4.1 Article

Semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging

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METHODSX
卷 7, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.mex.2020.101023

关键词

Diffusion tensor imaging; Hydrocephalus; Intraventricular hemorrhage; Lateral ventricular perimeter; Preterm infant; Subventricular zone; Ventricular zone

资金

  1. Vanier Canada Graduate Scholarship [396212]
  2. National Institutes of Health [K02 NS089852, K23 NS075151-01A1, K23 MH105179, TL1 TR002344, P30 NS098577, R01 HD061619, R01 HD057098]
  3. Child Neurology Foundation
  4. Cerebral Palsy International Research Foundation
  5. Dana Foundation
  6. March of Dimes Prematurity Research Center at Washington University
  7. Doris Duke Charitable Foundation
  8. Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health [U54 HD087011]

向作者/读者索取更多资源

The lateral ventricular perimeter (LVP) of the brain is a critical region because in addition to housing neural stem cells required for brain development, it facilitates cerebrospinal fluid (CSF) bulk flow and functions as a blood-CSF barrier to protect periventricular white matter (PVWM) and other adjacent regions from injurious toxins. LVP injury is common, particularly among preterm infants who sustain intraventricular hemorrhage or post hemorrhagic hydrocephalus and has been associated with poor neurological outcomes. Assessment of the LVP with diffusion MRI has been challenging, primarily due to issues with partial volume artifacts since the LVP region is in close proximity to CSF and other structures of varying signal intensities that may be inadvertently included in LVP segmentation. This research method presents: A novel MATLAB-based method to segment a homogenous LVP layer using high spatial resolution parameters (voxel size 1.2 x 1.2 x 1.2 mm(3)) to only capture the innermost layer of the LVP. The segmented LVP is averaged from three contiguous axial slices to increase signal to noise ratio and reduce the effect of any residual volume averaging effect and eliminates manual and inter/intrarater-related errors. (C) 2020 The Author(s). Published by Elsevier B.V.

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