title: Quantification of diffusion tensor imaging in normal white matter maturation of early childhood using an automated processing pipeline.
abstract: OBJECTIVE
  The degree and status of white matter myelination can be sensitively monitored using diffusion tensor imaging (DTI). This study looks at the measurement of fractional anistropy (FA) and mean diffusivity (MD) using an automated ROI with an existing DTI atlas.
  
  
  METHODS
  Anatomical MRI and structural DTI were performed cross-sectionally on 26 normal children (newborn to 48 months old), using 1.5-T MRI. The automated processing pipeline was implemented to convert diffusion-weighted images into the NIfTI format. DTI-TK software was used to register the processed images to the ICBM DTI-81 atlas, while AFNI software was used for automated atlas-based volumes of interest (VOIs) and statistical value extraction.
  
  
  RESULTS
  DTI exhibited consistent grey-white matter contrast. Triphasic temporal variation of the FA and MD values was noted, with FA increasing and MD decreasing rapidly early in the first 12 months. The second phase lasted 12-24 months during which the rate of FA and MD changes was reduced. After 24 months, the FA and MD values plateaued.
  
  
  CONCLUSIONS
  DTI is a superior technique to conventional MR imaging in depicting WM maturation. The use of the automated processing pipeline provides a reliable environment for quantitative analysis of high-throughput DTI data.
  
  
  CONCLUSIONS
  Diffusion tensor imaging outperforms conventional MRI in depicting white matter maturation. • DTI will become an important clinical tool for diagnosing paediatric neurological diseases. • DTI appears especially helpful for developmental abnormalities, tumours and white matter disease. • An automated processing pipeline assists quantitative analysis of high throughput DTI data.
authors.affiliation: Department of Biomedical Imaging, University Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia.
authors.forename: K B
authors.initials: KB
authors.lastname: Loh
authors.forename: N
authors.initials: N
authors.lastname: Ramli
authors.forename: L K
authors.initials: LK
authors.lastname: Tan
authors.forename: M
authors.initials: M
authors.lastname: Roziah
authors.forename: K
authors.initials: K
authors.lastname: Rahmat
authors.forename: H
authors.initials: H
authors.lastname: Ariffin
mesh_terms: D000375:Aging / Q000473:pathology* / Q000502:physiology*; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000254:growth & development*; D002648:Child; D002675:Child, Preschool; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D009413:Nerve Fibers, Myelinated / Q000502:physiology* / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity