Abstract

Matrix imaging paves the way towards a next revolution in wave physics. Based on the response matrix recorded between a set of sensors, it enables an optimized compensation of aberration phenomena and multiple scattering events that usually drastically hinder the focusing process in heterogeneous media. Although it gave rise to spectacular results in optical microscopy or seismic imaging, the success of matrix imaging has been so far relatively limited with ultrasonic waves because wave control is generally only performed with a linear array of transducers. In this paper, we extend ultrasound matrix imaging to a 3D geometry. Switching from a 1D to a 2D probe enables a much sharper estimation of the transmission matrix that links each transducer and each medium voxel. Here, we first present an experimental proof of concept on a tissue-mimicking phantom through ex-vivo tissues and then, show the potential of 3D matrix imaging for transcranial applications.

Ultrasound is a flexible and powerful medical tool. Yet, brain imaging has remained elusive so far for ultrasound due to the blurring induced by the skull. Here, a 3D non-invasive approach is proposed to make the skull digitally transparent and image brain tissues at unprecedented resolution.

Details

Title
Three-dimensional ultrasound matrix imaging
Author
Bureau, Flavien 1 ; Robin, Justine 2 ; Le Ber, Arthur 1   VIAFID ORCID Logo  ; Lambert, William 3   VIAFID ORCID Logo  ; Fink, Mathias 1   VIAFID ORCID Logo  ; Aubry, Alexandre 1   VIAFID ORCID Logo 

 PSL University, CNRS, Institut Langevin, ESPCI Paris, Paris, France (GRID:grid.4444.0) (ISNI:0000 0001 2112 9282) 
 PSL University, CNRS, Institut Langevin, ESPCI Paris, Paris, France (GRID:grid.4444.0) (ISNI:0000 0001 2112 9282); ESPCI Paris, PSL University, INSERM, CNRS, Physics for Medicine, Paris, France (GRID:grid.4444.0) 
 PSL University, CNRS, Institut Langevin, ESPCI Paris, Paris, France (GRID:grid.4444.0) (ISNI:0000 0001 2112 9282); Hologic / SuperSonic Imagine, Aix-en-Provence, France (GRID:grid.510641.7) (ISNI:0000 0004 0498 7420) 
Pages
6793
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2881543459
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.