---
_id: '18249'
abstract:
- lang: eng
  text: Magnetic Resonance Imaging (MRI) is considered today the golden-standard modality
    for soft tissues. The long acquisition times, however, make it more prone to motion
    artifacts as well as contribute to the relative high costs of this examination.
    Over the years, multiple studies concentrated on designing reduced measurement
    schemes and image reconstruction schemes for MRI, however these problems have
    been so far addressed separately. On the other hand, recent works in optical computational
    imaging have demonstrated growing success of simultaneous learning-based design
    of the acquisition and reconstruction schemes manifesting significant improvement
    in the reconstruction quality with a constrained time budget. Inspired by these
    successes, in this work, we propose to learn accelerated MR acquisition schemes
    (in the form of Cartesian trajectories) jointly with the image reconstruction
    operator. To this end, we propose an algorithm for training the combined acquisition-reconstruction
    pipeline end-to-end in a differentiable way. We demonstrate the significance of
    using the learned Cartesian trajectories at different speed up rates. Code available
    at https://github.com/tomer196/fastMRI-Cartesian.
article_number: '9054542'
article_processing_charge: No
arxiv: 1
author:
- first_name: Tomer
  full_name: Weiss, Tomer
  last_name: Weiss
- first_name: Sanketh
  full_name: Vedula, Sanketh
  last_name: Vedula
- first_name: Ortal
  full_name: Senouf, Ortal
  last_name: Senouf
- first_name: Oleg
  full_name: Michailovich, Oleg
  last_name: Michailovich
- first_name: Michael
  full_name: Zibulevsky, Michael
  last_name: Zibulevsky
- first_name: Alexander
  full_name: Bronstein, Alexander
  id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
  last_name: Bronstein
  orcid: 0000-0001-9699-8730
citation:
  ama: 'Weiss T, Vedula S, Senouf O, Michailovich O, Zibulevsky M, Bronstein AM. Joint
    learning of cartesian undersampling and reconstruction for accelerated MRI. In:
    <i>ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal
    Processing (ICASSP)</i>. IEEE; 2020. doi:<a href="https://doi.org/10.1109/icassp40776.2020.9054542">10.1109/icassp40776.2020.9054542</a>'
  apa: 'Weiss, T., Vedula, S., Senouf, O., Michailovich, O., Zibulevsky, M., &#38;
    Bronstein, A. M. (2020). Joint learning of cartesian undersampling and reconstruction
    for accelerated MRI. In <i>ICASSP 2020 - 2020 IEEE International Conference on
    Acoustics, Speech and Signal Processing (ICASSP)</i>. Barcelona, Spain: IEEE.
    <a href="https://doi.org/10.1109/icassp40776.2020.9054542">https://doi.org/10.1109/icassp40776.2020.9054542</a>'
  chicago: Weiss, Tomer, Sanketh Vedula, Ortal Senouf, Oleg Michailovich, Michael
    Zibulevsky, and Alex M. Bronstein. “Joint Learning of Cartesian Undersampling
    and Reconstruction for Accelerated MRI.” In <i>ICASSP 2020 - 2020 IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP)</i>. IEEE, 2020.
    <a href="https://doi.org/10.1109/icassp40776.2020.9054542">https://doi.org/10.1109/icassp40776.2020.9054542</a>.
  ieee: T. Weiss, S. Vedula, O. Senouf, O. Michailovich, M. Zibulevsky, and A. M.
    Bronstein, “Joint learning of cartesian undersampling and reconstruction for accelerated
    MRI,” in <i>ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech
    and Signal Processing (ICASSP)</i>, Barcelona, Spain, 2020.
  ista: Weiss T, Vedula S, Senouf O, Michailovich O, Zibulevsky M, Bronstein AM. 2020.
    Joint learning of cartesian undersampling and reconstruction for accelerated MRI.
    ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal
    Processing (ICASSP). IEEE International Conference on Acoustics, Speech, and Signal
    Processing, 9054542.
  mla: Weiss, Tomer, et al. “Joint Learning of Cartesian Undersampling and Reconstruction
    for Accelerated MRI.” <i>ICASSP 2020 - 2020 IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP)</i>, 9054542, IEEE, 2020, doi:<a href="https://doi.org/10.1109/icassp40776.2020.9054542">10.1109/icassp40776.2020.9054542</a>.
  short: T. Weiss, S. Vedula, O. Senouf, O. Michailovich, M. Zibulevsky, A.M. Bronstein,
    in:, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and
    Signal Processing (ICASSP), IEEE, 2020.
conference:
  end_date: 2020-05-08
  location: Barcelona, Spain
  name: IEEE International Conference on Acoustics, Speech, and Signal Processing
  start_date: 2020-05-04
date_created: 2024-10-08T13:05:24Z
date_published: 2020-04-09T00:00:00Z
date_updated: 2024-12-11T16:06:20Z
day: '09'
doi: 10.1109/icassp40776.2020.9054542
extern: '1'
external_id:
  arxiv:
  - '1905.09324'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.1905.09324
month: '04'
oa: 1
oa_version: Preprint
publication: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech
  and Signal Processing (ICASSP)
publication_identifier:
  eissn:
  - 2379-190X
  isbn:
  - '9781509066322'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Joint learning of cartesian undersampling and reconstruction for accelerated
  MRI
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...