An autoregressive model-based method for contrast agent detection in ultrasound radiofrequency images

This paper presents a spectral autoregressive method dedicated to the detection of ultrasound contrast agents (USCA) from radiofrequency (rf) data. The method is based on second-order autoregressive (AR) modeling of the rf signal. Contrast agents induce a second harmonic, which may be efficiently detected through the AR spectrum using the magnitude of the second AR spectral peak (SM2). In contrast to multipulse methods that process two or more rf frames, our method processes a single rf frame. The method is tested by numerical simulation and on in vitro data for contrast agent concentrations ranging from 10(3) to 50 x 10(3) bubbles/ml (2 x 10(-6) to 10(-4) volumic concentration) and mechanical index (MI) ranging from 0.1 to 0.36. The results show that the proposed parameter SM2 enables one to detect correctly the contrast agent, in particular at low concentration and MI (the minimum difference in SM2 between tissue and USCA is 10 dB). Furthermore, the in-vitro data demonstrates that an adapted smoothing technique reduces the variability of SM2 and provides accurate and stable segmentation of the contrast agent perfusion region.