High frame-rate cardiac ultrasound imaging with deep learning

TL;DR

This paper introduces a deep learning method to enhance high frame-rate cardiac ultrasound images obtained via multi-line acquisition, reducing artifacts and improving image quality to match single-line acquisition standards.

Contribution

A novel end-to-end neural network approach that significantly improves MLA ultrasound image quality, enabling high frame-rate imaging without artifacts.

Findings

Improved image quality for 5- and 7-line MLA.

Achieved decorrelation measures similar to SLA.

Enhanced frame-rate imaging with reduced artifacts.

Abstract

Cardiac ultrasound imaging requires a high frame rate in order to capture rapid motion. This can be achieved by multi-line acquisition (MLA), where several narrow-focused received lines are obtained from each wide-focused transmitted line. This shortens the acquisition time at the expense of introducing block artifacts. In this paper, we propose a data-driven learning-based approach to improve the MLA image quality. We train an end-to-end convolutional neural network on pairs of real ultrasound cardiac data, acquired through MLA and the corresponding single-line acquisition (SLA). The network achieves a significant improvement in image quality for both $5-$ and $7-$line MLA resulting in a decorrelation measure similar to that of SLA while having the frame rate of MLA.