Title
Automated correction of improperly rotated diffusion gradient orientations in diffusion weighted MRI Automated correction of improperly rotated diffusion gradient orientations in diffusion weighted MRI
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
article
Publication
Amsterdam ,
Subject
Computer. Automation
Source (journal)
Medical image analysis. - Amsterdam
Volume/pages
18(2014) :7 , p. 953-962
ISSN
1361-8415
ISI
000342256200002
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Ensuring one is using the correct gradient orientations in a diffusion MRI study can be a challenging task. As different scanners, file formats and processing tools use different coordinate frame conventions, in practice, users can end up with improperly oriented gradient orientations. Using such wrongly oriented gradient orientations for subsequent diffusion parameter estimation will invalidate all rotationally variant parameters and fiber tractography results. While large misalignments can be detected by visual inspection, small rotations of the gradient table (e.g. due to angulation of the acquisition plane), are much more difficult to detect. In this work, we propose an automated method to align the coordinate frame of the gradient orientations with that of the corresponding diffusion weighted images, using a metric based on whole brain fiber tractography. By transforming the gradient table and measuring the average fiber trajectory length, we search for the transformation that results in the best global 'connectivity'. To ensure a fast calculation of the metric we included a range of algorithmic optimizations in our tractography routine. To make the optimization routine robust to spurious local maxima, we use a stochastic optimization routine that selects a random set of seed points on each evaluation. Using simulations, we show that our method can recover the correct gradient orientations with high accuracy and precision. In addition, we demonstrate that our technique can successfully recover rotated gradient tables on a wide range of clinically realistic data sets. As such, our method provides a practical and robust solution to an often overlooked pitfall in the processing of diffusion MRI. (C) 2014 Elsevier B.V. All rights reserved.
E-info
https://repository.uantwerpen.be/docman/iruaauth/042ddf/5a88649.pdf
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