Multi-channel registration for diffusion mri: longitudinal analysis for the neonatal brain

Alena Uus; Maximilian Pietsch; Irina Grigorescu; Daan Christiaens; Jacques Donald Tournier; Lucilio Cordero Grande; Jana Hutter; David Edwards; Joseph Hajnal; Maria Deprez

doi:10.1007/978-3-030-50120-4_11

Multi-channel registration for diffusion mri: longitudinal analysis for the neonatal brain

Alena Uus^*, Maximilian Pietsch, Irina Grigorescu, Daan Christiaens, Jacques Donald Tournier, Lucilio Cordero Grande, Jana Hutter, David Edwards, Joseph Hajnal, Maria Deprez

^*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

In multi-channel (MC) registration, fusion of structural and diffusion brain MRI provides information on both cortex and white matter (WM) structures thus decreasing the uncertainty of deformation fields. However, the existing solutions employ only diffusion tensor imaging (DTI) derived metrics which are limited by inconsistencies in fiber-crossing regions. In this work, we extend the pipeline for registration of multi-shell high angular resolution diffusion imaging (HARDI) [15] with a novel similarity metric based on angular correlation and an option for multi-channel registration that allows incorporation of structural MRI. The contributions of channels to the displacement field are weighted with spatially varying certainty maps. The implementation is based on MRtrix3 (MRtrix3: https://www.mrtrix.org) toolbox. The approach is quantitatively evaluated on intra-patient longitudinal registration of diffusion MRI datasets of 20 preterm neonates with 7–11 weeks gap between the scans. In addition, we present an example of an MC template generated using the proposed method.

Original language	English
Title of host publication	Biomedical Image Registration - 9th International Workshop, WBIR 2020, Proceedings
Editors	Ziga Spiclin, Jamie McClelland, Jan Kybic, Orcun Goksel
Publisher	SPRINGER
Pages	111-121
Number of pages	11
ISBN (Print)	9783030501198
DOIs	https://doi.org/10.1007/978-3-030-50120-4_11
Publication status	Published - 1 Jan 2020
Event	9th International Workshop on Biomedical Image Registration, WBIR 2020 - Portoroz, Slovenia Duration: 1 Dec 2020 → 2 Dec 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12120 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Workshop on Biomedical Image Registration, WBIR 2020
Country/Territory	Slovenia
City	Portoroz
Period	1/12/2020 → 2/12/2020

Keywords

Certainty maps
Fibre orientation distribution registration
High angular resolution diffusion imaging
Multi-channel registration

Access to Document

10.1007/978-3-030-50120-4_11

Cite this

Uus, A., Pietsch, M., Grigorescu, I., Christiaens, D., Tournier, J. D., Grande, L. C., Hutter, J., Edwards, D., Hajnal, J., & Deprez, M. (2020). Multi-channel registration for diffusion mri: longitudinal analysis for the neonatal brain. In Z. Spiclin, J. McClelland, J. Kybic, & O. Goksel (Eds.), Biomedical Image Registration - 9th International Workshop, WBIR 2020, Proceedings (pp. 111-121). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12120 LNCS). SPRINGER. https://doi.org/10.1007/978-3-030-50120-4_11

Uus, Alena ; Pietsch, Maximilian ; Grigorescu, Irina et al. / Multi-channel registration for diffusion mri : longitudinal analysis for the neonatal brain. Biomedical Image Registration - 9th International Workshop, WBIR 2020, Proceedings. editor / Ziga Spiclin ; Jamie McClelland ; Jan Kybic ; Orcun Goksel. SPRINGER, 2020. pp. 111-121 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inbook{aacb7b9a50c6482ebdf874d50cdcebe3,

title = "Multi-channel registration for diffusion mri: longitudinal analysis for the neonatal brain",

abstract = "In multi-channel (MC) registration, fusion of structural and diffusion brain MRI provides information on both cortex and white matter (WM) structures thus decreasing the uncertainty of deformation fields. However, the existing solutions employ only diffusion tensor imaging (DTI) derived metrics which are limited by inconsistencies in fiber-crossing regions. In this work, we extend the pipeline for registration of multi-shell high angular resolution diffusion imaging (HARDI) [15] with a novel similarity metric based on angular correlation and an option for multi-channel registration that allows incorporation of structural MRI. The contributions of channels to the displacement field are weighted with spatially varying certainty maps. The implementation is based on MRtrix3 (MRtrix3: https://www.mrtrix.org) toolbox. The approach is quantitatively evaluated on intra-patient longitudinal registration of diffusion MRI datasets of 20 preterm neonates with 7–11 weeks gap between the scans. In addition, we present an example of an MC template generated using the proposed method.",

keywords = "Certainty maps, Fibre orientation distribution registration, High angular resolution diffusion imaging, Multi-channel registration",

author = "Alena Uus and Maximilian Pietsch and Irina Grigorescu and Daan Christiaens and Tournier, {Jacques Donald} and Grande, {Lucilio Cordero} and Jana Hutter and David Edwards and Joseph Hajnal and Maria Deprez",

year = "2020",

month = jan,

day = "1",

doi = "10.1007/978-3-030-50120-4_11",

language = "English",

isbn = "9783030501198",

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

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editor = "Ziga Spiclin and Jamie McClelland and Jan Kybic and Orcun Goksel",

booktitle = "Biomedical Image Registration - 9th International Workshop, WBIR 2020, Proceedings",

note = "9th International Workshop on Biomedical Image Registration, WBIR 2020 ; Conference date: 01-12-2020 Through 02-12-2020",

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Uus, A , Pietsch, M, Grigorescu, I, Christiaens, D , Tournier, JD, Grande, LC, Hutter, J, Edwards, D, Hajnal, J & Deprez, M 2020, Multi-channel registration for diffusion mri: longitudinal analysis for the neonatal brain. in Z Spiclin, J McClelland, J Kybic & O Goksel (eds), Biomedical Image Registration - 9th International Workshop, WBIR 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12120 LNCS, SPRINGER, pp. 111-121, 9th International Workshop on Biomedical Image Registration, WBIR 2020, Portoroz, Slovenia, 1/12/2020. https://doi.org/10.1007/978-3-030-50120-4_11

Multi-channel registration for diffusion mri: longitudinal analysis for the neonatal brain. / Uus, Alena ; Pietsch, Maximilian; Grigorescu, Irina et al.
Biomedical Image Registration - 9th International Workshop, WBIR 2020, Proceedings. ed. / Ziga Spiclin; Jamie McClelland; Jan Kybic; Orcun Goksel. SPRINGER, 2020. p. 111-121 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12120 LNCS).

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

TY - CHAP

T1 - Multi-channel registration for diffusion mri

T2 - 9th International Workshop on Biomedical Image Registration, WBIR 2020

AU - Uus, Alena

AU - Pietsch, Maximilian

AU - Grigorescu, Irina

AU - Christiaens, Daan

AU - Tournier, Jacques Donald

AU - Grande, Lucilio Cordero

AU - Hutter, Jana

AU - Edwards, David

AU - Hajnal, Joseph

AU - Deprez, Maria

PY - 2020/1/1

Y1 - 2020/1/1

N2 - In multi-channel (MC) registration, fusion of structural and diffusion brain MRI provides information on both cortex and white matter (WM) structures thus decreasing the uncertainty of deformation fields. However, the existing solutions employ only diffusion tensor imaging (DTI) derived metrics which are limited by inconsistencies in fiber-crossing regions. In this work, we extend the pipeline for registration of multi-shell high angular resolution diffusion imaging (HARDI) [15] with a novel similarity metric based on angular correlation and an option for multi-channel registration that allows incorporation of structural MRI. The contributions of channels to the displacement field are weighted with spatially varying certainty maps. The implementation is based on MRtrix3 (MRtrix3: https://www.mrtrix.org) toolbox. The approach is quantitatively evaluated on intra-patient longitudinal registration of diffusion MRI datasets of 20 preterm neonates with 7–11 weeks gap between the scans. In addition, we present an example of an MC template generated using the proposed method.

AB - In multi-channel (MC) registration, fusion of structural and diffusion brain MRI provides information on both cortex and white matter (WM) structures thus decreasing the uncertainty of deformation fields. However, the existing solutions employ only diffusion tensor imaging (DTI) derived metrics which are limited by inconsistencies in fiber-crossing regions. In this work, we extend the pipeline for registration of multi-shell high angular resolution diffusion imaging (HARDI) [15] with a novel similarity metric based on angular correlation and an option for multi-channel registration that allows incorporation of structural MRI. The contributions of channels to the displacement field are weighted with spatially varying certainty maps. The implementation is based on MRtrix3 (MRtrix3: https://www.mrtrix.org) toolbox. The approach is quantitatively evaluated on intra-patient longitudinal registration of diffusion MRI datasets of 20 preterm neonates with 7–11 weeks gap between the scans. In addition, we present an example of an MC template generated using the proposed method.

KW - Certainty maps

KW - Fibre orientation distribution registration

KW - High angular resolution diffusion imaging

KW - Multi-channel registration

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U2 - 10.1007/978-3-030-50120-4_11

DO - 10.1007/978-3-030-50120-4_11

M3 - Conference paper

AN - SCOPUS:85087041061

SN - 9783030501198

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

SP - 111

EP - 121

BT - Biomedical Image Registration - 9th International Workshop, WBIR 2020, Proceedings

A2 - Spiclin, Ziga

A2 - McClelland, Jamie

A2 - Kybic, Jan

A2 - Goksel, Orcun

PB - SPRINGER

Y2 - 1 December 2020 through 2 December 2020

ER -

Uus A , Pietsch M, Grigorescu I, Christiaens D , Tournier JD, Grande LC et al. Multi-channel registration for diffusion mri: longitudinal analysis for the neonatal brain. In Spiclin Z, McClelland J, Kybic J, Goksel O, editors, Biomedical Image Registration - 9th International Workshop, WBIR 2020, Proceedings. SPRINGER. 2020. p. 111-121. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-50120-4_11

Multi-channel registration for diffusion mri: longitudinal analysis for the neonatal brain

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