» Articles » PMID: 31196859

Quantification of DTI in the Pediatric Spinal Cord: Application to Clinical Evaluation in a Healthy Patient Population

Overview
Specialty Neurology
Date 2019 Jun 15
PMID 31196859
Citations 8
Authors
Affiliations
Soon will be listed here.
Abstract

Background And Purpose: The purpose of the study is to characterize diffusion tensor imaging indices in the developing spinal cord, evaluating differences based on age and cord region. Describing the progression of DTI indices in the pediatric cord increases our understanding of spinal cord development.

Materials And Methods: A retrospective analysis was performed on DTI acquired in 121 pediatric patients (mean, 8.6 years; range, 0.3-18.0 years) at Monroe Carell Jr. Children's Hospital at Vanderbilt from 2017 to 2018. Diffusion-weighted images (15 directions; = 750 s/mm; slice thickness, 5 mm; in-plane resolution, 1.0 × 1.0 mm) were acquired on a 3T scanner in the cervicothoracic and/or thoracolumbar cord. Manual whole-cord segmentation was performed. Images were masked and further segmented into cervical, upper thoracic, thoracolumbar, and conus regions. Analyses of covariance were performed for each DTI-derived index to investigate how age affects diffusion across cord regions, and 95% confidence intervals were calculated across age for each derived index and region. Post hoc testing was performed to analyze regional differences.

Results: Analyses of covariance revealed significant correlations of age with axial diffusivity, mean diffusivity, and fractional anisotropy (all, < .001). There were also significant differences among cord regions for axial diffusivity, radial diffusivity, mean diffusivity, and fractional anisotropy (all, < .001).

Conclusions: This research demonstrates that diffusion evolves in the pediatric spinal cord during development, dependent on both cord region and the diffusion index of interest. Future research could investigate how diffusion may be affected by common pediatric spinal pathologies.

Citing Articles

Harmonization of Longitudinal Diffusion Tensor Imaging Data of the Pediatric Cervical and Thoracic Spinal Cord at 3T Using Longitudinal ComBat.

Li Y, Middleton D, Chen A, Shinohara R, Krisa L, Faro S Res Sq. 2024; .

PMID: 39011114 PMC: 11247925. DOI: 10.21203/rs.3.rs-4536023/v1.


Harmonization of multi-site diffusion tensor imaging data for cervical and thoracic spinal cord at 1.5 T and 3 T using longitudinal ComBat.

Middleton D, Li Y, Chen A, Shinohara R, Fisher J, Krisa L Sci Rep. 2023; 13(1):19809.

PMID: 37957164 PMC: 10643628. DOI: 10.1038/s41598-023-46465-6.


Diffusion Kurtosis Imaging of Neonatal Spinal Cord in Clinical Routine.

Tro R, Roascio M, Tortora D, Severino M, Rossi A, Cohen-Adad J Front Radiol. 2023; 2:794981.

PMID: 37492682 PMC: 10365122. DOI: 10.3389/fradi.2022.794981.


Porcine Model of the Growing Spinal Cord-Changes in Diffusion Tensor Imaging Parameters.

Owsinska-Schmidt K, Drobot P, Zimny A, Wrzosek M Animals (Basel). 2023; 13(4).

PMID: 36830353 PMC: 9951717. DOI: 10.3390/ani13040565.


Mapping Pediatric Spinal Cord Development with Age.

Kumar A, Vandekar S, Schilling K, Bhatia A, Landman B, Smith S Proc SPIE Int Soc Opt Eng. 2022; 12032.

PMID: 36506260 PMC: 9733418. DOI: 10.1117/12.2612210.


References
1.
Lobel U, Sedlacik J, Gullmar D, Kaiser W, Reichenbach J, Mentzel H . Diffusion tensor imaging: the normal evolution of ADC, RA, FA, and eigenvalues studied in multiple anatomical regions of the brain. Neuroradiology. 2009; 51(4):253-63. DOI: 10.1007/s00234-008-0488-1. View

2.
Taso M, Girard O, Duhamel G, Le Troter A, Feiweier T, Guye M . Tract-specific and age-related variations of the spinal cord microstructure: a multi-parametric MRI study using diffusion tensor imaging (DTI) and inhomogeneous magnetization transfer (ihMT). NMR Biomed. 2016; 29(6):817-32. DOI: 10.1002/nbm.3530. View

3.
Courchesne E, Chisum H, Townsend J, Cowles A, Covington J, Egaas B . Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology. 2000; 216(3):672-82. DOI: 10.1148/radiology.216.3.r00au37672. View

4.
Prados F, Ashburner J, Blaiotta C, Brosch T, Carballido-Gamio J, Cardoso M . Spinal cord grey matter segmentation challenge. Neuroimage. 2017; 152:312-329. PMC: 5440179. DOI: 10.1016/j.neuroimage.2017.03.010. View

5.
Hasan K, Walimuni I, Abid H, Hahn K . A review of diffusion tensor magnetic resonance imaging computational methods and software tools. Comput Biol Med. 2010; 41(12):1062-72. PMC: 3135778. DOI: 10.1016/j.compbiomed.2010.10.008. View