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Measurement of Myelin in the Preterm Brain: Multi-compartment Diffusion Imaging and Multi-component T2 Relaxometry

  • Andrew Melbourne
  • Zach Eaton-Rosen
  • Alan Bainbridge
  • Giles S. Kendall
  • Manuel Jorge Cardoso
  • Nicola J. Robertson
  • Neil Marlow
  • Sebastien Ourselin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

Measurements of myelination and indicators of myelination status in the preterm brain could be predictive of later neurological outcome. Quantitative imaging of myelin could thus serve to develop predictive biomarkers; however, accurate estimation of myelin content is difficult. In this work we show that measurement of the myelin water fraction (MWF) is achievable using widely available pulse sequences and state-of-the-art algorithmic modelling of the MR imaging. We show results of myelin water fraction measurement at both 30 (4 infants) and 40 (2 infants) weeks equivalent gestational age (EGA) and show that the spatial pattern of myelin is different between these ages. Furthermore we apply a multi-component fitting routine to multi-shell diffusion weighted data to show differences in neurite density and local spatial arrangement in grey and white matter. Finally we combine these results to investigate the relationships between the diffusion and myelin measurements to show that MWF in the preterm brain may be measured alongside multi-component diffusion characteristics using clinically feasible MR sequences.

Keywords

Fractional Anisotropy High Fractional Anisotropy Myelin Water Fraction Myelin Content Component Magnitude 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andrew Melbourne
    • 1
  • Zach Eaton-Rosen
    • 1
  • Alan Bainbridge
    • 3
  • Giles S. Kendall
    • 2
  • Manuel Jorge Cardoso
    • 1
  • Nicola J. Robertson
    • 2
  • Neil Marlow
    • 2
  • Sebastien Ourselin
    • 1
  1. 1.Centre for Medical Image ComputingUniversity CollegeLondonUK
  2. 2.Academic Neonatology, EGA UCL Institute for Women’s HealthLondonUK
  3. 3.Medical PhysicsUniversity College HospitalLondonUK

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