Automated Analysis of Cartilage Thickness and Tissue Quality from MRI

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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.