Methods for ultrasound imaging systems to improve image qualities and to reduce the system cost

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Ultrasound imaging is a safe, relatively simple and inexpensive, and real-time modality for the imaging of soft tissues, and has been used ubiquitously in clinics for many years. It has good spatial resolution and imaging depth. However, poor detectability of low-contrast lesions and the high cost of 3-D ultrasound imaging systems are two significant limitations of ultrasound applications. The applicant's research program will address both of these. The proposed work has two goals: (1) to improve lesion detectability by developing advanced image reconstruction algorithms that will process the pre-beamformed radiofrequency (RF) channel data in Synthetic Aperture imaging (STA). This will achieve better image quality, i.e. spatial resolutions and contrast-noise-ratio, and will help identify small targets, such as nerve, small blood vessels, and small tumors in ultrasound images; (2) to reduce the ultrasound system cost by lowering the number of simultaneous receiving channels and simplifying the transmission system. The applicant's team has obtained some preliminary results that have advanced toward the above two goals in the last three years. In the preliminary investigations we have demonstrated the feasibility of the proposed methods. However, we have only tested the new methods on numerical phantoms and gelatin phantoms in the preliminary experiments. In addition, the decoding algorithms in the new methods are too time-consuming for any industrial or clinical applications due to the high computational complexity. This proposed research represents a natural progression of the above projects. It includes (a) accelerating the proposed algorithms and exploring further methods to improve lesion detectability; (b) considering the effect of aberration and frequency-dependent attenuation on the performance of the image reconstruction algorithms and testing the methods on tissue phantoms; (c) applying compressive sensing (CS) and wavelet transform to synthetic aperture ultrasound imaging to improve imaging system performance; (d) extending the method of reducing receiving channels to the simulations of 3D ultrasound imaging. In the long term, these outputs will advance the algorithms toward future clinical investigations and technology translation.