Tomography method employed for detecting early stages of osteoarthritis

When humans age, the cartilage between their bones can wear down, causing the painful condition called osteoarthritis. However, to date noninvasive methods are not yet available to early detect the pathology onset for a routine in-hospital screening. Baer et al. reported a quantitative method that may help medical practitioners detect the early stages of osteoarthritis.

The team’s technique for detecting early stages of cartilage degradation employs a high-resolution, photon-processing computed tomography scanner, known as MARS CT imaging.

“We have shown that photon-processing spectral computed tomography can noninvasively measure and discriminate important markers of cartilage quality,” author Tim Woodfield said.

The MARS CT works by detecting glycosaminoglycans, a key component of healthy tissue that mark degraded parts of cartilage when they are lacking. The authors showed they could distinguish between healthy and diseased regions of human cartilage by showing that different areas of cartilage had higher and lower concentrations of glycosaminoglycans.

This quantitative information could be obtained from a single CT scan and displayed as a color image of cartilage and bone health for clarity of diagnosis. Furthermore, the team verified their color images, which showed a strong inverse correlation between glycosaminoglycans and the contrast agents through the depth of the cartilage, corresponded well to results obtained from using more traditional destructive methods.

The authors are continuing their work by investigating how photon-processing spectral computed tomography could show the health of the bone found under the cartilage, which could further improve the quality of patient outcomes. The researchers also plan to extend their research to human patients, using clinical MRI to compare their results.

Source: “Spectral CT imaging of human osteoarthritic cartilage via quantitative assessment of glycosaminoglycan content using multiple contrast agents,” by Kenzie Baer, Sandra Kieser, Ben Schon, Kishore Rajendran, Timen ten Harkel, Mohser Ramyar, Caroline Löbker, Christopher Bateman, Anthony Butler, Aamir Raja, Gary Hooper, Nigel Anderson, and Tim Woodfield, APL Bioengineering (2021). The article can be accessed at https://doi.org/10.1063/5.0035312 .