---
OA_place: repository
OA_type: green
_id: '18242'
abstract:
- lang: eng
text: Fiber tractography is an important tool of computational neuroscience that
enables reconstructing the spatial connectivity and organization of white matter
of the brain. Fiber tractography takes advantage of diffusion Magnetic Resonance
Imaging (dMRI) which allows measuring the apparent diffusivity of cerebral water
along different spatial directions. Unfortunately, collecting such data comes
at the price of reduced spatial resolution and substantially elevated acquisition
times, which limits the clinical applicability of dMRI. This problem has been
thus far addressed using two principal strategies. Most of the efforts have been
extended towards improving the quality of signal estimation for any, yet fixed
sampling scheme (defined through the choice of diffusion-encoding gradients).
On the other hand, optimization over the sampling scheme has also proven to be
effective. Inspired by the previous results, the present work consolidates the
above strategies into a unified estimation framework, in which the optimization
is carried out with respect to both estimation model and sampling design concurrently.
The proposed solution offers substantial improvements in the quality of signal
estimation as well as the accuracy of ensuing analysis by means of fiber tractography.
While proving the optimality of the learned estimation models would probably need
more extensive evaluation, we nevertheless claim that the learned sampling schemes
can be of immediate use, offering a way to improve the dMRI analysis without the
necessity of deploying the neural network used for their estimation. We present
a comprehensive comparative analysis based on the Human Connectome Project data.
Code and learned sampling designs available at https://github.com/tomer196/Learned_dMRI.
alternative_title:
- Mathematics and Visualization
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: 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, Bronstein AM. Towards learned
optimal q-space sampling in diffusion MRI. In: Gyori N, Hutter J, Nath V, Palombo
M, Pizzolato M, Zhang F, eds. Computational Diffusion MRI. Cham: Springer
Nature; 2021:13-28. doi:10.1007/978-3-030-73018-5_2'
apa: 'Weiss, T., Vedula, S., Senouf, O., Michailovich, O., & Bronstein, A. M.
(2021). Towards learned optimal q-space sampling in diffusion MRI. In N. Gyori,
J. Hutter, V. Nath, M. Palombo, M. Pizzolato, & F. Zhang (Eds.), Computational
Diffusion MRI (pp. 13–28). Cham: Springer Nature. https://doi.org/10.1007/978-3-030-73018-5_2'
chicago: 'Weiss, Tomer, Sanketh Vedula, Ortal Senouf, Oleg Michailovich, and Alex
M. Bronstein. “Towards Learned Optimal Q-Space Sampling in Diffusion MRI.” In
Computational Diffusion MRI, edited by Noemi Gyori, Jana Hutter, Vishwesh
Nath, Marco Palombo, Marco Pizzolato, and Fan Zhang, 13–28. Cham: Springer Nature,
2021. https://doi.org/10.1007/978-3-030-73018-5_2.'
ieee: 'T. Weiss, S. Vedula, O. Senouf, O. Michailovich, and A. M. Bronstein, “Towards
learned optimal q-space sampling in diffusion MRI,” in Computational Diffusion
MRI, N. Gyori, J. Hutter, V. Nath, M. Palombo, M. Pizzolato, and F. Zhang,
Eds. Cham: Springer Nature, 2021, pp. 13–28.'
ista: 'Weiss T, Vedula S, Senouf O, Michailovich O, Bronstein AM. 2021.Towards learned
optimal q-space sampling in diffusion MRI. In: Computational Diffusion MRI. Mathematics
and Visualization, , 13–28.'
mla: Weiss, Tomer, et al. “Towards Learned Optimal Q-Space Sampling in Diffusion
MRI.” Computational Diffusion MRI, edited by Noemi Gyori et al., Springer
Nature, 2021, pp. 13–28, doi:10.1007/978-3-030-73018-5_2.
short: T. Weiss, S. Vedula, O. Senouf, O. Michailovich, A.M. Bronstein, in:, N.
Gyori, J. Hutter, V. Nath, M. Palombo, M. Pizzolato, F. Zhang (Eds.), Computational
Diffusion MRI, Springer Nature, Cham, 2021, pp. 13–28.
conference:
end_date: 2020-10-08
location: Lima, Peru/Virtual
name: 'MICCAI: Conference on Medical Image Computing and Computer-Assisted Intervention'
start_date: 2020-10-08
date_created: 2024-10-08T13:03:26Z
date_published: 2021-09-30T00:00:00Z
date_updated: 2024-10-16T09:51:45Z
day: '30'
doi: 10.1007/978-3-030-73018-5_2
editor:
- first_name: Noemi
full_name: Gyori, Noemi
last_name: Gyori
- first_name: Jana
full_name: Hutter, Jana
last_name: Hutter
- first_name: Vishwesh
full_name: Nath, Vishwesh
last_name: Nath
- first_name: Marco
full_name: Palombo, Marco
last_name: Palombo
- first_name: Marco
full_name: Pizzolato, Marco
last_name: Pizzolato
- first_name: Fan
full_name: Zhang, Fan
last_name: Zhang
extern: '1'
external_id:
arxiv:
- '2009.03008'
fulldoi: https://doi.org/10.1007/978-3-030-73018-5_2
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2009.03008
month: '09'
oa: 1
oa_version: Preprint
page: 13-28
place: Cham
publication: Computational Diffusion MRI
publication_identifier:
eisbn:
- '9783030730185'
isbn:
- '9783030730178'
issn:
- 1612-3786
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/tomer196/Learned_dMRI
scopus_import: '1'
status: public
title: Towards learned optimal q-space sampling in diffusion MRI
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...