Ultrasound Matrix Imaging-Part II: The distortion matrix for aberration correction over multiple isoplanatic patches

TL;DR

This paper introduces a matrix-based method for correcting aberrations in ultrasound imaging caused by medium heterogeneity, significantly improving image quality over multiple isoplanatic patches through wave-front analysis and iterative compensation.

Contribution

It presents a novel approach using the distortion matrix and singular value decomposition to correct high-order aberrations across multiple patches in ultrasound imaging.

Findings

Enhanced image contrast and resolution after aberration correction

Effective compensation of high-order, spatially-distributed aberrations

Strong correlation between isoplanatic modes and tissue structure

Abstract

This is the second article in a series of two which report on a matrix approach for ultrasound imaging in heterogeneous media. This article describes the quantification and correction of aberration, i.e. the distortion of an image caused by spatial variations in the medium speed-of-sound. Adaptive focusing can compensate for aberration, but is only effective over a restricted area called the isoplanatic patch. Here, we use an experimentally-recorded matrix of reflected acoustic signals tosynthesize a set of virtual transducers. We then examine wave propagation between these virtual transducers and an arbitrary correction plane. Such wave-fronts consist of two components: (i) An ideal geometric wave-front linked to diffraction and the input focusing point, and; (ii) Phase distortions induced by the speed-of-sound variations. These distortions are stored in a so-called distortion matrix, the singular value decomposition of which gives access to an optimized focusing law at any point. We show that, by decoupling the aberrations undergone by the outgoing and incoming waves and applying an iterative strategy, compensation for even high-order and spatially-distributed aberrations can be achieved. After a numerical validation of the process, ultrasound matrix imaging (UMI) is applied to the in-vivo imaging of a gallbladder. A map of isoplanatic modes is retrieved and is shown to be strongly correlated with the arrangement of tissues constituting the medium. The corresponding focusing laws yield an ultrasound image with drastically improved contrast and transverse resolution. UMI thus provides a flexible and powerful route towards computational ultrasound.