Motivation: Osteoarthritis (OA) is a painful disease that affects tens of millions of Americans, but is poorly understood, resulting in a lack of treatments. Enabling low-cost approaches for widespread study of risk factors, onset and early progression of OA will enable better understanding of OA mechanisms, treatment development, and triage of patients to different treatments based on speci?c disease phenotypes. Multiple systemic factors, biochemical factors, and other risk factors are associated with OA, but causes are dif?- cult to isolate and study during slow progression. Currently OA is diagnosed as joint-space narrowing using X-ray radiography, at a stage well beyond when interventions can be effective. Magnetic resonance imaging (MRI) of- fers sensitivity to morphologic and biochemical changes, but most methods are impractical for widespread clinical or research use. Usually MRI exams study only one knee, precluding the opportunity to compare knees. Sim- ilarly, biomechanics assessment typically requires numerous tests using advanced and rarely-available equip- ment and time-intensive analysis by skilled personnel, making this a challenge for widespread use. We have shown rapid, simultaneous 3D scanning of both knees with quantitative relaxometry and diffusion map- ping of connective tissues, combined with novel visualization of longitudinal change validated in a population with anterior cruciate ligament (ACL) tears. We have developed fully-automated cartilage and meniscus seg- mentation to simplify post-processing. (Our automated cartilage segmentation variability approaches that of reader-to-reader variability.) We now propose to combine MRI acquisition, reconstruction and analysis tech- niques with simple measures of kinematics into a widely applicable low-cost imaging and biomechanical test, which we will validate in subjects with ACL-injury and subjects with varying Kellgren-Lawrence grades of OA. Approach: We will begin by developing a robust 5-to-8-minute bilateral knee MRI exam, using an ef?cient 3D isotropic acquisition and novel deep-learning based image reconstructions. This will be followed with automated cartilage segmentation and quantitative analysis (thickness, T2, diffusion) of all 3 knee plates and automated semiquantitative scoring approaches for synovitis, bone marrow and cartilage lesions. Inertial measurement units (IMUs) will be used to measure kinematics, and gait asymmetries. We will continue our studies in ACL pa- tients to validate techniques and to develop asymmetry analyses for both imaging and biomechanical measures. Finally, in subjects with varying OA grade, we will evaluate the potential of the overall low-cost approach to relate asymmetry and longitudinal change measures to progression and OA grade. Signi?cance: This project will develop an acquisition and analysis pipeline to quantify knee changes and left/right asymmetries that precede OA. We will characterize methods in idiopathic OA subjects and ACL- injured subjects at risk of post-traumatic OA. The very low target cost, under $120/subject, will ultimately enable widespread study of early onset and progression of different OA types, leading to earlier and better treatments.

Public Health Relevance

Osteoarthritis remains the leading cause of disability, and effective treatment will require ef?cient assessment of disease risk-factors, onset, and progression, both for development and personalization of minimally invasive interventions. We propose a 5-minute 3D MRI exam of both knees without radiation or contrast injection, that will be combined with low-cost measures of knee motion and fully automated analysis methods to provide quan- titative measurements of cartilage, tendon, ligament, bone and ?brocartilage health and asymmetries between knees. This low-cost, rapid, bilateral assessment will enable research studies in large populations, as well as adding quantitative bilateral information to clinical scans to dramatically improve understanding of onset of dif- ferent types of osteoarthritis.

National Institute of Health (NIH)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Research Project (R01)
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Emerging Imaging Technologies and Applications Study Section (EITA)
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Zheng, Xincheng
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Stanford University
Schools of Medicine
United States
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