Share Email Print

Proceedings Paper

Detection of a slow-flow component in contrast-enhanced ultrasound of the synovia for the differential diagnosis of arthritis
Author(s): Gaia Rizzo; Matteo Tonietto; Marco Castellaro; Bernd Raffeiner; Alessandro Coran; Ugo Fiocco; Roberto Stramare; Enrico Grisan
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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%.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013441 (3 March 2017); doi: 10.1117/12.2250818
Show Author Affiliations
Gaia Rizzo, Univ. of Padova (Italy)
Matteo Tonietto, Univ. of Padova (Italy)
Marco Castellaro, Univ. of Padova (Italy)
Bernd Raffeiner, Univ. of Padova (Italy)
Alessandro Coran, IRCCS Veneto Institute of Oncology (Italy)
Ugo Fiocco, Univ. of Padova (Italy)
Roberto Stramare, Univ. of Padova (Italy)
Enrico Grisan, Univ. of Padova (Italy)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?