Development and validation of novel approaches for real-time ultrasound vector velocity measurements

Ultrasound imaging techniques have become increasingly successful in the medical field as they provide relatively low cost and totally safe diagnosis. Doppler methods focus on blood flow for the diagnosis and follow-up of cardiovascular diseases. First Doppler methods only measured the axial component of the motion. More recently, advanced methods have solved this problem, by estimating two or even all three velocity components. In this context, high frame rate (HFR) imaging techniques, based on the transmission of plane waves (PW), lead to the reconstruction of 2-D and 3-D vector maps of blood velocity distribution. The aim of this Ph.D. project was to develop novel acquisition schemes and processing methods for advanced ultrasound Doppler systems. Each development step was based on simulations and experimental tests. Simulations were based on Field II©, while experiments were conducted by using the ULA OP 256 open scanner. In particular, the recently proposed 2-D HFR vector flow imaging (VFI) method (DOI: 10.1109/TUFFC.2014.3064), based on the frequency domain for displacement estimation, was thoroughly investigated. Three main issues were addressed: the high underestimation of blood flow velocity observed when examining vessels at great depths, the high computational load, which hindered any real-time implementation and the lack of information about the third velocity component. Specifically, the progressive broadening of the transmitted beam on the elevation plane due to the acoustic lens was demonstrated to be responsible for the underestimation. The computational cost was reduced by processing demodulated and down-sampled baseband data instead of radiofrequency data, and a preliminary real time version of the 2-D VFI method was implemented. It was also found that a more efficient implementation could be obtained by exploiting parallel computing and graphic processing units (GPUs). An expansion circuit board for the ULA-OP 256 hardware, which allocates GPU resources, was thus designed and built. This new system architecture may allow the implementation of even more complex algorithms, such as the 3-D VFI methods. In particular, it will be possible to implement the novel method for 3D VFI that was developed and tested during this Ph.D. project. Such method suitably extended the 2D VFI approach by proposing an efficient estimation strategy that considerably limits the overall computational load.