---
_id: '18251'
abstract:
- lang: eng
  text: Magnetic Resonance Imaging (MRI) has long been considered to be among the
    gold standards of today’s diagnostic imaging. The most significant drawback of
    MRI is long acquisition times, prohibiting its use in standard practice for some
    applications. Compressed sensing (CS) proposes to subsample the k-space (the Fourier
    domain dual to the physical space of spatial coordinates) leading to significantly
    accelerated acquisition. However, the benefit of compressed sensing has not been
    fully exploited; most of the sampling densities obtained through CS do not produce
    a trajectory that obeys the stringent constraints of the MRI machine imposed in
    practice. Inspired by recent success of deep learning-based approaches for image
    reconstruction and ideas from computational imaging on learning-based design of
    imaging systems, we introduce 3D FLAT, a novel protocol for data-driven design
    of 3D non-Cartesian accelerated trajectories in MRI. Our proposal leverages the
    entire 3D k-space to simultaneously learn a physically feasible acquisition trajectory
    with a reconstruction method. Experimental results, performed as a proof-of-concept,
    suggest that 3D FLAT achieves higher image quality for a given readout time compared
    to standard trajectories such as radial, stack-of-stars, or 2D learned trajectories
    (trajectories that evolve only in the 2D plane while fully sampling along the
    third dimension). Furthermore, we demonstrate evidence supporting the significant
    benefit of performing MRI acquisitions using non-Cartesian 3D trajectories over
    2D non-Cartesian trajectories acquired slice-wise.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Jonathan
  full_name: Alush-Aben, Jonathan
  last_name: Alush-Aben
- first_name: Linor
  full_name: Ackerman-Schraier, Linor
  last_name: Ackerman-Schraier
- 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: Alexander
  full_name: Bronstein, Alexander
  id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
  last_name: Bronstein
  orcid: 0000-0001-9699-8730
citation:
  ama: 'Alush-Aben J, Ackerman-Schraier L, Weiss T, Vedula S, Senouf O, Bronstein
    AM. 3D FLAT: Feasible learned acquisition trajectories for accelerated MRI. In:
    <i>International Workshop on Machine Learning for Medical Image Reconstruction</i>.
    Vol 12450. Springer Nature; 2020:3-16. doi:<a href="https://doi.org/10.1007/978-3-030-61598-7_1">10.1007/978-3-030-61598-7_1</a>'
  apa: 'Alush-Aben, J., Ackerman-Schraier, L., Weiss, T., Vedula, S., Senouf, O.,
    &#38; Bronstein, A. M. (2020). 3D FLAT: Feasible learned acquisition trajectories
    for accelerated MRI. In <i>International Workshop on Machine Learning for Medical
    Image Reconstruction</i> (Vol. 12450, pp. 3–16). Lima, Peru: Springer Nature.
    <a href="https://doi.org/10.1007/978-3-030-61598-7_1">https://doi.org/10.1007/978-3-030-61598-7_1</a>'
  chicago: 'Alush-Aben, Jonathan, Linor Ackerman-Schraier, Tomer Weiss, Sanketh Vedula,
    Ortal Senouf, and Alex M. Bronstein. “3D FLAT: Feasible Learned Acquisition Trajectories
    for Accelerated MRI.” In <i>International Workshop on Machine Learning for Medical
    Image Reconstruction</i>, 12450:3–16. Springer Nature, 2020. <a href="https://doi.org/10.1007/978-3-030-61598-7_1">https://doi.org/10.1007/978-3-030-61598-7_1</a>.'
  ieee: 'J. Alush-Aben, L. Ackerman-Schraier, T. Weiss, S. Vedula, O. Senouf, and
    A. M. Bronstein, “3D FLAT: Feasible learned acquisition trajectories for accelerated
    MRI,” in <i>International Workshop on Machine Learning for Medical Image Reconstruction</i>,
    Lima, Peru, 2020, vol. 12450, pp. 3–16.'
  ista: 'Alush-Aben J, Ackerman-Schraier L, Weiss T, Vedula S, Senouf O, Bronstein
    AM. 2020. 3D FLAT: Feasible learned acquisition trajectories for accelerated MRI.
    International Workshop on Machine Learning for Medical Image Reconstruction. MLMIR:
    Workshop on Machine Learning for Medical Image Reconstruction, LNCS, vol. 12450,
    3–16.'
  mla: 'Alush-Aben, Jonathan, et al. “3D FLAT: Feasible Learned Acquisition Trajectories
    for Accelerated MRI.” <i>International Workshop on Machine Learning for Medical
    Image Reconstruction</i>, vol. 12450, Springer Nature, 2020, pp. 3–16, doi:<a
    href="https://doi.org/10.1007/978-3-030-61598-7_1">10.1007/978-3-030-61598-7_1</a>.'
  short: J. Alush-Aben, L. Ackerman-Schraier, T. Weiss, S. Vedula, O. Senouf, A.M.
    Bronstein, in:, International Workshop on Machine Learning for Medical Image Reconstruction,
    Springer Nature, 2020, pp. 3–16.
conference:
  end_date: 2020-10-08
  location: Lima, Peru
  name: 'MLMIR: Workshop on Machine Learning for Medical Image Reconstruction'
  start_date: 2020-10-08
date_created: 2024-10-08T13:06:03Z
date_published: 2020-10-20T00:00:00Z
date_updated: 2025-01-23T15:13:44Z
day: '20'
doi: 10.1007/978-3-030-61598-7_1
extern: '1'
intvolume: '     12450'
language:
- iso: eng
month: '10'
oa_version: None
page: 3 - 16
publication: International Workshop on Machine Learning for Medical Image Reconstruction
publication_identifier:
  eisbn:
  - '9783030615987'
  eissn:
  - 1611-3349
  isbn:
  - '9783030615970'
  issn:
  - 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: '3D FLAT: Feasible learned acquisition trajectories for accelerated MRI'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 12450
year: '2020'
...