Beyond Classical Ultrasound Contrast via Deep Neural Networks
- Resource Type
- Authors
- Sven Rothlubbers; Hannah Strohm; Matthias Günther; Klaus Eickel
- Source
- 2020 IEEE International Ultrasonics Symposium (IUS).
- Subject
- medicine.diagnostic_test
business.industry
Computer science
Deep learning
Ultrasound
Contrast (statistics)
Magnetic resonance imaging
01 natural sciences
Image (mathematics)
0103 physical sciences
medicine
Deep neural networks
Computer vision
Artificial intelligence
business
010301 acoustics
- Language
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.