A multi-tissue global estimation framework for asymmetric fiber orientation distributions

Ye Wu; Yuanjing Feng; Dinggang Shen; Pew Thian Yap

doi:10.1007/978-3-030-00931-1_6

A multi-tissue global estimation framework for asymmetric fiber orientation distributions

Ye Wu, Yuanjing Feng, Dinggang Shen, Pew Thian Yap

Department of Brain and Cognitive Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

Abstract

In connectomics, tractography involves tracing connections across gray-white matter boundaries in gyral blades of complex cortical convolutions. To date, most tractography algorithms exhibit gyral bias with fiber streamlines preferentially terminating at gyral crowns rather than sulcal banks or fundi. In this work, we will demonstrate that a multi-tissue global estimation framework of the asymmetric fiber orientation distribution function (AFODF) will mitigate the effects of gyral bias and will allow fiber streamlines at gyral blades to make sharper turns into the cortical gray matter. This is validated using in-vivo data from the Human Connectome Project (HCP), showing that, in a typical gyral blade with high curvature, the fiber streamlines estimated using AFODFs bend more naturally into the cortex than FODFs. Furthermore, we show that AFODF tractography results in better cortico-cortical connectivity.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
Editors	Alejandro F. Frangi, Christos Davatzikos, Gabor Fichtinger, Carlos Alberola-López, Julia A. Schnabel
Publisher	Springer Verlag
Pages	45-52
Number of pages	8
ISBN (Print)	9783030009304
DOIs	https://doi.org/10.1007/978-3-030-00931-1_6
Publication status	Published - 2018
Event	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 2018 Sept 16 → 2018 Sept 20

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11072 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	18/9/16 → 18/9/20

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-00931-1_6

Cite this

Wu, Y., Feng, Y., Shen, D., & Yap, P. T. (2018). A multi-tissue global estimation framework for asymmetric fiber orientation distributions. In A. F. Frangi, C. Davatzikos, G. Fichtinger, C. Alberola-López, & J. A. Schnabel (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings (pp. 45-52). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11072 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00931-1_6

A multi-tissue global estimation framework for asymmetric fiber orientation distributions. / Wu, Ye; Feng, Yuanjing; Shen, Dinggang et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. ed. / Alejandro F. Frangi; Christos Davatzikos; Gabor Fichtinger; Carlos Alberola-López; Julia A. Schnabel. Springer Verlag, 2018. p. 45-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11072 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wu, Y, Feng, Y, Shen, D & Yap, PT 2018, A multi-tissue global estimation framework for asymmetric fiber orientation distributions. in AF Frangi, C Davatzikos, G Fichtinger, C Alberola-López & JA Schnabel (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11072 LNCS, Springer Verlag, pp. 45-52, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 18/9/16. https://doi.org/10.1007/978-3-030-00931-1_6

Wu Y, Feng Y, Shen D, Yap PT. A multi-tissue global estimation framework for asymmetric fiber orientation distributions. In Frangi AF, Davatzikos C, Fichtinger G, Alberola-López C, Schnabel JA, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Springer Verlag. 2018. p. 45-52. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00931-1_6

Wu, Ye ; Feng, Yuanjing ; Shen, Dinggang et al. / A multi-tissue global estimation framework for asymmetric fiber orientation distributions. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. editor / Alejandro F. Frangi ; Christos Davatzikos ; Gabor Fichtinger ; Carlos Alberola-López ; Julia A. Schnabel. Springer Verlag, 2018. pp. 45-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In connectomics, tractography involves tracing connections across gray-white matter boundaries in gyral blades of complex cortical convolutions. To date, most tractography algorithms exhibit gyral bias with fiber streamlines preferentially terminating at gyral crowns rather than sulcal banks or fundi. In this work, we will demonstrate that a multi-tissue global estimation framework of the asymmetric fiber orientation distribution function (AFODF) will mitigate the effects of gyral bias and will allow fiber streamlines at gyral blades to make sharper turns into the cortical gray matter. This is validated using in-vivo data from the Human Connectome Project (HCP), showing that, in a typical gyral blade with high curvature, the fiber streamlines estimated using AFODFs bend more naturally into the cortex than FODFs. Furthermore, we show that AFODF tractography results in better cortico-cortical connectivity.",

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A multi-tissue global estimation framework for asymmetric fiber orientation distributions

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