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Beyond classical ultrasound contrast via deep neural networks

 
: Strohm, H.; Rothlübbers, S.; Eickel, K.; Günther, M.

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society:
IEEE International Ultrasonics Symposium, IUS 2020. Symposium Proceedings : September 7-11, 2020, Las Vegas, NV, USA, Online Event
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-5448-0
ISBN: 978-1-7281-5449-7
S.1579-1582
International Ultrasonics Symposium (IUS) <2020, Online>
Englisch
Konferenzbeitrag
Fraunhofer MEVIS ()

Abstract
Classical ultrasound reconstruction applies model driven approaches to obtain ultrasound images from ultrasound raw data. With the emergence of Deep Learning however data driven approaches become feasible and can be explored. These can be used to take shortcuts in the reconstruction, directly learning the relationship between raw data and image data. Even more, entirely new target contrasts can be pursued. In this work we present an approach to train a neural network to reconstruct image data of a classical ultrasound and a novel MR-like contrast from the same ultrasound raw data.

: http://publica.fraunhofer.de/dokumente/N-639413.html