Algorithm better than X-rays in measuring joint disease

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A new semi-automatic 3D imaging algorithm, called joint space mapping, is at least twice as good as detecting tiny changes in the width of joints as the current “gold standard” of joint imaging, which uses X-rays, according to a new study in Scientific Reports.

Joint diseases such as arthritis and gout affect one fourth of all Americans. There is currently no known cure, and the only definitive treatment is joint replacement surgery.

Joint imaging is an important tool in treating the joints because it avoids the need for invasive tissue sampling and can be used to monitor progression of joint disease.

However, X-rays aren’t sensitive enough to uncover subtle changes in the joint, such as narrowing, over time. X-rays also require manual interpretation and measurement of the joint and the space around it.

The researchers, from the University of Cambridge in England developed the joint space mapping algorithm that uses routine clinical computer tomography data to deliver 3D joint space width maps of the joints, and analyzes the images to identify changes in the space between the bones of the joint. Color-coded images produced using the algorithm show where the space between bones is wider or narrower. The algorithm automatically projects the bone onto the nearby femoral surfaces using the 3D imaging data volume. A bright patch appears on the femur (thigh bone) where there’s opposing hip socket bone; there’s a dark patch where there is only soft tissue.

They then tested the technique on the hips of 30 female cadavers that had been donated for research purposes. They found that the algorithm was much better than X-rays in detecting small structural changes in the joints.

The researchers noted that the joint space mapping technique requires no special imaging conditions, just a routine clinical CT acquisition, and can be applied to different joints, It also needs only low doses of radiation, so that it can be used often for monitoring.

Currently, 3D imaging has not yet been approved for use in research trials involving the joints. The researchers hope that this will change in the future and that the new technique could be used to identify joint diseases earlier and treat them before the disease becomes debilitating, says lead study author Tom Turmezei, MD, from the University of Cambridge’s Department of Engineering, now a consultant at the Norfolk and Norwich University Hospital’s Department of Radiology.

“[Joint space mapping] could have an important role in quantification and visualization of joint space for clinical decision making. On a wider scale, it could be used to screen at-risk populations for disease progression, such as those with known arthritis, shape disorders that predispose to accelerated degenerative change, or elite athletes that undertake high stress-loading activity,” the study authors say.

The algorithm could also augment or replace the use of X-rays in assessing joint conditions.

“Once its clinical utility has been established, this reliable CT-based 3D imaging analysis technique could represent an important step forward in quantitative analysis of joint disease as an alternative to 2D radiographic imaging,” the researchers add.

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