TY - GEN
T1 - Characterizing Non-Gaussian Diffusion in Heterogeneously Oriented Tissue Microenvironments
AU - Huynh, Khoi Minh
AU - Xu, Tiantian
AU - Wu, Ye
AU - Thung, Kim Han
AU - Chen, Geng
AU - Lin, Weili
AU - Shen, Dinggang
AU - Yap, Pew Thian
N1 - Funding Information:
This work was supported in part by NIH grants (NS093842 and EB022880).
Funding Information:
This work was supported in part by NIH grants (NS093842 and
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Complex tissue microstructure involving various types of cells and their membranes can deviate the movement of water molecules from the typical Gaussian diffusion. This deviation can be quantified using excess kurtosis to characterize tissue structural complexity. However, true kurtosis measurements can be obscured by complex white matter configurations such as fiber crossing, bending, and branching, which are ubiquitous in the brain. In this paper, we extend diffusion kurtosis imaging (DKI) to allow characterization of diffusional kurtosis in microstructural environments that are oriented heterogeneously. Our method, called microscopic DKI fits a cylindrically symmetric kurtosis model to the spherical mean of the diffusion signal as a function of diffusion weighting. The spherical mean, computed for each b-shell, is invariant to the fiber orientation distribution and is a function of per-axon microstructural properties. Experimental results indicate that DKI yields significantly higher consistency in quantifying microstructure than the conventional DKI in the presence of orientation heterogeneity.
AB - Complex tissue microstructure involving various types of cells and their membranes can deviate the movement of water molecules from the typical Gaussian diffusion. This deviation can be quantified using excess kurtosis to characterize tissue structural complexity. However, true kurtosis measurements can be obscured by complex white matter configurations such as fiber crossing, bending, and branching, which are ubiquitous in the brain. In this paper, we extend diffusion kurtosis imaging (DKI) to allow characterization of diffusional kurtosis in microstructural environments that are oriented heterogeneously. Our method, called microscopic DKI fits a cylindrically symmetric kurtosis model to the spherical mean of the diffusion signal as a function of diffusion weighting. The spherical mean, computed for each b-shell, is invariant to the fiber orientation distribution and is a function of per-axon microstructural properties. Experimental results indicate that DKI yields significantly higher consistency in quantifying microstructure than the conventional DKI in the presence of orientation heterogeneity.
UR - http://www.scopus.com/inward/record.url?scp=85075696305&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32248-9_62
DO - 10.1007/978-3-030-32248-9_62
M3 - Conference contribution
AN - SCOPUS:85075696305
SN - 9783030322472
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 556
EP - 563
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 -