New Imaging Algorithm Better at Analyzing Joint Diseases than X-rays

Published By : 26 Jun 2018 | Published By : QYRESEARCH

Researchers from the University of Cambridge have developed a novel semi-automatic 3D imaging algorithm, named joint space mapping, which has turned out to be at least two times as good in detecting minute changes in bone joints, such as widening or narrowing, as the traditional gold-standard for joint imaging-X-ray images. Bone joint diseases such as gout and arthritis affect millions across the globe and the conditions do not have a plausible cure, with joint replacement being the only definitive treatment.

Joint imaging plays a key role in the treatment of joints as it helps avoid the need for invasive procedures such as tissue sampling and can help better monitor the advancement of joint diseases. However, X-ray images are not sensitive enough for understanding tiny changes in the joint over time. X-ray imaging also requires manual analysis for measuring and interpreting joints and the space surrounding them.

The new algorithm, on the other hand, makes use of routine data from clinical computed tomography scans for delivering 3-dimension width maps of the space surrounding the joint in question. The algorithm then examines the images for identifying changes, if any, in the space in the middle of the bones in a joint. The algorithm produces color-coded images and shows areas where the space between the bones has become narrower or wider.

The technique was tested for the hip joint of 30 female bodies that were donated for the purpose of the research. It was found that the algorithm proved to be much better as compared to X-ray images when it came to examining minute structure changes in the joints in focus. More than the fact that the technique can be plausibly used for examining different joints, the technique also requires the use of only low radiation doses, making it safer for monitoring bones often.

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