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Title: Exploring the developing preterm brain with diffusion tensor magnetic resonance imaging
Author: Anjari, Mustafa
ISNI:       0000 0004 2672 7582
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2009
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The majority of preterm-born infants now survive beyond the perinatal period. However, this has been accompanied by increases in neurodevelopmental impairment not explained by the presence of focal lesions on conventional magnetic resonance imaging (MRI). Diffusion tensor imaging (DTI) is a quantitative MRI technique with the potential to assess micro-structural brain abnormalities. This project examines the developing preterm brain using computational analysis of DTI data. Tract-based spatial statistics (TBSS) is a method for registering diffusion-derived fractional anisotropy (FA) data to allow objective investigation of cerebral white matter tracts. FA maps from term-born control infants and preterm infants at term age with no evidence of focal white matter abnormality on conventional MRI were used to assess the feasibility of using TBSS with neonatal DTI data, and then to investigate the effects of preterm birth on white matter microstructure at term. FA was found to be reduced in the preterm group in numerous white matter regions, with the most immature-bom infants displaying more extensive regions of FA reduction. The effects of various clinical variables on FA data processed using TBSS were assessed in another cohort of preterm infants imaged at term. This demonstrated for the first time that acute and chronic lung disease are independently associated with localised cerebral white matter abnormalities in the genu of the corpus callosum and the left inferior longitudinal fasciculus respectively. Whilst using TBSS, only major white matter tracts can be studied. To enable analysis of whole brain DTI data, optimisation of a nonlinear registration algorithm based on basis- splines for retrospective unwarping of echo planar DTI data is presented.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available