Detection of a slow-flow component in contrast-enhanced ultrasound of the synovia for the differential diagnosis of arthritis

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%. © 2017 SPIE

Building a reduced dictionary of relevant perfusion patterns from CEUS data for the classification of testis lesions

Radical orchifunicolectomy has traditionally been the main clinical treatment for small testicular masses (STMs); however STMs represent a constantly increasing and often incidental finding. Since many of them result benign, a more conservative testis-sparing surgery was proposed, but it requires a preliminary differentiation between benign and malignant masses: this however remains challenging. Although common understanding in radiology and oncology is that perfusion patterns might provide a useful information about the type of masses, no guidelines or consensus is available for the differentiation of STMs. We propose to build a dictionary of relevant perfusion patterns, extracted using non-negative matrix factorization on pixel-wise time-intensity curves from contrast-enhanced ultrasound data. When data from a lesion are reconstructed using this dictionary, a vector containing the frequency of utilization of each pattern can be used as a tissue signature. Using this signature, a support vector machine classifier has been trained, and the cross validated accuracy reached 100% in our pilot cohort