Robust blood velocity estimation using point-spread-function-based beamforming and multi-step speckle tracking

Abstract

Speckle tracking enables 2D blood velocity estimation; however, imaging at a high frame rate is crucial, since speckle quickly decorrelates due to high velocity gradients. Recently, ultrafast imaging has become possible, which allows imaging at very high frame rates. This improves the accuracy of high velocity blood flow estimation, but hinders the estimation of low velocities, since interframe displacements drop below the data sampling resolution. A lot of effort has already been put into the development of methods to estimate subsample displacements, but still it remains challenging. Ultrafast imaging provides the opportunity to generate ultrasound radio frequency (RF) data at any location covered by the transmit beam. In other words, the spatial sampling of the data can be reconsidered. In this work, a method is proposed which incorporates knowledge of the point spread function (PSF) of the imaging system into the data sampling grid, to improve subsample displacement estimation. By using the dimensions of the PSF to choose the data sampling grid, the peak in the cross-correlation function is captured in a standardized way, which facilitates an optimal match with a 2D cubic interpolation method. Performance of the new approach is evaluated based on simulated data from a carotid artery as well as on in vivo data from the common carotid artery of a healthy volunteer. For a large range of pulse repetition frequencies and beam-to-flow angles, simulation results showed improved performance with respect to results obtained using conventionally spaced RF and envelope data. These findings are corroborated by in vivo results, showing the most consistent flow using the proposed method.