---
_id: '18283'
abstract:
- lang: eng
  text: Cardiac ultrasound imaging requires a high frame rate in order to capture
    rapid motion. This can be achieved by multi-line acquisition (MLA), where several
    narrow-focused received lines are obtained from each wide-focused transmitted
    line. This shortens the acquisition time at the expense of introducing block artifacts.
    In this paper, we propose a data-driven learning-based approach to improve the
    MLA image quality. We train an end-to-end convolutional neural network on pairs
    of real ultrasound cardiac data, acquired through MLA and the corresponding single-line
    acquisition (SLA). The network achieves a significant improvement in image quality
    for both 5- and 7-line MLA resulting in a decorrelation measure similar to that
    of SLA while having the frame rate of MLA.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Ortal
  full_name: Senouf, Ortal
  last_name: Senouf
- first_name: Sanketh
  full_name: Vedula, Sanketh
  last_name: Vedula
- first_name: Grigoriy
  full_name: Zurakhov, Grigoriy
  last_name: Zurakhov
- first_name: Alexander
  full_name: Bronstein, Alexander
  id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
  last_name: Bronstein
  orcid: 0000-0001-9699-8730
- first_name: Michael
  full_name: Zibulevsky, Michael
  last_name: Zibulevsky
- first_name: Oleg
  full_name: Michailovich, Oleg
  last_name: Michailovich
- first_name: Dan
  full_name: Adam, Dan
  last_name: Adam
- first_name: David
  full_name: Blondheim, David
  last_name: Blondheim
citation:
  ama: 'Senouf O, Vedula S, Zurakhov G, et al. High frame-rate cardiac ultrasound
    imaging with deep learning. In: <i>International Conference on Medical Image Computing
    and Computer Assisted Intervention</i>. Vol 11070. Springer Nature; 2018:126-134.
    doi:<a href="https://doi.org/10.1007/978-3-030-00928-1_15">10.1007/978-3-030-00928-1_15</a>'
  apa: 'Senouf, O., Vedula, S., Zurakhov, G., Bronstein, A. M., Zibulevsky, M., Michailovich,
    O., … Blondheim, D. (2018). High frame-rate cardiac ultrasound imaging with deep
    learning. In <i>International Conference on Medical Image Computing and Computer
    Assisted Intervention</i> (Vol. 11070, pp. 126–134). Granada, Spain: Springer
    Nature. <a href="https://doi.org/10.1007/978-3-030-00928-1_15">https://doi.org/10.1007/978-3-030-00928-1_15</a>'
  chicago: Senouf, Ortal, Sanketh Vedula, Grigoriy Zurakhov, Alex M. Bronstein, Michael
    Zibulevsky, Oleg Michailovich, Dan Adam, and David Blondheim. “High Frame-Rate
    Cardiac Ultrasound Imaging with Deep Learning.” In <i>International Conference
    on Medical Image Computing and Computer Assisted Intervention</i>, 11070:126–34.
    Springer Nature, 2018. <a href="https://doi.org/10.1007/978-3-030-00928-1_15">https://doi.org/10.1007/978-3-030-00928-1_15</a>.
  ieee: O. Senouf <i>et al.</i>, “High frame-rate cardiac ultrasound imaging with
    deep learning,” in <i>International Conference on Medical Image Computing and
    Computer Assisted Intervention</i>, Granada, Spain, 2018, vol. 11070, no. Part
    1, pp. 126–134.
  ista: 'Senouf O, Vedula S, Zurakhov G, Bronstein AM, Zibulevsky M, Michailovich
    O, Adam D, Blondheim D. 2018. High frame-rate cardiac ultrasound imaging with
    deep learning. International Conference on Medical Image Computing and Computer
    Assisted Intervention. MICCAI: Medical Image Computing and Computer Assisted Intervention,
    LNCS, vol. 11070, 126–134.'
  mla: Senouf, Ortal, et al. “High Frame-Rate Cardiac Ultrasound Imaging with Deep
    Learning.” <i>International Conference on Medical Image Computing and Computer
    Assisted Intervention</i>, vol. 11070, no. Part 1, Springer Nature, 2018, pp.
    126–34, doi:<a href="https://doi.org/10.1007/978-3-030-00928-1_15">10.1007/978-3-030-00928-1_15</a>.
  short: O. Senouf, S. Vedula, G. Zurakhov, A.M. Bronstein, M. Zibulevsky, O. Michailovich,
    D. Adam, D. Blondheim, in:, International Conference on Medical Image Computing
    and Computer Assisted Intervention, Springer Nature, 2018, pp. 126–134.
conference:
  end_date: 2018-09-20
  location: Granada, Spain
  name: 'MICCAI: Medical Image Computing and Computer Assisted Intervention'
  start_date: 2018-09-16
date_created: 2024-10-09T07:47:49Z
date_published: 2018-09-14T00:00:00Z
date_updated: 2025-01-23T13:13:01Z
day: '14'
doi: 10.1007/978-3-030-00928-1_15
extern: '1'
fulldoi: https://doi.org/10.1007/978-3-030-00928-1_15
intvolume: '     11070'
issue: Part 1
language:
- iso: eng
month: '09'
oa_version: None
page: 126 - 134
publication: International Conference on Medical Image Computing and Computer Assisted
  Intervention
publication_identifier:
  eisbn:
  - '9783030009281'
  eissn:
  - 1611-3349
  isbn:
  - '9783030009274'
  issn:
  - 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: High frame-rate cardiac ultrasound imaging with deep learning
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 11070
year: '2018'
...