Inverse Problem Approach to Aberration Correction for in vivo Transcranial Imaging Based on a Sparse Representation of Contrast-enhanced Ultrasound Data

TL;DR

This paper introduces an inverse problem approach leveraging microbubble sparsity for correcting skull-induced aberrations in transcranial ultrasound imaging, enhancing image contrast and resolution in vivo.

Contribution

The study presents a novel inverse problem method (IPAC) that uses microbubble sparsity and wave propagation knowledge for aberration correction in transcranial ultrasound imaging.

Findings

Improved contrast of CEUS images by 4.6 dB.

Enhanced vessel sharpness and resolution from 21.1 μm to 18.3 μm.

Better hemodynamic quantification of velocity and flow direction.

Abstract

Transcranial ultrasound imaging is currently limited by attenuation and aberration induced by the skull. First used in contrast-enhanced ultrasound (CEUS), highly echoic microbubbles allowed for the development of novel imaging modalities such as ultrasound localization microscopy (ULM). Herein, we develop an inverse problem approach to aberration correction (IPAC) that leverages the sparsity of microbubble signals. We propose to use the \textit{a priori} knowledge of the medium based upon microbubble localization and wave propagation to build a forward model to link the measured signals directly to the aberration function. A standard least-squares inversion is then used to retrieve the aberration function. We first validated IPAC on simulated data of a vascular network using plane wave as well as divergent wave emissions. We then evaluated the reproducibility of IPAC \textit{in vivo} in 5 mouse brains. We showed that aberration correction improved the contrast of CEUS images by 4.6 dB. For ULM images, IPAC yielded sharper vessels, reduced vessel duplications, and improved the resolution from 21.1 $\mu$m to 18.3 $\mu$m. Aberration correction also improved hemodynamic quantification for velocity magnitude and flow direction.