Doctors can’t see it, but patients sure can feel it: Osteoarthritis occurs when cartilage, the tissue that provides cushioning between bones at every joint in the body, wears away. Patients’ knees are often the first to get it—along with pain and stiffness in that joint.
If clinicians could noninvasively gauge the health of patients’ cartilage, they could use that information to help guide personalized treatment options and ultimately, to improve patients’ lives.
“In the future, we’d like to be able to have a patient who is experiencing joint issues go in for a magnetic resonance scan. Then we’d develop a patient-specific computational model with cartilage material properties accurate to that patient’s joint,” says Corinne Henak, an assistant professor of mechanical engineering at UW-Madison. “This model could enable clinicians to better predict the likelihood of damage progression in the joint, and potentially help inform decisions about surgical procedures for patients.”
Using a magnetic resonance sequence—a specific series of pulsed radio waves—developed by radiology and medical physics colleagues, Henak and her collaborators already have made an important step toward realizing this goal.
The team demonstrated relationships between quantitative magnetic resonance parameters and cartilage material properties. In particular, the researchers found a significant correlation between one magnetic resonance parameter and both the cartilage’s elastic behavior and its energy dissipation. Additionally, they showed that the same magnetic resonance parameter is significantly correlated with the cartilage’s proteoglycan content.
Proteoglycan is a biological molecule that, along with collagen, makes up what’s called the cartilage extracellular matrix. Proteoglycan is of interest, Henak says, because it controls how quickly fluid flows through the matrix.
Importantly, fluid flow is one of the main ways that cartilage dissipates energy without failing.
The findings mean that clinicians might be able to use the magnetic resonance sequence to noninvasively predict cartilage material behavior in the future. “Demonstrating the correlations is a key step toward one day being able to use this approach in a clinical setting,” says Henak.
She and her collaborators detailed their findings in a paper published in the Feb. 12, 2021, issue of the Journal of Biomechanics.
For their research, Henak and her graduate students removed cartilage samples from the patellae of cadaver knee joints. Using a custom tabletop materials test machine in Henak’s lab, the researchers conducted experiments, including applying dynamic loading on the cartilage samples, to test their material properties. Then they analyzed the relationships between the experimental data and the magnetic resonance parameters to identify correlations.
Henak notes that the particular magnetic resonance sequence used in this work can be performed quickly enough to use on patients. With further development, Henak says the approach could potentially help clinicians predict whether or not a surgery would reduce a specific patient’s odds of osteoarthritis developing or progressing.
“It’s those kinds of questions where we can really have an impact with this research and improve people’s lives,” Henak says.
She says cross-campus collaboration at UW-Madison was key to this advance, with researchers from the Departments of Radiology, Medical Physics, and Orthopedics and Rehabilitation in the UW School of Medicine and Public Health making important contributions to the project.
Co-authors on the Journal of Biomechanics paper include UW-Madison mechanical engineering graduate student Matthew Grondin, mechanical engineering PhD graduate Michael Vignos, UW-Madison Department of Medical Physics faculty Alexey Samsonov, Department of Orthopedics and Rehabilitation faculty member and biomedical engineering affiliate Wan-Ju Li, Richard Kijowski of NYU Langone Health, and Fang Liu of the Department of Radiology at Massachusetts General Hospital, Harvard University.
The research was supported by the Clinical and Translational Science Award program through the National Center for Advancing Translational Sciences (grant UL1TR002373), and funding from NIAMS (R01-AR068373), NIBIB (R01-EB027087), GE Healthcare, and NSF GRFP (DGE-1747503, MFV).
Author: Adam Malecek