Automated Analysis of Cartilage Thickness and Tissue Quality from MRI

通过 MRI 自动分析软骨厚度和组织质量

• 批准号: DT/F003072/1
• 负责人: Chris Taylor
• 金额: $ 37.18万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=DT%2FF003072%2F1
• 关键词: Automated; Analysis; Cartilage; Thickness; Tissue

Osteoarthritis (OA) is a major cause of disability and reduced quality of life. It is associated with degenerative changes in the bone and cartilage of articulating joints (particularly the knee), leading to pain and disability. The disease is extremely variable, with some patients deteriorating extremely rapidly, whilst others progress slowly over many years. There are currently no particularly effective treatments for the disease, but more effective therapies are on the horizon - particularly drugs that target cartilage or bone degeneration. Clinical indicators of disease progression, such as pain and disability, are subjective and non-specific, leading to long and expensive clinical trials, which may ultimately prove inconclusive. There is thus a pressing need for quantitative biomarkers (measures of disease progression) to support decision making in OA drug development. The availability of effective but expensive drugs for OA will also create a need for methods to identify those patients who would most benefit from treatment, leading to more cost-effective deployment. The aim of the project is to develop technology to provide biomarkers for OA, through automated analysis of 3D magnetic resonance images (MRI) of the knee. This will build on feasibility work previously undertaken by members of the consortium. The specific objectives are to: 1. Extract the structure of bone and cartilage from MRI of the knee automatically - enabling computerised measurement of cartilage and bone morphology; 2. Develop methods for measuring cartilage and bone quality from the images - enabling early detection of degenerative disease; 3. Adapt existing methods for obtaining anatomically consistent maps of cartilage thickness and quality - enabling direct comparison and statistical analysis of maps from different subjects; 4. Develop statistical methods for detecting subtle but consistent patterns of change in these maps - providing insight into the disease process and a sensitive means of detecting progression in cohorts of patients; 5. Integrate the methods to create a technology demonstrator - providing a test-bed for the technology; 6. Validate the approach using data from large-scale OA imaging studies.

骨关节炎 (OA) 是导致残疾和生活质量下降的主要原因。它与关节（特别是膝盖）的骨骼和软骨的退行性变化有关，导致疼痛和残疾。这种疾病变化很大，一些患者病情恶化得非常快，而另一些患者则在多年内进展缓慢。目前还没有针对这种疾病的特别有效的治疗方法，但更有效的治疗方法即将出现，特别是针对软骨或骨退化的药物。疾病进展的临床指标，例如疼痛和残疾，是主观的和非特异性的，导致临床试验漫长且昂贵，最终可能证明是不确定的。因此，迫切需要定量生物标志物（疾病进展的衡量标准）来支持 OA 药物开发决策。治疗 OA 的有效但昂贵的药物的出现也将产生对确定哪些患者最能从治疗中受益的方法的需求，从而导致更具成本效益的部署。该项目的目标是开发技术，通过对膝关节 3D 磁共振图像 (MRI) 进行自动分析，为 OA 提供生物标志物。这将以联盟成员之前开展的可行性工作为基础。具体目标是： 1. 自动从膝关节 MRI 中提取骨骼和软骨结构 - 实现软骨和骨骼形态的计算机化测量； 2. 开发从图像中测量软骨和骨骼质量的方法——能够及早发现退行性疾病； 3. 调整现有方法以获得解剖学上一致的软骨厚度和质量图 - 能够对来自不同受试者的图进行直接比较和统计分析； 4. 开发统计方法来检测这些图谱中微妙但一致的变化模式——提供对疾病过程的洞察和检测患者群体进展的敏感方法； 5. 整合方法创建技术演示器——为技术提供测试平台； 6. 使用大规模 OA 成像研究的数据验证该方法。

项目成果

Comparison of 3T MR scanners in regional cartilage-thickness analysis in osteoarthritis: a cross-sectional multicenter, multivendor study.

• DOI: 10.1186/ar3174
• 发表时间: 2010
• 期刊: Arthritis research & therapy
• 影响因子: 4.9
• 作者: Balamoody S;Williams TG;Waterton JC;Bowes M;Hodgson R;Taylor CJ;Hutchinson CE
• 通讯作者: Hutchinson CE

Group-wise Correspondence of Surfaces using Non-paramentric Regularisation and Shape Images

使用非参数正则化和形状图像的曲面的分组对应

• 发表时间: 2007
• 作者: C J Twining