Research Interest Groups (RIG) offer the opportunity to engage in lively discussions with colleagues in your area of musculoskeletal research. RIGs foster the thoughtful exchange of ideas and forge collaborative partnerships.
Friday, March 9
Saturday, March 10
Sunday, March 11
7:00 AM – 8:00 AM
Organizers: Samuel Adams, MD, Duke University; L. Daniel Latt, MD, PhD, University of Arizona
With support provided by:
The RIG will advance the science underpinning foot and ankle care by promoting communication and fostering collaborations among individuals interested in foot and ankle science. Its goal is to serve as a network to facilitate the interaction between basic scientists, translational researchers, and clinicians interested in collaborating on studies of foot and ankle science. The objectives are to: 1) hold meetings twice a year where clinicians and scientists can interact to discuss research priorities. 2) promote communication and foster collaboration through face to face meetings as well as online resources such as a member list and an online forum, 3) advocate for foot and ankle research.
6:45 PM – 8:45 PM
Organizers: Dominik Haudenschild, PhD, University of California Davis; Peter van der Kraan, PhD, Radboud University Medical Center
Growth factors are essential to every aspect of orthopaedic research, from tissue development and homeostasis to wound healing, and are critical to tissue-engineering efforts. The RIG goal is to encourage intimate discussions with speakers and attendees on GFs in orthopedic research. One speaker will provide an overview, and 3-5 speakers present exciting new data for discussion. The theme will be how cellular responses to the GFs are directed by the extracellular matrix “context.”
Monday, March 12
6:45 AM – 7:45 AM
Chair: David B Burr, PhD
Co-organizers: Teresita Bellido, PhD and Alex Robling, PhD
With support provided by:
Although magnetic resonance imaging (MRI) of articular cartilage is a standard approach to assess cartilage morphology, novel MRI techniques have been introduced recently to identify early indications in osteoarthritis (OA), when disease-modifying interventions may be most effective. The breakdown and structural loss of major macromolecules such as aggrecan and type II collagen leads to altered strains and material properties within the tissue, suggesting that MRI may be useful to identify biomechanical changes associated with cartilage degeneration. Clinically, relaxometry techniques can be used to image cartilage ultrastructure and evaluate cartilage properties, but also can be used to evaluate meniscus and ligament. Novel techniques have been established to evaluate proteoglycan, include sodium MRI, gagCEST, dGEMRIC, and T1rho, while assessment of collagen is largely confined to T2 and T2* mapping. Each of these techniques has advantages and disadvantages, but their utility in clinical applications can be demonstrated, including use of parametric mapping in comparative effectiveness studies. More robust analysis can be assessed with texture analysis, in which there is a detection of the spatial variation of individual pixel values, rather than just measuring the mean within a chosen tissue voxel. Moreover, deep learning models recently have been applied to solve joint tissue segmentation tasks, relaxation time feature extraction and morphology anomaly detection. Innovations in the big data analytics field have brought several multidimensional visualization methods with which to compare individual patients as a ‘point-cloud’ in multidimensional space, overcoming the inherent limitations of single endpoints. Those approaches allow for the integration of imaging data with other sources. The key aspect of deep learning is that rather than solving a specific task, the models are designed to adapt to specific problems, learning directly from the data and using a general-purpose learning procedure.
These approaches can be applied to cartilage repair, obviating the need for arthroscopic tissue biopsy.
Unfortunately, noninvasive diagnosis of OA remains poor, especially in early disease stages. Several challenges remain, including the need for sensitive and specific imaging biomarkers that predict damage outcomes, and the desire to relate imaging biomarkers to tissue biomechanics. Functional imaging, or the visualization of intra-tissue strain, stress, and material properties, provides an opportunity to probe the evolution of cartilage damage. Automation of post-processing pipelines and more effective feature extraction techniques can exploit the real potential of quantitative compositional MRI techniques.
These concepts will be discussed in the manner of the Musculoskeletal Biology Workshops at Sun Valley in which discussion time equals or exceeds presentation time, questions and discussion do not need to wait until the end of the talk, and there is strong audience participation, especially from young investigators.
Emerging MRI Methods to Detect Functional Changes in Early Osteoarthritis
Corey P. Neu, PhD, University of Colorado, Boulder
Allied use of Quantitative Imaging and Artificial Intelligence to study Osteoarthritis
Valentina Pedoia, PhD, University of California, San Francisco
Clinical MRI Applications
Hollis G. Potter, MD, Hospital for Special Surgery, New York
