Detecting Cartilage Surface Degeneration Using Photon Counting CT and Solute Transport Modeling
Abstract
Osteoarthritis (OA), a joint disease that currently has no cure, is characterized by progressive and largely irreversible damage to the soft tissues such as the articular cartilage that normally provides a smooth, durable surface to support forces produced during daily activity. OA is a major cause of disability and pain affecting 12% of the U.S. adult population and is a substantial burden on the health care system, with estimated annual costs upwards of $40 billion. While OA is not always associated with known factors, joint injuries such as tears to the meniscus or anterior cruciate ligament (ACL) in the knee dramatically increase the likelihood of developing OA, often rapidly and at a young age. OA in general, and knee OA following joint injury specifically, are more common among military Service Members and Veterans than among the general population. Efforts to understand early disease progress and develop and test interventions to slow OA progress are hampered by the relative insensitivity of existing medical imaging tests to the earliest signs of degeneration. This proposal aims to address this critical roadblock by developing a new imaging approach to detect early physical changes to the cartilage surface characteristic of early degeneration. This proposal addresses the Diagnosis Strategic Goal and the Arthritis Topic Area within the Orthopaedic Medicine Portfolio Category of the Fiscal Year 2022 Peer Reviewed Medical Research Program. The overall goal of the proposed research is to develop techniques to characterize changes in contrast agent diffusivity, a measure of how easily a substance in the joint fluid can penetrate the cartilage surface, as a marker for early degenerative changes indicative of early-stage OA. Our approach uses computed tomography (CT), a three-dimensional imaging method based on x-rays, to detect the progressive penetration into the articular cartilage of a clinical CT contrast agent injected into the knee joint. Specifically, we will use a CT imaging approach based on a new type of x-ray detector (photon counting CT, PCCT) and will develop specific technical advances to produce even finer detail at high accuracy. We will analyze the sequential image data using mathematical models that account for confounding factors that occur in real patient scans, allowing us to determine maps of the diffusivity over the joint surface. Because diffusivity (ease of contrast penetration) increases as the tissue near the surface degenerates, we expect to be able to identify regions where early changes occur before these changes propagate through the entire tissue. Aim 1 of the proposed project will focus on developing and validating specific technical advances of the CT imaging approach to increase the spatial resolution of the images and allow more accurate detection of the cartilage surface location. Aim 2 will focus on improving methods for estimating the contrast agent diffusivity from the CT images by accounting for realistic, complex patterns that occur in clinical images but are not often factors in the laboratory. We will validate these approaches by comparing diffusivity values determined from scans of patients prior to total knee replacement surgeries to values determined for isolated samples of their tissue removed during the procedure. Aim 3 will test the ability of the combined approach to detect degenerative changes in patients after anterior cruciate ligament repair surgery, as this patient group is known to have a greatly increased risk of developing OA. We expect this new imaging strategy to be beneficial for research on developing new interventions and for clinical diagnosis. In the near term, the ability to sensitively detect early degenerative changes will provide a powerful research platform for investigating early disease processes, risk factors for rapid progression, and effectiveness of mechanical or pharmaceutical interventions to modify OA progression. In the longer term as PC
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2024
- Source ID
- HT94252310331
Entities
People
- Marc Levenston
Organizations
- Stanford University
- United States Army