MRI image of the knee highlighting bony anatomy and joint surfaces used for musculoskeletal imaging analysis.

Clinical & Translational Musculoskeletal Imaging (CTMI)

The Clinical and Translational Musculoskeletal Imaging (CTMI) group focuses on advancing standard and emerging imaging techniques to better understand diseases of the musculoskeletal system, with a primary emphasis on osteoarthritis. The lab develops and applies imaging biomarkers to assess disease severity, monitor progression, and identify early structural and biochemical changes in joints using MRI based approaches.

The team has created widely used quantitative and semi quantitative tools, including Whole Organ MRI Scores of the knee, composite measures of synovitis, and the SHOMRI system for hip osteoarthritis. In parallel, the lab develops artificial intelligence based methods to quantify muscle volume, fat infiltration, periarticular adipose tissue, and synovitis, as well as explores innovative imaging approaches such as low field MRI at 0.55T, metal artifact reduction techniques, and CT like MRI.

Led by Dr. Thomas M. Link and Dr. Gabby Joseph, the CTMI group brings together clinician scientists, bioengineers, epidemiologists, biostatisticians, and computer scientists. The lab’s goal is to improve patient care by creating sensitive imaging biomarkers that predict disease progression and support early intervention. Research is supported by the National Institutes of Health, the National Institute of Arthritis and Musculoskeletal and Skin Diseases, and industry partners.

Preventing Fractures and Degenerative Joint Disease Through Research

 

Research

This research shows that a novel 0.55T MRI system significantly reduces metal related artifacts compared to standard 1.5T and 3T imaging, enabling clearer visualization of spinal and…

Using MRI, our research shows that sustained synovitis is linked to greater progression of knee osteoarthritis, while weight loss slows synovitis progression in part through reductions…

This research developed an AI based tool to measure knee adjacent subcutaneous fat and found that greater fat thickness was linked to cartilage degeneration, increased knee pain, and…

This research developed a machine learning model that combines MRI features, demographics, symptoms, muscle strength, and physical activity to predict the development of radiographic…

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Clinical & Translational Musculoskeletal Imaging  (CTMI)

Thomas Link, MD, PhD
Professor & Musculoskeletal Radiology Division Chief
Director of T32 Program
Clinical Director of MQIR

 

Gabby Joseph, PhD
Professional Researcher