Ultrasound is an important medical imaging modality because of its non-invasive, real-time, low-cost nature, and its ability to delineate soft tissues. As digital signal processing hardware becomes more sophisticated and more economical, it is now possible synthesize complex transmission waveforms to enhance image quality. This research studies the design and optimization of ultrasound waveforms, and the accompanying signal processing techniques and image formation algorithms.
The investigator considers three types of waveform design problems with different objectives. (1) Coded excitation pulse with the objective of maximizing SNR, which is most appropriate in object detection applications. The primary approach here is coded modulation technique addressing the unique frequency dependent attenuation of ultrasound waves. (2) Adaptive pulse selection with the objective of maximizing mutual information between the image and the transmitted waveform. Such waveforms are optimal for imaging extended objects such as biological tissues. The algorithm is to successively compute new transmission pulse based on previous returns, thus making the transmission pulses object dependent. (3) Design of novel beamforming basis functions with the objective of maximizing spatial resolution. Standard delay-and-sum beamforming is intuitive and easy to implement, however it is not optimized for spatial resolution particularly in the near field. The investigator's approach establishes rigorous mathematical objective functions quantifying resolution and uses modern optimization tools to obtain the solutions of optimal spatiotemporal basis functions for image formation.
