Data-Driven Learning to Detect Characteristic Kinetics in Ultrasound Images of Arthritis

Contrast Enhanced Ultrasound (CEUS) is a sensitive imaging technique to assess synovial vascularization and perfusion, allowing a pixel-wise perfusion quantification that can be used to distinguish different forms of disease and help their early detection. However, the high dimensionality of the perfusion parameter space prevents an easy understanding of the underlying pathological changes in the synovia. In order extract relevant clinical information, we present a data-driven method to identify the perfusions patterns characterizing the different types of arthritis, exploiting a sparse representation obtained from a dictionary of basis signals learned from the data.