Deep Learning Based Single-Shot Focused Tissue Harmonic Imaging: an in-Vivo Study

Pulse Inversion and Amplitude Modulation techniques are commonly used to extract the harmonic components in tissue harmonic imaging (THI). However, they are hindered by the reduction in frame rate and susceptibility to motion artefacts due to the necessity of firing two or more successive pulses. In...

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Bibliographic Details
Published in:IEEE International Ultrasonics Symposium (Online) pp. 1 - 4
Main Authors: Fouad, Mariam, Schmitz, Georg
Format: Conference Proceeding
Language:English
Published: IEEE 03.09.2023
Subjects:
Apertures
Checkerboard apertures
Convolutional Autoencoders
Deep learning
DeepH
Firing
Harmonic analysis
Harmonic Imaging
Image quality
Imaging
Measurement
Single shot harmonic imaging
ISSN:1948-5727
Online Access:Get full text
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Summary:Pulse Inversion and Amplitude Modulation techniques are commonly used to extract the harmonic components in tissue harmonic imaging (THI). However, they are hindered by the reduction in frame rate and susceptibility to motion artefacts due to the necessity of firing two or more successive pulses. In our previous work, we showed the ability of deep-learning based techniques to achieve signal-based single-shot harmonic imaging in ex-vivo tissue. Here, we demonstrate the capability of our single-shot approach to achieve comparable results within in-vivo settings in contrast to conventional techniques. Specifically, the proposed approach yields harmonic images with comparable contrast metrics to the conventional checkerboard apertures technique, while achieving an approximate threefold increase in frame rate. Furthermore, we demonstrate the robustness of the approach across different human subjects and in-vivo tissues, alleviating the necessity for retraining. These findings further pave the way for a robust harmonic imaging technique achieving image quality that is on par with existing harmonic imaging methods but with a higher frame rate and fewer motion artifacts.
ISSN:1948-5727
DOI:10.1109/IUS51837.2023.10306903