DeepNLCI: A Deep Learning Framework for Single-Shot Nonlinear Contrast Imaging in Medical Ultrasound.
Authors
Abstract
Nonlinear imaging of contrast agents has emerged as a prominent technique in medical ultrasound (US) applications, owing to its enhanced contrast sensitivity and minimized tissue signal interference in comparison to fundamental imaging. This advancement has facilitated the widespread adoption of contrast-enhanced US (CEUS) imaging in various fields, including visualizing vascular architecture within scanned organs. Nevertheless, challenges persist in extracting the nonlinear signal of the contrast agents from the backscattered echo, with conventional methods facing spectral leakage in case of linear filtering, or M-fold reduction in frame rate and increased motion artifacts in case of M-pulse techniques. To address these challenges, we propose a signal-based deep learning framework for single-shot nonlinear contrast imaging (NLCI) in CEUS applications. Here, the network is trained to predict the subsequent pulse echoes required for different multipulse harmonic imaging techniques from a single pulse-echo acquisition. The proposed method yields comparable quality and contrast metrics to conventional M-pulse techniques, including pulse inversion (PI), amplitude modulation (AM), and checkerboard apertures harmonic imaging, across phantoms and in vivo data. This is achieved while attaining an improved framerate by a factor of $M$ , reduced susceptibility to motion artifacts, and lower noise levels.