Applying positivity constraints to q-space trajectory imaging: The QTI+ implementation

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Standard

Applying positivity constraints to q-space trajectory imaging : The QTI+ implementation. / Boito, Deneb; Herberthson, Magnus; Dela Haije, Tom; Özarslan, Evren.

I: SoftwareX, Bind 18, 101030, 06.2022.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Boito, D, Herberthson, M, Dela Haije, T & Özarslan, E 2022, 'Applying positivity constraints to q-space trajectory imaging: The QTI+ implementation', SoftwareX, bind 18, 101030. https://doi.org/10.1016/j.softx.2022.101030

APA

Boito, D., Herberthson, M., Dela Haije, T., & Özarslan, E. (2022). Applying positivity constraints to q-space trajectory imaging: The QTI+ implementation. SoftwareX, 18, [101030]. https://doi.org/10.1016/j.softx.2022.101030

Vancouver

Boito D, Herberthson M, Dela Haije T, Özarslan E. Applying positivity constraints to q-space trajectory imaging: The QTI+ implementation. SoftwareX. 2022 jun.;18. 101030. https://doi.org/10.1016/j.softx.2022.101030

Author

Boito, Deneb ; Herberthson, Magnus ; Dela Haije, Tom ; Özarslan, Evren. / Applying positivity constraints to q-space trajectory imaging : The QTI+ implementation. I: SoftwareX. 2022 ; Bind 18.

Bibtex

@article{9eee547d51704660906b9b4e4415fd2e,
title = "Applying positivity constraints to q-space trajectory imaging: The QTI+ implementation",
abstract = "Diffusion MRI is a powerful technique sensitive to the microstructure of heterogeneous media. By relating the dMRI signal obtained via general gradient waveforms to the moments of an underlying diffusion tensor distribution, q-space trajectory imaging (QTI) provides several quantities indicative of the structural composition of the medium. Substantial improvements in the reliability of the produced estimates has been achieved via incorporating necessary positivity constraints in the estimation by employing Semidefinite Programming. Here we present the Matlab code implementing said constraints, provide a simple example showing the main functionalities of the package, and point to resources within the package that can be used to reproduce results recently published with this software. The block-based structure of our implementation allows the selection of steps to be performed, and facilitates the incorporation of new constraints in future releases.",
keywords = "Constrained estimation, Diffusion MRI, Microscopic anisotropy, QTI",
author = "Deneb Boito and Magnus Herberthson and {Dela Haije}, Tom and Evren {\"O}zarslan",
note = "Publisher Copyright: {\textcopyright} 2022 The Authors",
year = "2022",
month = jun,
doi = "10.1016/j.softx.2022.101030",
language = "English",
volume = "18",
journal = "SoftwareX",
issn = "2352-7110",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Applying positivity constraints to q-space trajectory imaging

T2 - The QTI+ implementation

AU - Boito, Deneb

AU - Herberthson, Magnus

AU - Dela Haije, Tom

AU - Özarslan, Evren

N1 - Publisher Copyright: © 2022 The Authors

PY - 2022/6

Y1 - 2022/6

N2 - Diffusion MRI is a powerful technique sensitive to the microstructure of heterogeneous media. By relating the dMRI signal obtained via general gradient waveforms to the moments of an underlying diffusion tensor distribution, q-space trajectory imaging (QTI) provides several quantities indicative of the structural composition of the medium. Substantial improvements in the reliability of the produced estimates has been achieved via incorporating necessary positivity constraints in the estimation by employing Semidefinite Programming. Here we present the Matlab code implementing said constraints, provide a simple example showing the main functionalities of the package, and point to resources within the package that can be used to reproduce results recently published with this software. The block-based structure of our implementation allows the selection of steps to be performed, and facilitates the incorporation of new constraints in future releases.

AB - Diffusion MRI is a powerful technique sensitive to the microstructure of heterogeneous media. By relating the dMRI signal obtained via general gradient waveforms to the moments of an underlying diffusion tensor distribution, q-space trajectory imaging (QTI) provides several quantities indicative of the structural composition of the medium. Substantial improvements in the reliability of the produced estimates has been achieved via incorporating necessary positivity constraints in the estimation by employing Semidefinite Programming. Here we present the Matlab code implementing said constraints, provide a simple example showing the main functionalities of the package, and point to resources within the package that can be used to reproduce results recently published with this software. The block-based structure of our implementation allows the selection of steps to be performed, and facilitates the incorporation of new constraints in future releases.

KW - Constrained estimation

KW - Diffusion MRI

KW - Microscopic anisotropy

KW - QTI

UR - http://www.scopus.com/inward/record.url?scp=85125867037&partnerID=8YFLogxK

U2 - 10.1016/j.softx.2022.101030

DO - 10.1016/j.softx.2022.101030

M3 - Journal article

AN - SCOPUS:85125867037

VL - 18

JO - SoftwareX

JF - SoftwareX

SN - 2352-7110

M1 - 101030

ER -

ID: 307372256