Abstract #3109

Robust generation of tract-wise myelination measurements from infant T1- and T2-weighted MRI using synth based deep learning methods

Henry F. J. Tregidgo¹, Layla Bradford², Simone Williams², Niall Bourke³, Michal R. Zieff², Zayaan Goolam Nabi², Thandeka Mazubane ², Peter Wijeratne ⁴, Lilla Zöllei⁵, Juan Eugenio Iglesias⁶, Steven Williams³, Derek Jones⁷, Kirsty Donald², and Daniel C. Alexander¹

¹Centre for Medical Image Computing, University College London, London, United Kingdom, ²Department of Paediatrics and Child Health, University of Cape Town, Cape Town, South Africa, ³Department of Neuroimaging, King's College London, London, United Kingdom, ⁴Department of Informatics, University of Sussex, Brighton, United Kingdom, ⁵Radiology, MGH & Harvard Medical School, Charlestown, MA, United States, ⁶Martinos Center for Biomedical Imaging, MGH & Harvard Medical School, Boston, MA, United States, ⁷CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom

Synopsis

Keywords: Data Processing, Data Processing, Infant myelination

Motivation: While T1/T2-weighted ratio maps are important for the study of myelination, existing reconstruction tools can fail in infants and present difficulty to automated segmentation.

Goal(s): To provide a pipeline for obtaining accurate regional myelination measures of whole brain regions and white matter tracts.

Approach: We adapted existing T1/T2-weighted ratio pipelines to incorporate deep learning methods for segmentation and registration as well as a high-quality tract atlas.

Results: Our pipeline showed reduced errors and improved differentiation between 3- and 6-month-old infants from a South African longitudinal birth cohort study.

Impact: The improved T1/T2-weighted ratio contrast and detailed segmentations provided by our pipeline will enable study of specific myelination patterns during neurodevelopment, especially in populations exposed to risk factors for altered white matter maturation.

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