Assessing brain maturation on neonatal MRI—do we need a more quantitative approach?
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-023-10525-2.pdf
Reference10 articles.
1. Sotardi S, Gollub RL, Bates SV et al (2021) (2021) Voxelwise and regional brain apparent diffusion coefficient changes on MRI from birth to 6 years of age. Radiology 298(2):415–424. https://doi.org/10.1148/radiol.2020202279
2. Zun Z, Kapse K, Jacobs M et al (2021) Longitudinal trajectories of regional cerebral blood flow in very preterm infants during third trimester ex utero development assessed with MRI. Radiology 299(3):691–702. https://doi.org/10.1148/radiol.2021202423
3. Chen JV, Chaudhari G, Hess CP et al (2022) Deep learning to predict neonatal and infant brain age from myelination on brain MRI scans. Radiology 305(3):678–687. https://doi.org/10.1148/radiol.211860
4. Taoudi-Benchekroun Y, Christiaens D, Grigorescu I et al (2022) Predicting age and clinical risk from the neonatal connectome. Neuroimage 257:119319. https://doi.org/10.1016/j.neuroimage.2022.119319
5. Hong J, Feng Z, Wang SH et al (2020) Brain age prediction of children using routine brain MR images via deep learning. Front Neurol 11:584682. https://doi.org/10.3389/fneur.2020.584682
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