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Abstract #3464

Accelerated Microstructure Quantification by Q-Space Trajectory Imaging Using Machine Learning

Oliver Goedicke1, Frederik B. Laun2, Jan Martin3, Julian Rauch1,4, Peter Neher5, Maximilian R. Rokuss5,6, Mark E. Ladd1,4,7, and Tristan A. Kuder1,4
1Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany, 2Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 3Siemens Healthineers, Erlangen, Germany, 4Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany, 5Division of Medical Image Computing, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany, 6Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany, 7Faculty of Medicine, Heidelberg University, Heidelberg, Germany

Synopsis

Keywords: Microstructure, Machine Learning/Artificial Intelligence, Q-space, QTI, Microscopic Anisotropy, µFA

Motivation: Tensor-encoded diffusion MRI (dMRI) methods for tissue microstructure elucidation typically require lengthy dMRI acquisitions and computationally costly, SNR-sensitive data analysis.

Goal(s): Employing q-space trajectory imaging (QTI), we seek to greatly reduce both the number of required measurements and computational burden in analysis for robust estimation of parameters quantifying brain tissue microstructure.

Approach: A machine learning-based estimator is trained on a 10-fold reduced subset of an extensive dMRI protocol acquired in 18 healthy volunteers.

Results: The proposed method outperforms a state-of-the-art model fitting framework, yielding smoother parameter maps and showing lower deviation from the chosen ground truth, even at reduced SNR/increased resolution.

Impact: Quantitative measures of brain microstructure are obtained by accelerated tensor-encoded diffusion MRI, employing a voxel-wise regression neural network. Observed resilience at reduced voxel size (1.7mm)3 appears promising regarding measurement of parameters such as microscopic fractional anisotropy in a clinical setting.

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Keywords