Abstract #4681

Reliability and Validity of Novel Multi-View, Whole-Knee Effusion-Synovitis Quantification with Axial and Sagittal Segmentation on 3T Knee MRI

Greg Gilles¹, Dagoberto Robles¹, Luke Grawer¹, Nathaniel H. Christiansen¹, Rong Rong Tang¹, Mihra S. Taljanovic^1,2,3, Jeffrey Duryea⁴, Edward J. Bedrick⁵, and C. Kent Kwoh¹

¹Arthritis Center, University of Arizona College of Medicine, Tucson, AZ, United States, ²Departments of Medical Imaging and Orthopaedic Surgery, University of Arizona, Tucson, AZ, United States, ³Department of Radiology, University of New Mexico Health Sciences, Albuquerque, NM, United States, ⁴Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, ⁵Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, AZ, United States

Synopsis

Keywords: Osteoarthritis, Inflammation, Analysis/Processing, Artifacts, Biomarkers, Body, Data Processing, Diagnosis/Prediction, Joints, MSK, Osteoarthritis, Precision & Accuracy, Prevention, Segmentation, Software Tools, Validation

Motivation: Knee osteoarthritis (KOA) is a widespread condition that lacks approved disease-modifying drugs. There is a critical need to develop and evaluate biomarkers of KOA, including effusion-synovitis (ES).

Goal(s): The goal is to develop a novel, rapid, semi-automated segmentation of ES by non-experts that utilizes multiple perspectives simultaneously.

Approach: A custom system allowing display of axial DESS and sagittal TSE 3T knee MRI images with continuous localization and quantification enabled precise biplanar ES measurement in parallel or simultaneously via projection.

Results: Extremely high intra- and inter-reader reliabilities (ICCs > 0.98) were found for both methods, suggesting their use for rapid, accurate assessments of ES.

Impact: Our results have broad public health impact, given the prevalence of knee osteoarthritis. Our next step involves precise quantification and localization of effusion-synovitis on large datasets to identify early disease stage progressors, facilitating the development of targeted treatments and prevention.

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