Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments

the UNC/UMN Baby Connectome Project Consortium

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Precise quantification of brain tissue micro-architecture using diffusion MRI is hampered by the conflation of diffusion-attenuated signals from micro-environments that can be orientationally heterogeneous due to complex fiber configurations, such as crossing, fanning, and bending, and compartmentally heterogeneous due to variability in tissue organization. In this paper, we introduce a method, called Spherical Mean Spectrum Imaging (SMSI), for quantification of tissue microstructure. SMSI does not assume a fixed number of compartments, but characterizes the signal as a spectrum of fine- to coarse-scale diffusion processes. Using SMSI, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that SMSI is fast, accurate, and can overcome biases in state-of-the-art microstructure models. We demonstrate its application in probing microstructural changes in the baby brain during the first two years of life.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages547-555
Number of pages9
ISBN (Print)9783030322472
DOIs
Publication statusPublished - 2019 Jan 1
Externally publishedYes
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period19/10/1319/10/17

Fingerprint

Invariant Distribution
Spherical Means
Brain
Imaging
Quantification
Imaging techniques
Tissue
Diffusivity
Microstructure
Magnetic resonance imaging
Diffusion Process
Anisotropy
Fractional
Architecture
Fiber
Configuration
Fibers
Demonstrate

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

the UNC/UMN Baby Connectome Project Consortium (2019). Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, ... S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 547-555). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS). Springer. https://doi.org/10.1007/978-3-030-32248-9_61

Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments. / the UNC/UMN Baby Connectome Project Consortium.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. ed. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer, 2019. p. 547-555 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

the UNC/UMN Baby Connectome Project Consortium 2019, Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11766 LNCS, Springer, pp. 547-555, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 19/10/13. https://doi.org/10.1007/978-3-030-32248-9_61
the UNC/UMN Baby Connectome Project Consortium. Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments. In Shen D, Yap P-T, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer. 2019. p. 547-555. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-32248-9_61
the UNC/UMN Baby Connectome Project Consortium. / Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. editor / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer, 2019. pp. 547-555 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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