Automatic Quantitative Analysis of MR Images of the Knee in Osteoarthritis

描述（由申请人提供）：骨关节炎（OA）是最常见的关节疾病，也是美国（US）长期残疾的主要原因。据估计，2.5%的成年人患有症状性膝关节或髋关节OA。在美国寻求治疗的780万OA患者中，超过三分之二的患者患有中度至重度关节受累，并将受益于阻止或延迟软骨损失的治疗。OA的病因仍部分不清楚：虽然遗传因素被认为是很大一部分OA病例的基础，但大多数发生可能不是遗传预定的。OA受饮食、身体状况或身体压力（由于关节损伤或过度使用）的影响。因此，通过早期识别OA进展并结合有效治疗，患者病情可能得到改善或预防进一步进展。然而，目前的OA治疗方法仅缓解OA的炎症和疼痛症状，但不能抑制正在进行的退行性过程。骨关节炎没有已知的治愈方法，进一步的药物研究对帮助OA患者至关重要。骨关节炎的主要病因是骨钙素的丢失。虽然标准的基于X线摄影的分析方法依赖于关节间隙宽度作为软骨厚度的替代指标，但越来越多的文献支持使用MRI作为评价骨关节炎进展的主要成像方法。MRI能够直接测量软骨体积和厚度。作为一种三维成像模式，与X射线投影图像不同，它允许在全三维空间背景下对成像数据进行局部分析。MRI的重大进展使得能够量化软骨形态，从而提供了一种评价药物干预对OA进展的潜在影响的方法。为了帮助药物开发并帮助随后的监管批准，需要准确的定量方法来快速筛选MR成像数据。为了节省时间和成本，计算机辅助3D图像分析至关重要。然而，大多数OA的图像分析方法仍然需要大量的人为干预，排除了对大型数据库的综合分析，例如骨关节炎倡议所获得的。在大脑研究中，一个有益的策略是使用地图集来帮助数据分析。在这样的成功之后，我们建议创建基于人口的骨和软骨图谱，以促进骨和软骨分割，并允许通过在一个共同的解剖坐标系中表示成像数据进行本地化数据分析。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分析。开发的软件工具将以开放源码形式分发。 公共卫生相关性：骨关节炎（OA）是一种使人衰弱的疾病，仅在美国就有数百万人受到影响。虽然骨关节炎倡议和辉瑞公司的大规模OA研究已经获得了惊人的丰富数据，但由于缺乏强大的全自动计算机分析方法，对成像数据进行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提取定量测量，最终帮助药物开发，以帮助患有致残性疾病而目前无法治愈的患者。

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.