---
_id: '18330'
abstract:
- lang: eng
  text: With increasingly sophisticated Diffusion Weighted MRI acquisition methods
    and modeling techniques, very large sets of streamlines (fibers) are presently
    generated per imaged brain. These reconstructions of white matter architecture,
    which are important for human brain research and pre-surgical planning, require
    a large amount of storage and are often unwieldy and difficult to manipulate and
    analyze. This work proposes a novel continuous parsimonious framework in which
    signals are sparsely represented in a dictionary with continuous atoms. The significant
    innovation in our new methodology is the ability to train such continuous dictionaries,
    unlike previous approaches that either used pre-fixed continuous transforms or
    training with finite atoms. This leads to an innovative fiber representation method,
    which uses Continuous Dictionary Learning to sparsely code each fiber with high
    accuracy. This method is tested on numerous tractograms produced from the Human
    Connectome Project data and achieves state-of-the-art performances in compression
    ratio and reconstruction error.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Guy
  full_name: Alexandroni, Guy
  last_name: Alexandroni
- first_name: Yana
  full_name: Podolsky, Yana
  last_name: Podolsky
- first_name: Hayit
  full_name: Greenspan, Hayit
  last_name: Greenspan
- first_name: Tal
  full_name: Remez, Tal
  last_name: Remez
- first_name: Or
  full_name: Litany, Or
  last_name: Litany
- first_name: Alexander
  full_name: Bronstein, Alexander
  id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
  last_name: Bronstein
  orcid: 0000-0001-9699-8730
- first_name: Raja
  full_name: Giryes, Raja
  last_name: Giryes
citation:
  ama: 'Alexandroni G, Podolsky Y, Greenspan H, et al. White matter fiber representation
    using continuous dictionary learning. In: <i>20th International Conference on
    Medical Image Computing and Computer-Assisted Intervention</i>. Vol 10433. Springer
    Nature; 2017:566-574. doi:<a href="https://doi.org/10.1007/978-3-319-66182-7_65">10.1007/978-3-319-66182-7_65</a>'
  apa: 'Alexandroni, G., Podolsky, Y., Greenspan, H., Remez, T., Litany, O., Bronstein,
    A. M., &#38; Giryes, R. (2017). White matter fiber representation using continuous
    dictionary learning. In <i>20th International Conference on Medical Image Computing
    and Computer-Assisted Intervention</i> (Vol. 10433, pp. 566–574). Quebec City,
    QC, Canada: Springer Nature. <a href="https://doi.org/10.1007/978-3-319-66182-7_65">https://doi.org/10.1007/978-3-319-66182-7_65</a>'
  chicago: Alexandroni, Guy, Yana Podolsky, Hayit Greenspan, Tal Remez, Or Litany,
    Alex M. Bronstein, and Raja Giryes. “White Matter Fiber Representation Using Continuous
    Dictionary Learning.” In <i>20th International Conference on Medical Image Computing
    and Computer-Assisted Intervention</i>, 10433:566–74. Springer Nature, 2017. <a
    href="https://doi.org/10.1007/978-3-319-66182-7_65">https://doi.org/10.1007/978-3-319-66182-7_65</a>.
  ieee: G. Alexandroni <i>et al.</i>, “White matter fiber representation using continuous
    dictionary learning,” in <i>20th International Conference on Medical Image Computing
    and Computer-Assisted Intervention</i>, Quebec City, QC, Canada, 2017, vol. 10433,
    no. Part 1, pp. 566–574.
  ista: 'Alexandroni G, Podolsky Y, Greenspan H, Remez T, Litany O, Bronstein AM,
    Giryes R. 2017. White matter fiber representation using continuous dictionary
    learning. 20th International Conference on Medical Image Computing and Computer-Assisted
    Intervention. MICCAI: Medical Image Computing and Computer-Assisted Intervention,
    LNCS, vol. 10433, 566–574.'
  mla: Alexandroni, Guy, et al. “White Matter Fiber Representation Using Continuous
    Dictionary Learning.” <i>20th International Conference on Medical Image Computing
    and Computer-Assisted Intervention</i>, vol. 10433, no. Part 1, Springer Nature,
    2017, pp. 566–74, doi:<a href="https://doi.org/10.1007/978-3-319-66182-7_65">10.1007/978-3-319-66182-7_65</a>.
  short: G. Alexandroni, Y. Podolsky, H. Greenspan, T. Remez, O. Litany, A.M. Bronstein,
    R. Giryes, in:, 20th International Conference on Medical Image Computing and Computer-Assisted
    Intervention, Springer Nature, 2017, pp. 566–574.
conference:
  end_date: 2017-09-13
  location: Quebec City, QC, Canada
  name: 'MICCAI: Medical Image Computing and Computer-Assisted Intervention'
  start_date: 2017-09-11
date_created: 2024-10-15T11:20:54Z
date_published: 2017-09-04T00:00:00Z
date_updated: 2025-01-16T16:04:30Z
day: '04'
doi: 10.1007/978-3-319-66182-7_65
extern: '1'
intvolume: '     10433'
issue: Part 1
language:
- iso: eng
month: '09'
oa_version: None
page: 566 - 574
publication: 20th International Conference on Medical Image Computing and Computer-Assisted
  Intervention
publication_identifier:
  eissn:
  - 1611-3349
  - '9783319661827'
  isbn:
  - '9783319661810'
  issn:
  - 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: White matter fiber representation using continuous dictionary learning
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 10433
year: '2017'
...