Multigate doppler signal analysis using 3-D regularized long AR modelling

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.

Towards ultrasound cardiac image segmentation based on the radiofrequency signal

In echocardiography, the radio-frequency (RF) image is a rich source of information about the investigated tissues. Nevertheless, very few works are dedicated to boundary detection based on the RF image, as opposed to envelope image. In this paper, we investigate the feasibility and limitations of boundary detection in echocardiographic images based on the RF signal. We introduce two types of RF-derived parameters: spectral autoregressive parameters and velocity-based parameters, and we propose a discontinuity adaptive framework to perform the detection task. In classical echographic cardiac acquisitions, we show that it is possible to use the spectral contents for boundary detection, and that improvement can be expected with respect to traditional methods. Using the system approach, we study on simulations how the spectral contents can be used for boundary detection. We subsequently perform boundary detection in high frame rate simulated and in vivo cardiac sequences using the variance of velocity, obtaining very promising results. Our work opens the perspective of a RF-based framework for ultrasound cardiac image segmentation and tracking.