Windowed Radon Transform and Tensor Rank-1 Decomposition for Adaptive
Beamforming in Ultrafast Ultrasound
- Samuel Beuret ,
- Jean-Philippe Thiran
Abstract
Ultrafast ultrasound has recently emerged as an alternative to
traditional focused ultrasound. By virtue of the low number of
insonifications it requires, ultrafast ultrasound enables the imaging of
the human body at potentially very high frame rates. However,
unaccounted for speed-of-sound variations in the insonified medium often
result in phase aberrations in the reconstructed images. The diagnosis
capability of ultrafast ultrasound is thus ultimately impeded.
Therefore, there is a strong need for adaptive beamforming methods that
are resilient to speed-of-sound aberrations. Several of such techniques
have been proposed recently but they often lack parallelizability or the
ability to directly correct both transmit and receive phase aberrations.
In this article, we introduce an adaptive beamforming method designed to
address these shortcomings. To do so, we compute the windowed Radon
transform of several complex radio-frequency images reconstructed using
delay-and-sum. Then, we apply to the obtained local sinograms weighted
tensor rank-1 decompositions and their results are eventually used to
reconstruct a corrected image. We demonstrate using simulated data that
our method is able to successfully recover aberration-free images and
that it outperforms both coherent compounding and the recently
introduced SVD beamformer. Finally, we validate the proposed beamforming
technique on in-vivo data, resulting in a significant improvement of
image quality compared to the two reference methods.