Constrained optimization of gradient waveforms for generalized diffusion encoding.
(2015) In Journal of Magnetic Resonance 261. p.157-168- Abstract
- Diffusion MRI is a useful probe of tissue microstructure. The conventional diffusion encoding sequence, the single pulsed field gradient, has recently been challenged as more general gradient waveforms have been introduced. Out of these, we focus on q-space trajectory imaging, which generalizes the scalar b-value to a tensor valued entity. To take full advantage of its capabilities, it is imperative to respect the constraints imposed by the hardware, while at the same time maximizing the diffusion encoding strength. We provide a tool that achieves this by solving a constrained optimization problem that accommodates constraints on maximum gradient amplitude, slew rate, coil heating and positioning of radio frequency pulses. The method's... (More)
- Diffusion MRI is a useful probe of tissue microstructure. The conventional diffusion encoding sequence, the single pulsed field gradient, has recently been challenged as more general gradient waveforms have been introduced. Out of these, we focus on q-space trajectory imaging, which generalizes the scalar b-value to a tensor valued entity. To take full advantage of its capabilities, it is imperative to respect the constraints imposed by the hardware, while at the same time maximizing the diffusion encoding strength. We provide a tool that achieves this by solving a constrained optimization problem that accommodates constraints on maximum gradient amplitude, slew rate, coil heating and positioning of radio frequency pulses. The method's efficacy and flexibility is demonstrated both experimentally and by comparison with previous work on optimization of isotropic diffusion sequences. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8235157
- author
- Sjölund, Jens ; Szczepankiewicz, Filip LU ; Nilsson, Markus LU ; Topgaard, Daniel ; Westin, Carl-Fredrik and Knutsson, Hans
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Magnetic Resonance
- volume
- 261
- pages
- 157 - 168
- publisher
- Academic Press
- external identifiers
-
- pmid:26583528
- wos:000367212100021
- scopus:84946846891
- pmid:26583528
- ISSN
- 1096-0856
- DOI
- 10.1016/j.jmr.2015.10.012
- language
- English
- LU publication?
- yes
- id
- 6ac5317f-3885-4521-8400-3ab3632b34f1 (old id 8235157)
- alternative location
- http://www.ncbi.nlm.nih.gov/pubmed/26583528?dopt=Abstract
- date added to LUP
- 2016-04-01 10:47:19
- date last changed
- 2023-09-18 11:06:22
@article{6ac5317f-3885-4521-8400-3ab3632b34f1, abstract = {{Diffusion MRI is a useful probe of tissue microstructure. The conventional diffusion encoding sequence, the single pulsed field gradient, has recently been challenged as more general gradient waveforms have been introduced. Out of these, we focus on q-space trajectory imaging, which generalizes the scalar b-value to a tensor valued entity. To take full advantage of its capabilities, it is imperative to respect the constraints imposed by the hardware, while at the same time maximizing the diffusion encoding strength. We provide a tool that achieves this by solving a constrained optimization problem that accommodates constraints on maximum gradient amplitude, slew rate, coil heating and positioning of radio frequency pulses. The method's efficacy and flexibility is demonstrated both experimentally and by comparison with previous work on optimization of isotropic diffusion sequences.}}, author = {{Sjölund, Jens and Szczepankiewicz, Filip and Nilsson, Markus and Topgaard, Daniel and Westin, Carl-Fredrik and Knutsson, Hans}}, issn = {{1096-0856}}, language = {{eng}}, pages = {{157--168}}, publisher = {{Academic Press}}, series = {{Journal of Magnetic Resonance}}, title = {{Constrained optimization of gradient waveforms for generalized diffusion encoding.}}, url = {{http://dx.doi.org/10.1016/j.jmr.2015.10.012}}, doi = {{10.1016/j.jmr.2015.10.012}}, volume = {{261}}, year = {{2015}}, }