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
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020/9/29
Y1 - 2020/9/29
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
UR - http://www.scopus.com/inward/record.url?scp=85092723578&partnerID=8YFLogxK
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 – MICCAI 2020 - 23rd International Conference, Proceedings
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 Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Y2 - 4 October 2020 through 8 October 2020
ER -