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Abstract: Beamforming Sub-Sampled Raw Ultrasound Data with DeepFormer

  • Walter SimsonEmail author
  • Magdalini Paschali
  • Guillaume Zahnd
  • Nassir Navab
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Converting reflected sonic signals to an ultrasound image, beaforming, has been traditionally formulated mathematically via the simple process of delay and sum (DAS). Recent research has aimed to improve ultrasound beamforming via advanced mathematical models for increased contrast, resolution and speckle filtering. These formulations, such as minimum variance, add minor improvement over the current real-time, state-of-the-art DAS, while requiring drastically increased computational time and therefore excluding them from wide-spread adoption.

Literatur

  1. 1.
    Simson W, Paschali M, Navab N, et al. Deep learning beamforming for sub-sampled ultrasound data. IEEE Int Ultrasonics Symp (IUS). 2018;.Google Scholar
  2. 2.
    Roy AG, Conjeti S, Navab N, et al. QuickNAT: segmenting MRI neuroanatomy in 20 seconds. ArXiv e-prints. 2018;ArXiv: 1801.04161.Google Scholar
  3. 3.
    Zhao H, Gallo O, Frosio I, et al. Loss functions for image restoration with neural networks. IEEE Trans Comput Imaging. 2017;3(1):47–57.CrossRefGoogle Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Walter Simson
    • 1
    Email author
  • Magdalini Paschali
    • 1
  • Guillaume Zahnd
    • 1
  • Nassir Navab
    • 1
    • 2
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenMünchenDeutschland
  2. 2.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA

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