| Contrast-assisted perfusion estimation is accomplished by detecting contrast agent microbubbles as they are transported via blood vessels into a tissue of interest. Ultrasound contrast agent microbubbles can be destroyed with a destructive imaging mode, and then the inflow of new contrast agent microbubbles can be observed with a non-destructive imaging mode. Mean velocity, the time required for the echo intensity to reach 80% of the original intensity, and vascular density are estimated from the characteristic exponential rise of the received signal intensity from a perfused region of tissue. The imaging process requires three steps: contrast agent destruction, nondestructive detection, and parameter estimation.; The destruction threshold of contrast agent microbubbles is characterized experimentally in order to specify the optimal imaging conditions for the destructive imaging mode. The optimization of fragmentation as a function of imaging parameters and contrast agent resting radius suggests that small resting radius, high transmission pressure, and low transmission frequency increase the probability of destruction.; A novel non-destructive imaging mode is developed, based on subharmonic oscillations that can be generated in contrast agent microbubbles. Excellent contrast agent enhancement can be accomplished with subharmonic imaging, phase-inversion, and a wall filter, yielding a mean contrast agent-to-tissue ratio of 23 dB. Furthermore, subharmonic imaging can be generated in contrast agent microbubbles with imaging conditions that do not cause fragmentation.; A two-step estimation scheme is designed for estimating the initial value, the final value, and the slope of the characteristic rising exponential received echo intensity over time.; The destructive imaging mode, using high pressure and low frequency insonation, and the non-destructive imaging mode, using phase-inversion subharmonic imaging, are implemented on a Siemens Elegra scanner. An in-vitro experimental system is employed to compare the performance of the blood velocity estimator, as predicted by the model, with the performance observed experimentally. (Abstract shortened by UMI.)... |
