Ultrafast Cardiac Imaging Using Deep Learning For Speckle-Tracking Echocardiography

TL;DR

This paper introduces a deep learning method that reconstructs high-quality ultrafast cardiac ultrasound images from minimal data, enabling accurate speckle tracking comparable to traditional methods with more data, thus improving speed and motion analysis.

Contribution

It presents a novel CNN-based approach for ultrafast cardiac imaging that maintains motion tracking accuracy with significantly fewer transmissions, outperforming existing methods.

Findings

CNN achieves image quality similar to 31 DWs using only 3 DWs

Method outperforms state-of-the-art MoCo at high velocities

Consistent improvement in in vivo datasets

Abstract

High-quality ultrafast ultrasound imaging is based on coherent compounding from multiple transmissions of plane waves (PW) or diverging waves (DW). However, compounding results in reduced frame rate, as well as destructive interferences from high-velocity tissue motion if motion compensation (MoCo) is not considered. While many studies have recently shown the interest of deep learning for the reconstruction of high-quality static images from PW or DW, its ability to achieve such performance while maintaining the capability of tracking cardiac motion has yet to be assessed. In this paper, we addressed such issue by deploying a complex-weighted convolutional neural network (CNN) for image reconstruction and a state-of-the-art speckle tracking method. The evaluation of this approach was first performed by designing an adapted simulation framework, which provides specific reference data, i.e. high quality, motion artifact-free cardiac images. The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts. The performance was then further evaluated on non-simulated, experimental in vitro data, using a spinning disk phantom. This experiment demonstrated that our approach yielded high-quality image reconstruction and motion estimation, under a large range of velocities and outperforms a state-of-the-art MoCo-based approach at high velocities. Our method was finally assessed on in vivo datasets and showed consistent improvement in image quality and motion estimation compared to standard compounding. This demonstrates the feasibility and effectiveness of deep learning reconstruction for ultrafast speckle-tracking echocardiography.