Inflammatory rheumatic diseases are leading causes of disability and constitute a frequent medical disorder, leading to inability to work, high comorbidity and increased mortality. The gold-standard for diagnosing and differentiating arthritis is based on patient conditions and radiographic findings, as joint erosions or decalcification. However, early signs of arthritis are joint effusion, hypervascularization and synovial hypertrophy. In particular, vascularization has been shown to correlate with arthritis’ destructive behavior, more than clinical assessment. Contrast Enhanced Ultrasound (CEUS) examination of the small joints is emerging as a sensitive tool for assessing vascularization and disease activity. The evaluation of perfusion pattern rely on subjective semi-quantitative scales, that are able to capture the macroscopic degree of vascularization, but are unable to detect the subtler differences in kinetics perfusion parameters that might lead to a deeper understanding of disease progression and a better management of patients. Quantitative assessment is mostly performed by means of the Qontrast software package, that requires the user to define a region of interest, whose mean intensity curve is fitted with an exponential function. We show that using a more physiologically motivated perfusion curve, and by estimating the kinetics parameters separately pixel per pixel, the quantitative information gathered is able to differentiate more effectively different perfusion patterns. In particular, we will show that a pixel-based analysis is able to provide significant markers differentiating rheumatoid arthritis from simil-rheumatoid psoriatic arthritis, that have non-significant differences in clinical evaluation (DAS28), serological markers, or region-based parameters.

Date Published: 20 March 2014
PDF: 6 pages
Proc. SPIE 9040, Medical Imaging 2014: Ultrasonic Imaging and Tomography, 90400F (20 March 2014); doi: 10.1117/12.2042801

Published in SPIE Proceedings Vol. 9040:
Medical Imaging 2014: Ultrasonic Imaging and Tomography
Johan G. Bosch; Marvin M. Doyley, Editor(s)