Linear, planar and spherical tensor-valued diffusion MRI data by free waveform encoding in healthy brain, water, oil and liquid crystals
Szczepankiewicz, Filip LU
; Hoge, Scott
and Westin, Carl-Fredrik
(2019)
In Data in Brief
25.
p.1-10
- Abstract
Recently, several biophysical models and signal representations have been proposed for microstructure imaging based on tensor-valued, or multidimensional, diffusion MRI. The acquisition of the necessary data requires non-conventional pulse sequences, and data is therefore not available to the wider diffusion MRI community. To facilitate exploration and development of analysis techniques based on tensor-valued diffusion encoding, we share a comprehensive data set acquired in a healthy human brain. The data encompasses diffusion weighted images using linear, planar and spherical diffusion tensor encoding at multiple b-values and diffusion encoding directions. We also supply data acquired in several phantoms that may support validation.... (More)
Recently, several biophysical models and signal representations have been proposed for microstructure imaging based on tensor-valued, or multidimensional, diffusion MRI. The acquisition of the necessary data requires non-conventional pulse sequences, and data is therefore not available to the wider diffusion MRI community. To facilitate exploration and development of analysis techniques based on tensor-valued diffusion encoding, we share a comprehensive data set acquired in a healthy human brain. The data encompasses diffusion weighted images using linear, planar and spherical diffusion tensor encoding at multiple b-values and diffusion encoding directions. We also supply data acquired in several phantoms that may support validation. The data is hosted by GitHub: https://github.com/filip-szczepankiewicz/Szczepankiewicz_DIB_2019.
(Less)
- author
- Szczepankiewicz, Filip
LU
; Hoge, Scott
and Westin, Carl-Fredrik
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- in
- Data in Brief
- volume
- 25
- article number
- 104208
- pages
- 1 - 10
- publisher
- Elsevier
- external identifiers
-
- pmid:31338402
- scopus:85068577622
- ISSN
- 2352-3409
- DOI
- 10.1016/j.dib.2019.104208
- language
- English
- LU publication?
- no
- id
- b99f4831-bffc-47d8-b4eb-c2dbfc7bf752
- date added to LUP
- 2022-04-04 12:36:21
- date last changed
- 2025-07-02 05:56:48
@article{b99f4831-bffc-47d8-b4eb-c2dbfc7bf752,
abstract = {{<p>Recently, several biophysical models and signal representations have been proposed for microstructure imaging based on tensor-valued, or multidimensional, diffusion MRI. The acquisition of the necessary data requires non-conventional pulse sequences, and data is therefore not available to the wider diffusion MRI community. To facilitate exploration and development of analysis techniques based on tensor-valued diffusion encoding, we share a comprehensive data set acquired in a healthy human brain. The data encompasses diffusion weighted images using linear, planar and spherical diffusion tensor encoding at multiple b-values and diffusion encoding directions. We also supply data acquired in several phantoms that may support validation. The data is hosted by GitHub: https://github.com/filip-szczepankiewicz/Szczepankiewicz_DIB_2019.</p>}},
author = {{Szczepankiewicz, Filip and Hoge, Scott and Westin, Carl-Fredrik}},
issn = {{2352-3409}},
language = {{eng}},
pages = {{1--10}},
publisher = {{Elsevier}},
series = {{Data in Brief}},
title = {{Linear, planar and spherical tensor-valued diffusion MRI data by free waveform encoding in healthy brain, water, oil and liquid crystals}},
url = {{http://dx.doi.org/10.1016/j.dib.2019.104208}},
doi = {{10.1016/j.dib.2019.104208}},
volume = {{25}},
year = {{2019}},
}