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Multiple b-values improve discrimination of cortical gray matter regions using diffusion MRI: an experimental validation with a data-driven approach


Ganepola, Tara; Lee, Yoojin; Alexander, Daniel C; Sereno, Martin I; Nagy, Zoltan (2021). Multiple b-values improve discrimination of cortical gray matter regions using diffusion MRI: an experimental validation with a data-driven approach. Magma, 34(5):677-687.

Abstract

Objective
To investigate whether varied or repeated b-values provide better diffusion MRI data for discriminating cortical areas with a data-driven approach.
Methods
Data were acquired from three volunteers at 1.5T with b-values of 800, 1400, 2000 s/mm$^{2}$ along 64 diffusion-encoding directions. The diffusion signal was sampled from gray matter in seven regions of interest (ROIs). Rotational invariants of the local diffusion profile were extracted as features that characterize local tissue properties. Random forest classification experiments assessed whether classification accuracy improved when data with multiple b-values were used over repeated acquisition of the same (1400 s/mm$^{2}$) b-value to compare all possible pairs of the seven ROIs. Three data sets from the Human Connectome Project were subjected to similar processing and analysis pipelines in eight ROIs.
Results
Three different b-values showed an average improvement in correct classification rates of 5.6% and 4.6%, respectively, in the local and HCP data over repeated measurements of the same b-value. The improvement in correct classification rate reached as high as 16% for individual binary classification experiments between two ROIs. Often using only two of the available three b-values were adequate to make such an improvement in classification rates.
Conclusion
Acquisitions with varying b-values are more suitable for discriminating cortical areas.

Abstract

Objective
To investigate whether varied or repeated b-values provide better diffusion MRI data for discriminating cortical areas with a data-driven approach.
Methods
Data were acquired from three volunteers at 1.5T with b-values of 800, 1400, 2000 s/mm$^{2}$ along 64 diffusion-encoding directions. The diffusion signal was sampled from gray matter in seven regions of interest (ROIs). Rotational invariants of the local diffusion profile were extracted as features that characterize local tissue properties. Random forest classification experiments assessed whether classification accuracy improved when data with multiple b-values were used over repeated acquisition of the same (1400 s/mm$^{2}$) b-value to compare all possible pairs of the seven ROIs. Three data sets from the Human Connectome Project were subjected to similar processing and analysis pipelines in eight ROIs.
Results
Three different b-values showed an average improvement in correct classification rates of 5.6% and 4.6%, respectively, in the local and HCP data over repeated measurements of the same b-value. The improvement in correct classification rate reached as high as 16% for individual binary classification experiments between two ROIs. Often using only two of the available three b-values were adequate to make such an improvement in classification rates.
Conclusion
Acquisitions with varying b-values are more suitable for discriminating cortical areas.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Life Sciences > Biophysics
Health Sciences > Radiological and Ultrasound Technology
Health Sciences > Radiology, Nuclear Medicine and Imaging
Uncontrolled Keywords:Cortical parcellation, Brodmann map, in-vivo histology, diffusion MRI, microstructural imaging
Language:English
Date:1 October 2021
Deposited On:20 May 2021 09:06
Last Modified:07 Sep 2021 01:05
Publisher:Springer
ISSN:0968-5243
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1007/s10334-021-00914-3
Project Information:
  • : FunderSNSF
  • : Grant ID31003A_166118
  • : Project TitleAdvanced non-invasive human in-vivo cortical parcellation using multimodal MRI

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