A novel measure of fractional anisotropy based on the tensor distribution function

Liang Zhan; Alex D. Leow; Siwei Zhu; Marina Barysheva; Arthur W. Toga; Katie L. McMahon; Greig I. De Zubicaray; Margaret J. Wright; Paul M. Thompson

doi:10.1007/978-3-642-04268-3_104

A novel measure of fractional anisotropy based on the tensor distribution function

Liang Zhan, Alex D. Leow, Siwei Zhu, Marina Barysheva, Arthur W. Toga, Katie L. McMahon, Greig I. De Zubicaray, Margaret J. Wright, Paul M. Thompson

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

21 Scopus citations

Abstract

Fractional anisotropy (FA), a very widely used measure of fiber integrity based on diffusion tensor imaging (DTI), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber's anisotropy, and partial volume effects from neighboring gray matter. With High-angular resolution diffusion imaging (HARDI) and the tensor distribution function (TDF), one can reconstruct multiple underlying fibers per voxel and their individual anisotropy measures by representing the diffusion profile as a probabilistic mixture of tensors. We found that FA, when compared with TDF-derived anisotropy measures, correlates poorly with individual fiber anisotropy, and may sub-optimally detect disease processes that affect myelination. By contrast, mean diffusivity (MD) as defined in standard DTI appears to be more accurate. Overall, we argue that novel measures derived from the TDF approach may yield more sensitive and accurate information than DTI-derived measures.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings
Pages	845-852
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-04268-3_104
State	Published - 2009
Externally published	Yes
Event	12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009 - London, United Kingdom Duration: 20 Sep 2009 → 24 Sep 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5761 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
Country/Territory	United Kingdom
City	London
Period	20/09/09 → 24/09/09

Access to Document

10.1007/978-3-642-04268-3_104

Cite this

Zhan, L., Leow, A. D., Zhu, S., Barysheva, M., Toga, A. W., McMahon, K. L., De Zubicaray, G. I., Wright, M. J., & Thompson, P. M. (2009). A novel measure of fractional anisotropy based on the tensor distribution function. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings (PART 1 ed., pp. 845-852). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5761 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-04268-3_104

Zhan, Liang ; Leow, Alex D. ; Zhu, Siwei et al. / A novel measure of fractional anisotropy based on the tensor distribution function. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings. PART 1. ed. 2009. pp. 845-852 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "A novel measure of fractional anisotropy based on the tensor distribution function",

abstract = "Fractional anisotropy (FA), a very widely used measure of fiber integrity based on diffusion tensor imaging (DTI), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber's anisotropy, and partial volume effects from neighboring gray matter. With High-angular resolution diffusion imaging (HARDI) and the tensor distribution function (TDF), one can reconstruct multiple underlying fibers per voxel and their individual anisotropy measures by representing the diffusion profile as a probabilistic mixture of tensors. We found that FA, when compared with TDF-derived anisotropy measures, correlates poorly with individual fiber anisotropy, and may sub-optimally detect disease processes that affect myelination. By contrast, mean diffusivity (MD) as defined in standard DTI appears to be more accurate. Overall, we argue that novel measures derived from the TDF approach may yield more sensitive and accurate information than DTI-derived measures.",

author = "Liang Zhan and Leow, {Alex D.} and Siwei Zhu and Marina Barysheva and Toga, {Arthur W.} and McMahon, {Katie L.} and {De Zubicaray}, {Greig I.} and Wright, {Margaret J.} and Thompson, {Paul M.}",

year = "2009",

doi = "10.1007/978-3-642-04268-3_104",

language = "English",

isbn = "3642042678",

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

number = "PART 1",

pages = "845--852",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings",

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note = "12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009 ; Conference date: 20-09-2009 Through 24-09-2009",

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Zhan, L, Leow, AD, Zhu, S, Barysheva, M, Toga, AW, McMahon, KL, De Zubicaray, GI, Wright, MJ & Thompson, PM 2009, A novel measure of fractional anisotropy based on the tensor distribution function. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5761 LNCS, pp. 845-852, 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009, London, United Kingdom, 20/09/09. https://doi.org/10.1007/978-3-642-04268-3_104

A novel measure of fractional anisotropy based on the tensor distribution function. / Zhan, Liang; Leow, Alex D.; Zhu, Siwei et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings. PART 1. ed. 2009. p. 845-852 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5761 LNCS, No. PART 1).

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

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AU - Zhan, Liang

AU - Leow, Alex D.

AU - Zhu, Siwei

AU - Barysheva, Marina

AU - Toga, Arthur W.

AU - McMahon, Katie L.

AU - De Zubicaray, Greig I.

AU - Wright, Margaret J.

AU - Thompson, Paul M.

PY - 2009

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N2 - Fractional anisotropy (FA), a very widely used measure of fiber integrity based on diffusion tensor imaging (DTI), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber's anisotropy, and partial volume effects from neighboring gray matter. With High-angular resolution diffusion imaging (HARDI) and the tensor distribution function (TDF), one can reconstruct multiple underlying fibers per voxel and their individual anisotropy measures by representing the diffusion profile as a probabilistic mixture of tensors. We found that FA, when compared with TDF-derived anisotropy measures, correlates poorly with individual fiber anisotropy, and may sub-optimally detect disease processes that affect myelination. By contrast, mean diffusivity (MD) as defined in standard DTI appears to be more accurate. Overall, we argue that novel measures derived from the TDF approach may yield more sensitive and accurate information than DTI-derived measures.

AB - Fractional anisotropy (FA), a very widely used measure of fiber integrity based on diffusion tensor imaging (DTI), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber's anisotropy, and partial volume effects from neighboring gray matter. With High-angular resolution diffusion imaging (HARDI) and the tensor distribution function (TDF), one can reconstruct multiple underlying fibers per voxel and their individual anisotropy measures by representing the diffusion profile as a probabilistic mixture of tensors. We found that FA, when compared with TDF-derived anisotropy measures, correlates poorly with individual fiber anisotropy, and may sub-optimally detect disease processes that affect myelination. By contrast, mean diffusivity (MD) as defined in standard DTI appears to be more accurate. Overall, we argue that novel measures derived from the TDF approach may yield more sensitive and accurate information than DTI-derived measures.

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Y2 - 20 September 2009 through 24 September 2009

ER -

Zhan L, Leow AD, Zhu S, Barysheva M, Toga AW, McMahon KL et al. A novel measure of fractional anisotropy based on the tensor distribution function. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings. PART 1 ed. 2009. p. 845-852. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-04268-3_104

A novel measure of fractional anisotropy based on the tensor distribution function

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