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Proceedings Paper

Quantitative cartilage imaging using spectral photon-counting detector based computed tomography
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Paper Abstract

Glycosaminoglycans (GAG) in the extracellular matrix of the articular cartilage are biomarkers of cartilage health. Loss of GAG has been associated with early stage osteoarthritis, with zonal depletion of intra-articular GAG levels occurring prior to cartilage degeneration. Detecting this biochemical change in articular cartilage may facilitate early diagnosis of osteoarthritis. GAG is negatively-charged and repels anionic contrast media. Increased uptake of anionic contrast agents could be correlated with depleted GAG levels in the cartilage. Photon-counting detector (PCD) based computed tomography (CT) offers high-resolution imaging and x-ray energy discrimination capabilities. This allows delineation of finer anatomical structures, and the generation of quantitative material maps using energy-resolved CT data. In this study, we demonstrate quantitative GAG imaging in porcine cartilage using a research whole-body PCD-CT system and an anionic contrast agent. Hind knee joints were harvested from euthanized pigs. GAG depletion mimicking early-OA was induced using trypsin treatment. Both the control group and the trypsin-treated group were incubated in an anionic gadolinium contrast prior to PCD-CT scanning. The specimens were scanned at ultra-high resolution using the PCD-CT system at 120kV, 330mAs, and [25, 51] keV energy thresholds. An image-domain material decomposition was employed to generate the mass density map for gadolinium in cartilage using energy-resolved PCD-CT data. The results showed significantly higher gadolinium uptake (p < 0.0001) in the trypsin-treated specimens, compared to the control specimens. We demonstrated high-resolution ex vivo cartilage imaging using PCD-CT to quantify gadolinium uptake in articular cartilage as an inverse marker of GAG.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109531A (15 March 2019); doi: 10.1117/12.2512627
Show Author Affiliations
Kishore Rajendran, Mayo Clinic (United States)
Shengzhen Tao, Mayo Clinic (United States)
Amy Benike, Mayo Clinic (United States)
Shuai Leng, Mayo Clinic (United States)
Cynthia McCollough, Mayo Clinic (United States)

Published in SPIE Proceedings Vol. 10953:
Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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