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Synthesizing Dynamic MRI Using Long-Term Recurrent Convolutional Networks

  • Frank Preiswerk
  • Cheng-Chieh Cheng
  • Jie Luo
  • Bruno Madore
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

Abstract

A method is proposed for converting raw ultrasound signals of respiratory organ motion into high frame rate dynamic MRI using a long-term recurrent convolutional neural network. Ultrasound signals were acquired using a single-element transducer, referred to here as ‘organ-configuration motion’ (OCM) sensor, while sagittal MR images were simultaneously acquired. Both streams of data were used for training a cascade of convolutional layers, to extract relevant features from raw ultrasound, followed by a recurrent neural network, to learn its temporal dynamics. The network was trained with MR images on the output, and was employed to predict MR images at a temporal resolution of 100 frames per second, based on ultrasound input alone, without any further MR scanner input. The method was validated on 7 subjects.

Notes

Acknowledgement

Support from grants NIH P41EB015898, R03EB025546, R01CA149342, and R21EB019500 is duly acknowledged. GPU hardware was generously donated by NVIDIA Corporation.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Frank Preiswerk
    • 1
  • Cheng-Chieh Cheng
    • 1
  • Jie Luo
    • 1
    • 2
  • Bruno Madore
    • 1
  1. 1.Brigham and Women’s Hospital, Harvard Medical SchoolBostonUSA
  2. 2.Graduate School of Frontier SciencesThe University of TokyoTokyoJapan

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