A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation

Registration of diffusion-weighted magnetic resonance images (DW-MRIs) is a key step for population studies, or construction of brain atlases, among other important tasks. Given the high dimensionality of the data, registration is usually performed by relying on scalar representative images, such as...

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Main Authors:	Duarte-Carvajalino, Julio M., Sapiro, Guillermo, Harel, Noam, Lenglet, Christophe
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3625902/

id	pubmed-3625902
recordtype	oai_dc
spelling	pubmed-36259022013-04-17 A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation Duarte-Carvajalino, Julio M. Sapiro, Guillermo Harel, Noam Lenglet, Christophe Neuroscience Registration of diffusion-weighted magnetic resonance images (DW-MRIs) is a key step for population studies, or construction of brain atlases, among other important tasks. Given the high dimensionality of the data, registration is usually performed by relying on scalar representative images, such as the fractional anisotropy (FA) and non-diffusion-weighted (b0) images, thereby ignoring much of the directional information conveyed by DW-MR datasets itself. Alternatively, model-based registration algorithms have been proposed to exploit information on the preferred fiber orientation(s) at each voxel. Models such as the diffusion tensor or orientation distribution function (ODF) have been used for this purpose. Tensor-based registration methods rely on a model that does not completely capture the information contained in DW-MRIs, and largely depends on the accurate estimation of tensors. ODF-based approaches are more recent and computationally challenging, but also better describe complex fiber configurations thereby potentially improving the accuracy of DW-MRI registration. A new algorithm based on angular interpolation of the diffusion-weighted volumes was proposed for affine registration, and does not rely on any specific local diffusion model. In this work, we first extensively compare the performance of registration algorithms based on (i) angular interpolation, (ii) non-diffusion-weighted scalar volume (b0), and (iii) diffusion tensor image (DTI). Moreover, we generalize the concept of angular interpolation (AI) to non-linear image registration, and implement it in the FMRIB Software Library (FSL). We demonstrate that AI registration of DW-MRIs is a powerful alternative to volume and tensor-based approaches. In particular, we show that AI improves the registration accuracy in many cases over existing state-of-the-art algorithms, while providing registered raw DW-MRI data, which can be used for any subsequent analysis. Frontiers Media S.A. 2013-04-04 /pmc/articles/PMC3625902/ /pubmed/23596381 http://dx.doi.org/10.3389/fnins.2013.00041 Text en Copyright © 2013 Duarte-Carvajalino, Sapiro, Harel and Lenglet. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Duarte-Carvajalino, Julio M. Sapiro, Guillermo Harel, Noam Lenglet, Christophe
spellingShingle	Duarte-Carvajalino, Julio M. Sapiro, Guillermo Harel, Noam Lenglet, Christophe A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation
author_facet	Duarte-Carvajalino, Julio M. Sapiro, Guillermo Harel, Noam Lenglet, Christophe
author_sort	Duarte-Carvajalino, Julio M.
title	A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation
title_short	A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation
title_full	A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation
title_fullStr	A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation
title_full_unstemmed	A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation
title_sort	framework for linear and non-linear registration of diffusion-weighted mris using angular interpolation
description	Registration of diffusion-weighted magnetic resonance images (DW-MRIs) is a key step for population studies, or construction of brain atlases, among other important tasks. Given the high dimensionality of the data, registration is usually performed by relying on scalar representative images, such as the fractional anisotropy (FA) and non-diffusion-weighted (b0) images, thereby ignoring much of the directional information conveyed by DW-MR datasets itself. Alternatively, model-based registration algorithms have been proposed to exploit information on the preferred fiber orientation(s) at each voxel. Models such as the diffusion tensor or orientation distribution function (ODF) have been used for this purpose. Tensor-based registration methods rely on a model that does not completely capture the information contained in DW-MRIs, and largely depends on the accurate estimation of tensors. ODF-based approaches are more recent and computationally challenging, but also better describe complex fiber configurations thereby potentially improving the accuracy of DW-MRI registration. A new algorithm based on angular interpolation of the diffusion-weighted volumes was proposed for affine registration, and does not rely on any specific local diffusion model. In this work, we first extensively compare the performance of registration algorithms based on (i) angular interpolation, (ii) non-diffusion-weighted scalar volume (b0), and (iii) diffusion tensor image (DTI). Moreover, we generalize the concept of angular interpolation (AI) to non-linear image registration, and implement it in the FMRIB Software Library (FSL). We demonstrate that AI registration of DW-MRIs is a powerful alternative to volume and tensor-based approaches. In particular, we show that AI improves the registration accuracy in many cases over existing state-of-the-art algorithms, while providing registered raw DW-MRI data, which can be used for any subsequent analysis.
publisher	Frontiers Media S.A.
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3625902/
_version_	1611969967809363968

A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation

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