Abstract #2420
MiniMORPH: A Morphometry Pipeline for Low-Field MRI in Infants
Chara Casella1,2, Niall Bourke3, Aksel Leknes4,5, Ayo Zahra4, Daniel Elijah Scheiene4, Russell Macleod1,2, James Cole6, Francesca Biondo6, Mariam Zabihi6, Victoria Nankabirwa7,8, Kirsten A Donald9, Muriel Marisa Katharina Bruchhage4,10,11, and Jonathan O'Muircheartaigh1,2,12
1Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom, 2Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 3Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom, 4Institute of Social Sciences, University of Stavanger, Stavanger, United Kingdom, 5Developmental Imaging, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Australia, 6Hawkes Institute, Computer Science, University College London, London, United Kingdom, 7Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Makerere University, Makerere University, Uganda, 8Vilirana Hospital, Kampala, Uganda, 9Department of Paediatrics and Child Health and the Neuroscience Institute, University of Cape Town, Cape Town, South Africa, 10Department of Radiology, Stavanger University Hospital, Stavanger, Norway, 11Stavanger Medical Imaging Laboratory (SMIL), Stavanger, Norway, 12MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
Synopsis
Keywords: Neuro, Low-Field MRI, Infants
Motivation: Infancy is a critical period for brain development. MRI provides a window into the developing brain, but its access is limited in low- and middle-income countries (LMICs). Portable ultra-low-field (ULF) MRI improves accessibility, yet low contrast, resolution, and signal-to-noise ratio challenge current morphometry tools.
Goal(s): To develop a pipeline for quantifying brain volumes in infant ULF MRI data, supporting neurodevelopmental research globally.
Approach: We built an atlas-guided tissue segmentation that accurately estimates tissue volumes in low-contrast infant ULF brain MRI.
Results: Our pipeline, MiniMORPH, successfully segmented ULF brain images, revealing significant effects of age, sex, and birthweight on tissue volumes.
Impact: We provide a scalable, open-source solution for segmentation of ULF MRI infant data, unlocking new possibilities for assessing neurodevelopment in diverse settings.
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