An Evolutionary Framework for Microstructure-Sensitive Generalized Diffusion Gradient Waveforms

Raphaël Truffet; Jonathan Rafael-Patino; Gabriel Girard; Marco Pizzolato; Christian Barillot; Jean Philippe Thiran; Emmanuel Caruyer

doi:10.1007/978-3-030-59713-9_10

An Evolutionary Framework for Microstructure-Sensitive Generalized Diffusion Gradient Waveforms

Raphaël Truffet^*, Jonathan Rafael-Patino, Gabriel Girard, Marco Pizzolato, Christian Barillot, Jean Philippe Thiran, Emmanuel Caruyer

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

In diffusion-weighted MRI, general gradient waveforms became of interest for their sensitivity to microstructure features of the brain white matter. However, the design of such waveforms remains an open problem. In this work, we propose a framework for generalized gradient waveform design with optimized sensitivity to selected microstructure features. In particular, we present a rotation-invariant method based on a genetic algorithm to maximize the sensitivity of the signal to the intra-axonal volume fraction. The sensitivity is evaluated by computing a score based on the Fisher information matrix from Monte-Carlo simulations, which offer greater flexibility and realism than conventional analytical models. As proof of concept, we show that the optimized waveforms have higher scores than the conventional pulsed-field gradients experiments. Finally, the proposed framework can be generalized to optimize the waveforms for to any microstructure feature of interest.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention
Editors	Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
Publisher	Springer
Publication date	2020
Pages	94-103
ISBN (Print)	9783030597122
DOIs	https://doi.org/10.1007/978-3-030-59713-9_10
Publication status	Published - 2020
Event	23rd International Conference on Medical Image Computing and Computer Assisted Intervention - Online Event, Lima, Peru Duration: 4 Oct 2020 → 8 Oct 2020 https://www.miccai2020.org/

Conference

Conference	23rd International Conference on Medical Image Computing and Computer Assisted Intervention
Location	Online Event
Country/Territory	Peru
City	Lima
Period	04/10/2020 → 08/10/2020
Internet address	https://www.miccai2020.org/

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12262 LNCS
ISSN	0302-9743

Keywords

Acquisition design
Diffusion MRI
Fisher information
Generalized gradient waveforms
Monte-Carlo simulations

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10.1007/978-3-030-59713-9_10

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Truffet, R., Rafael-Patino, J., Girard, G., Pizzolato, M., Barillot, C., Thiran, J. P., & Caruyer, E. (2020). An Evolutionary Framework for Microstructure-Sensitive Generalized Diffusion Gradient Waveforms. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention (pp. 94-103). Springer. https://doi.org/10.1007/978-3-030-59713-9_10

Truffet, Raphaël ; Rafael-Patino, Jonathan ; Girard, Gabriel et al. / An Evolutionary Framework for Microstructure-Sensitive Generalized Diffusion Gradient Waveforms. Medical Image Computing and Computer Assisted Intervention . editor / Anne L. Martel ; Purang Abolmaesumi ; Danail Stoyanov ; Diana Mateus ; Maria A. Zuluaga ; S. Kevin Zhou ; Daniel Racoceanu ; Leo Joskowicz. Springer, 2020. pp. 94-103 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12262 LNCS).

@inproceedings{d019735a26c04a33b8ed564fd10c9183,

title = "An Evolutionary Framework for Microstructure-Sensitive Generalized Diffusion Gradient Waveforms",

abstract = "In diffusion-weighted MRI, general gradient waveforms became of interest for their sensitivity to microstructure features of the brain white matter. However, the design of such waveforms remains an open problem. In this work, we propose a framework for generalized gradient waveform design with optimized sensitivity to selected microstructure features. In particular, we present a rotation-invariant method based on a genetic algorithm to maximize the sensitivity of the signal to the intra-axonal volume fraction. The sensitivity is evaluated by computing a score based on the Fisher information matrix from Monte-Carlo simulations, which offer greater flexibility and realism than conventional analytical models. As proof of concept, we show that the optimized waveforms have higher scores than the conventional pulsed-field gradients experiments. Finally, the proposed framework can be generalized to optimize the waveforms for to any microstructure feature of interest.",

keywords = "Acquisition design, Diffusion MRI, Fisher information, Generalized gradient waveforms, Monte-Carlo simulations",

author = "Rapha{\"e}l Truffet and Jonathan Rafael-Patino and Gabriel Girard and Marco Pizzolato and Christian Barillot and Thiran, {Jean Philippe} and Emmanuel Caruyer",

year = "2020",

doi = "10.1007/978-3-030-59713-9_10",

language = "English",

isbn = "9783030597122",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Medical Image Computing and Computer Assisted Intervention",

note = "23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",

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Truffet, R, Rafael-Patino, J, Girard, G, Pizzolato, M, Barillot, C, Thiran, JP & Caruyer, E 2020, An Evolutionary Framework for Microstructure-Sensitive Generalized Diffusion Gradient Waveforms. in AL Martel, P Abolmaesumi, D Stoyanov, D Mateus, MA Zuluaga, SK Zhou, D Racoceanu & L Joskowicz (eds), Medical Image Computing and Computer Assisted Intervention . Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12262 LNCS, pp. 94-103, 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, Lima, Peru, 04/10/2020. https://doi.org/10.1007/978-3-030-59713-9_10

An Evolutionary Framework for Microstructure-Sensitive Generalized Diffusion Gradient Waveforms. / Truffet, Raphaël; Rafael-Patino, Jonathan; Girard, Gabriel et al.
Medical Image Computing and Computer Assisted Intervention . ed. / Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Springer, 2020. p. 94-103 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12262 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - An Evolutionary Framework for Microstructure-Sensitive Generalized Diffusion Gradient Waveforms

AU - Truffet, Raphaël

AU - Rafael-Patino, Jonathan

AU - Girard, Gabriel

AU - Pizzolato, Marco

AU - Barillot, Christian

AU - Thiran, Jean Philippe

AU - Caruyer, Emmanuel

PY - 2020

Y1 - 2020

N2 - In diffusion-weighted MRI, general gradient waveforms became of interest for their sensitivity to microstructure features of the brain white matter. However, the design of such waveforms remains an open problem. In this work, we propose a framework for generalized gradient waveform design with optimized sensitivity to selected microstructure features. In particular, we present a rotation-invariant method based on a genetic algorithm to maximize the sensitivity of the signal to the intra-axonal volume fraction. The sensitivity is evaluated by computing a score based on the Fisher information matrix from Monte-Carlo simulations, which offer greater flexibility and realism than conventional analytical models. As proof of concept, we show that the optimized waveforms have higher scores than the conventional pulsed-field gradients experiments. Finally, the proposed framework can be generalized to optimize the waveforms for to any microstructure feature of interest.

AB - In diffusion-weighted MRI, general gradient waveforms became of interest for their sensitivity to microstructure features of the brain white matter. However, the design of such waveforms remains an open problem. In this work, we propose a framework for generalized gradient waveform design with optimized sensitivity to selected microstructure features. In particular, we present a rotation-invariant method based on a genetic algorithm to maximize the sensitivity of the signal to the intra-axonal volume fraction. The sensitivity is evaluated by computing a score based on the Fisher information matrix from Monte-Carlo simulations, which offer greater flexibility and realism than conventional analytical models. As proof of concept, we show that the optimized waveforms have higher scores than the conventional pulsed-field gradients experiments. Finally, the proposed framework can be generalized to optimize the waveforms for to any microstructure feature of interest.

KW - Acquisition design

KW - Diffusion MRI

KW - Fisher information

KW - Generalized gradient waveforms

KW - Monte-Carlo simulations

U2 - 10.1007/978-3-030-59713-9_10

DO - 10.1007/978-3-030-59713-9_10

M3 - Article in proceedings

AN - SCOPUS:85092723578

SN - 9783030597122

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 94

EP - 103

BT - Medical Image Computing and Computer Assisted Intervention

A2 - Martel, Anne L.

A2 - Abolmaesumi, Purang

A2 - Stoyanov, Danail

A2 - Mateus, Diana

A2 - Zuluaga, Maria A.

A2 - Zhou, S. Kevin

A2 - Racoceanu, Daniel

A2 - Joskowicz, Leo

PB - Springer

T2 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention

Y2 - 4 October 2020 through 8 October 2020

ER -

Truffet R, Rafael-Patino J, Girard G, Pizzolato M, Barillot C, Thiran JP et al. An Evolutionary Framework for Microstructure-Sensitive Generalized Diffusion Gradient Waveforms. In Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L, editors, Medical Image Computing and Computer Assisted Intervention . Springer. 2020. p. 94-103. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12262 LNCS). doi: 10.1007/978-3-030-59713-9_10

An Evolutionary Framework for Microstructure-Sensitive Generalized Diffusion Gradient Waveforms

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