3 March 2017 Detection of a slow-flow component in contrast-enhanced ultrasound of the synovia for the differential diagnosis of arthritis
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Abstract
Contrast Enhanced Ultrasound (CEUS) is a sensitive imaging technique to assess tissue vascularity, that can be useful in the quantification of different perfusion patterns. This can particularly important in the early detection and differentiation of different types of arthritis. A Gamma-variate can accurately quantify synovial perfusion and it is flexible enough to describe many heterogeneous patterns. However, in some cases the heterogeneity of the kinetics can be such that even the Gamma model does not properly describe the curve, especially in presence of recirculation or of an additional slowflow component.

In this work we apply to CEUS data both the Gamma-variate and the single compartment recirculation model (SCR) which takes explicitly into account an additional component of slow flow. The models are solved within a Bayesian framework.

We also employed the perfusion estimates obtained with SCR to train a support vector machine classifier to distinguish different types of arthritis. When dividing the patients into two groups (rheumatoid arthritis and polyarticular RA-like psoriatic arthritis vs. other arthritis types), the slow component amplitude was significantly different across groups: mean values of a1 and its variability were statistically higher in RA and RA-like patients (131% increase in mean, p = 0.035 and 73% increase in standard deviation, p = 0.049 respectively). The SVM classifier achieved a balanced accuracy of 89%, with a sensitivity of 100% and a specificity of 78%.
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Gaia Rizzo, Gaia Rizzo, Matteo Tonietto, Matteo Tonietto, Marco Castellaro, Marco Castellaro, Bernd Raffeiner, Bernd Raffeiner, Alessandro Coran, Alessandro Coran, Ugo Fiocco, Ugo Fiocco, Roberto Stramare, Roberto Stramare, Enrico Grisan, Enrico Grisan, } "Detection of a slow-flow component in contrast-enhanced ultrasound of the synovia for the differential diagnosis of arthritis", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013441 (3 March 2017); doi: 10.1117/12.2250818; https://doi.org/10.1117/12.2250818
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