A Single-Shot Harmonic Imaging Approach Utilizing Deep Learning for Medical Ultrasound.

Tissue harmonic imaging (THI) is an invaluable tool in clinical ultrasound due to its enhanced contrast resolution and reduced reverberation clutter in comparison with fundamental mode imaging. However, harmonic content separation based on high-pass filtering suffers from potential contrast degradation or lower axial resolution due to spectral leakage, whereas nonlinear multipulse harmonic imaging schemes, such as amplitude modulation and pulse inversion, suffer from a reduced frame rate and comparatively higher motion artifacts due to the necessity of at least two pulse echo acquisitions. To address this problem, we propose a deep-learning-based single-shot harmonic imaging technique capable of generating comparable image quality to pulse amplitude modulation methods, yet at a higher frame rate and with fewer motion artifacts. Specifically, an asymmetric convolutional encoder-decoder structure is designed to estimate the combination of the echoes resulting from the half-amplitude transmissions using the echo produced from the full amplitude transmission as input. The echoes were acquired with the checkerboard amplitude modulation technique for training. The model was evaluated across various targets and samples to illustrate generalizability as well as the possibility and impact of transfer learning. Furthermore, for possible interpretability of the network, we investigate if the latent space of the encoder holds information on the nonlinearity parameter of the medium. We demonstrate the ability of the proposed approach to generate harmonic images with a single firing that are comparable to those from a multipulse acquisition.