Deep learning-based reconstruction of ultrasound images from raw channel data

Purpose We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic reconstruction to form, as compared to common reco...

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Veröffentlicht in:	International journal for computer assisted radiology and surgery Jg. 15; H. 9; S. 1487 - 1490
Hauptverfasser:	Strohm, Hannah, Rothlübbers, Sven, Eickel, Klaus, Günther, Matthias
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.09.2020 Springer Nature B.V
Schlagworte:	Algorithms Computer Imaging Computer Science Contrast Media Deep Learning Diagnosis, Computer-Assisted - methods Feasibility Ground truth Health Informatics Humans Image Processing, Computer-Assisted - methods Image quality Image reconstruction Imaging In vivo methods and tests Information retrieval Machine learning Medicine Medicine & Public Health Models, Theoretical Optimization Pattern Recognition and Graphics Phantoms, Imaging Plane waves Radiology Reference Values Reproducibility of Results Short Communication Signal-To-Noise Ratio Surgery Target recognition Ultrasonic imaging Ultrasonic testing Ultrasonography Ultrasound Vision Deep learning Plane wave ultrasound imaging
ISSN:	1861-6410, 1861-6429, 1861-6429
Online-Zugang:	Volltext
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