TY - GEN
T1 - Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments
AU - the UNC/UMN Baby Connectome Project Consortium
AU - Huynh, Khoi Minh
AU - Xu, Tiantian
AU - Wu, Ye
AU - Chen, Geng
AU - Thung, Kim Han
AU - Wu, Haiyong
AU - Lin, Weili
AU - Shen, Dinggang
AU - Yap, Pew Thian
N1 - Funding Information:
Acknowledgment. This work was supported in part by NIH grants (NS093842, EB022880, and 1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85075646189&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32248-9_61
DO - 10.1007/978-3-030-32248-9_61
M3 - Conference contribution
AN - SCOPUS:85075646189
SN - 9783030322472
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 547
EP - 555
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -