapy which arrests or delays cartilage loss. The etiology of OA is still partially unclear: While genetic factors are believed to underlie a significant proportion of OA cases, the majority of occurrences may not be genetically predetermined. OA is influenced by diet, body condition, or physical stress experienced (due to injury or overuse of a joint). Patient condition may therefore likely be improved or further progression prevented by an early identification of OA progression, combined with effective therapies. However, the current armamentarium of OA therapies merely relieves the inflammation and painful symptoms of OA but does not suppress the ongoing degenerative process. There is no known cure for osteoarthritis and further drug research is essential to help OA patients. Cartilage loss is believed to be the dominating factor in OA. While the standard radiography-based analysis method relies on joint-space width as a surrogate measure for cartilage thickness, an increasing body of literature supports the use of MRI as a primary imaging method to evaluate progression of osteoarthritis. MRI is able to directly measure cartilage volume and thickness. Being a three-dimensional imaging modality it allows, unlike x-ray projection images, for a localized analysis of imaging data in the full three-dimensional spatial context. Significant advances in MRI have resulted in the ability to quantify cartilage morphology and thereby provide a means to evaluate potential effects of pharmacologic intervention on OA progression. To aid drug development and to help subsequent regulatory approval, accurate, quantitative methods are needed to rapidly screen MR imaging data. To be time- and cost-effective, computer-assisted 3D image analysis is essential. However, most image-analysis methods for OA still require significant human intervention, precluding the comprehensive analysis of large databases as for example acquired by the Osteoarthritis Initiative. A strategy that has been beneficial in studies of the brain is the use of atlases to assist in data analysis. Following such success we propose the creation of population-based bone and cartilage atlases to facilitate bone and cartilage segmentation and to allow for localized data analysis by representing imaging data in a common anatomical coordinate system. We will use the developed methods to analyze cartilage thickness and to perform correlations with clinical variables. Developed software tools will be distributed in open-source form. PUBLIC HEALTH RELEVANCE: Osteoarthritis (OA) is a debilitating disease, with millions of people affected in the US alone. While large-scale OA studies by the Osteoarthritis Initiative and Pfizer have acquired a breathtaking wealth of data, a comprehensive analysis of the imaging data has proven difficult, due to the unavailability of robust, fully-automatic computer analysis methods. This project will develop such automatic image analysis tools to allow for the efficient extraction of quantitative measures from magnetic resonance images, to ultimately aid drug development to help patients afflicted by a disabling disease without a current cure.