Retrieving pulsatility in ultrasound localization microscopy

Retrieving pulsatility in ultrasound localization microscopy

Wiersma, Myrthe (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control)

Heiles, B.G. (mentor)
Kalisvaart, D. (mentor)
Maresca, D. (mentor)
Smith, C.S. (mentor)
van de Plas, R. (graduation committee)
Verbiest, G.J. (graduation committee)

Delft University of Technology

Mechanical Engineering | Systems and Control

2022-05-20

Ultrafast ultrasound localization microscopy (ULM) is a super-resolved vascular imaging method that provides a 10-fold improvement in resolution compared to ultrafast ultrasound Doppler imaging. Because typical ULM acquisitions accumulate large numbers of synthetic microbubble (MB) tracks over hundreds of cardiac cycles, transient hemodynamic variations such as pulsatility get averaged out. Here we introduce two independent processing methods to retrieve pulsatile flow information from MB tracks sampled at kilohertz framerates and demonstrate their potential on a simulated dataset. Our first approach filters out ULM localization grid artifacts and successfully recovers the pulsatility fraction Pf with a root mean square error of 3.3%. Our second approach relies on the derivation of the velocity distribution of MBs as observed from a stationary observer. We show that pulsatile flow gives rise to a bimodal velocity distribution with peaks indicating the maximum and minimum velocity of the cardiac cycle. Measuring the locations of these peaks, we successfully estimated Pf with an error of 5.2%. Last, we evaluated the impact of the MB localization precision σ on our ability to retrieve the bimodal signature of a pulsatile flow. Together, our results demonstrate that pulsatility can be retrieved from high framerate ULM acquisitions and that the estimation of the pulsatility fraction improves with MB localization precision.

ultrasound localization microscopy
pulsatility
localization precision
velocity distribution

http://resolver.tudelft.nl/uuid:21c152eb-aee7-4376-b4bb-70a35b8c02e5

Student theses

master thesis

© 2022 Myrthe Wiersma