Academic Journal
miniMORPH: A Morphometry Pipeline for Low-Field MRI in Infants.
| Τίτλος: | miniMORPH: A Morphometry Pipeline for Low-Field MRI in Infants. |
|---|---|
| Συγγραφείς: | Casella C; Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK., Leknes A; Institute of Psychology, University of Stavanger, Stavanger, Norway., Bourke NJ; Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK., Zahra A; Institute of Psychology, University of Stavanger, Stavanger, Norway., Cromb D; Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.; Department of General Paediatrics, Evelina London Children's Hospital, London, UK., Barnes D; Department of General Paediatrics, Evelina London Children's Hospital, London, UK., Segura AM; Department of General Paediatrics, St George's Hospital, London, UK., Silvester F; Department of General Paediatrics, Evelina London Children's Hospital, London, UK., Kyriakopoulou V; Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK., Scheiene DE; Institute of Psychology, University of Stavanger, Stavanger, Norway., Williams SR; Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa.; Neuroscience Institute, University of Cape Town, Cape Town, South Africa., Bradford LE; Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa.; Neuroscience Institute, University of Cape Town, Cape Town, South Africa., Murungi J; Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda., Williams SCR; Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK., Deoni SCL; MNCH D&T, Bill & Melinda Gates Foundation, Seattle, Washington, USA., Nankabirwa V; Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda.; Vilirana Hospital, Kampala, Uganda., Donald KA; Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa.; Neuroscience Institute, University of Cape Town, Cape Town, South Africa., Bruchhage MMK; Institute of Psychology, University of Stavanger, Stavanger, Norway.; Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.; Stavanger Medical Imaging Laboratory, Department of Radiology, Stavanger University Hospital, Stavanger, Norway., O'Muircheartaigh J; Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.; MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK. |
| Πηγή: | Human brain mapping [Hum Brain Mapp] 2026 Jun 15; Vol. 47 (9), pp. e70547. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley Country of Publication: United States NLM ID: 9419065 Publication Model: Print Cited Medium: Internet ISSN: 1097-0193 (Electronic) Linking ISSN: 10659471 NLM ISO Abbreviation: Hum Brain Mapp Subsets: MEDLINE |
| Imprint Name(s): | Publication: New York : Wiley Original Publication: New York : Wiley-Liss, c1993- |
| Ιατρικοί όροι (MeSH): | Magnetic Resonance Imaging*/methods , Magnetic Resonance Imaging*/standards , Brain*/diagnostic imaging , Brain*/anatomy & histology , Brain*/growth & development , Image Processing, Computer-Assisted*/methods , Image Processing, Computer-Assisted*/standards , Neuroimaging*/methods , Neuroimaging*/standards, Humans ; Infant ; Male ; Female ; Child, Preschool ; Uganda |
| Περίληψη: | Ultra-low-field (ULF) MRI facilitates neuroimaging access, yet its application in early infancy is constrained by low resolution and contrast, and the limited suitability of existing segmentation tools. In this work we introduce and validate miniMORPH, an open-source pipeline for automated brain volumetry from 0.064T T2-weighted MRI acquired across infancy and toddlerhood. ULF scans were acquired from infants aged 2 to 27 months across two cohorts in South Africa and Uganda. Age-specific templates and priors were used to segment major brain tissues and substructures. Validation used two high-field (HF) references: (i) expert manual HF segmentations for key ROIs across ages, and (ii) automated HF segmentations from SuperSynth on paired HF-ULF scans. We quantified (a) between-subject ordering across modalities using Pearson's correlation (r) and (b) systematic scaling differences using percentage error (PE) and time-corrected percentage error (CPE), stratifying performance by cohort and age. Face validity was also tested via mixed-effects models of age, sex, and birthweight. miniMORPH generated anatomically plausible segmentations of major brain regions across infancy. In paired HF-ULF comparisons, between-subject ordering was generally preserved across many ROIs, with stronger correspondence in the South African cohort than in the Ugandan cohort at 12 months. Systematic scaling offsets were most evident in CSF-rich or boundary-sensitive compartments, with consistently negative CPE for ventricles and cerebellum. Performance varied with age, showing the greatest variability at 3 months. miniMORPH successfully captured regional age-related growth trajectories. Sex-dependent volumetric differences were widespread but attenuated after intracranial volume correction. Low birthweight infants exhibited reduced regional volumes and altered growth trajectories. Taken together, these findings indicate that miniMORPH enables volumetric analysis of ULF infant MRI and preserves between-subject variation suitable for developmental and group analyses. ROI- and cohort-specific offsets, particularly in CSF-rich regions, may require calibration when absolute volumes are needed. The pipeline is openly available at https://github.com/UNITY-Physics/fw-minimorph. (© 2026 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.) |
| References: | Abate, F., A. Adu‐Amankwah, K. Ae‐Ngibise, et al. 2024. “UNITY: A Low‐Field Magnetic Resonance Neuroimaging Initiative to Characterize Neurodevelopment in Low and Middle‐Income Settings.” Developmental Cognitive Neuroscience 69: 101397. https://doi.org/10.1016/j.dcn.2024.101397. Avants, B. B., N. J. Tustison, J. Wu, P. A. Cook, and J. C. Gee. 2011. “An Open Source Multivariate Framework for n‐Tissue Segmentation With Evaluation on Public Data.” Neuroinformatics 9, no. 4: 381–400. https://doi.org/10.1007/S12021‐011‐9109‐Y. Billot, B., D. N. Greve, O. Puonti, et al. 2023. “SynthSeg: Segmentation of Brain MRI Scans of Any Contrast and Resolution Without Retraining.” Medical Image Analysis 86: 102789. https://doi.org/10.1016/j.media.2023.102789. Bozdogan, H. 1987. “Model Selection and Akaike's Information Criterion (AIC): The General Theory and Its Analytical Extensions.” Psychometrika 52, no. 3: 345–370. https://doi.org/10.1007/BF02294361. Bruchhage, M. M. K., G.‐C. Ngo, N. Schneider, V. D'Sa, and S. C. L. Deoni. 2020. “Functional Connectivity Correlates of Infant and Early Childhood Cognitive Development.” Brain Structure & Function 225, no. 2: 669–681. https://doi.org/10.1007/s00429‐020‐02027‐4. Chen, L., Z. Wu, D. Hu, et al. 2022. “A 4D Infant Brain Volumetric Atlas Based on the UNC/UMN Baby Connectome Project (BCP) Cohort.” NeuroImage 253: 119097. https://doi.org/10.1016/j.neuroimage.2022.119097. Chin, J. H., M. K. Wyburd, V. Ayzenberg, et al. 2026. “Deep Learning in Fetal, Infant, and Toddler Neuroimaging Research.” Developmental Cognitive Neuroscience 78: 101680. https://doi.org/10.1016/j.dcn.2026.101680. Cooley, C. Z., P. C. McDaniel, J. P. Stockmann, et al. 2021. “A Portable Scanner for Magnetic Resonance Imaging of the Brain.” Nature Biomedical Engineering 5, no. 3: 229–239. https://doi.org/10.1038/s41551‐020‐00641‐5. de Kieviet, J. F., L. Zoetebier, R. M. van Elburg, R. J. Vermeulen, and J. Oosterlaan. 2012. “Brain Development of Very Preterm and Very Low‐Birthweight Children in Childhood and Adolescence: A Meta‐Analysis.” Developmental Medicine and Child Neurology 54, no. 4: 313–323. https://doi.org/10.1111/j.1469‐8749.2011.04216.x. Dean, D. C., J. O'Muircheartaigh, H. Dirks, et al. 2014. “Modeling Healthy Male White Matter and Myelin Development: 3 Through 60 Months of Age.” NeuroImage 84: 742–752. https://doi.org/10.1016/j.neuroimage.2013.09.058. Deoni, S. C. L., J. O'Muircheartaigh, E. Ljungberg, M. Huentelman, and S. C. R. Williams. 2022. “Simultaneous High‐Resolution T2‐Weighted Imaging and Quantitative T 2 Mapping at Low Magnetic Field Strengths Using a Multiple TE and Multi‐Orientation Acquisition Approach.” Magnetic Resonance in Medicine 88, no. 3: 1273–1281. https://doi.org/10.1002/mrm.29273. Desikan, R. S., F. Ségonne, B. Fischl, et al. 2006. “An Automated Labeling System for Subdividing the Human Cerebral Cortex on MRI Scans Into Gyral Based Regions of Interest.” NeuroImage 31, no. 3: 968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021. Donnay, C., S. V. Okar, C. Tsagkas, et al. 2024. “Super Resolution Using Sparse Sampling at Portable Ultra‐Low Field MR.” Frontiers in Neurology 15: 1330203. https://doi.org/10.3389/fneur.2024.1330203. Fischl, B. 2012. “FreeSurfer.” NeuroImage, 20 Years of fMRI 62, no. 2: 774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021. Gilmore, J. H., W. Lin, M. W. Prastawa, et al. 2007. “Regional Gray Matter Growth, Sexual Dimorphism, and Cerebral Asymmetry in the Neonatal Brain.” Journal of Neuroscience: The Official Journal of the Society for Neuroscience 27, no. 6: 1255–1260. https://doi.org/10.1523/jneurosci.3339‐06.2007. Ginsburg, A. S., A. Braima de Sa, V. Nankabirwa, et al. 2022. “Randomized Controlled Trial of Early, Small‐Volume Formula Supplementation Among Newborns: A Study Protocol.” PLoS One 17, no. 2: e0263129. https://doi.org/10.1371/journal.pone.0263129. Hoffmann, M., A. Hoopes, D. N. Greve, B. Fischl, and A. V. Dalca. 2024. “Anatomy‐Aware and Acquisition‐Agnostic Joint Registration With SynthMorph.” Imaging Neuroscience 2: 1–33. https://doi.org/10.1162/imag_a_00197. Holland, D., L. Chang, T. M. Ernst, et al. 2014. “Structural Growth Trajectories and Rates of Change in the First 3 Months of Infant Brain Development.” JAMA Neurology 71, no. 10: 1266–1274. https://doi.org/10.1001/jamaneurol.2014.1638. Hoopes, A., J. S. Mora, A. V. Dalca, B. Fischl, and M. Hoffmann. 2022. “SynthStrip: Skull‐Stripping for Any Brain Image.” NeuroImage 260: 119474. https://doi.org/10.1016/j.neuroimage.2022.119474. Jenkinson, M., C. F. Beckmann, T. E. J. Behrens, M. W. Woolrich, and S. M. Smith. 2012. “Fsl.” NeuroImage 62, no. 2: 782–790. https://doi.org/10.1016/J.NEUROIMAGE.2011.09.015. Knickmeyer, R. C., J. Wang, H. Zhu, et al. 2014. “Impact of Sex and Gonadal Steroids on Neonatal Brain Structure.” Cerebral Cortex 24, no. 10: 2721–2731. https://doi.org/10.1093/cercor/bht125. Lau, V., L. Xiao, Y. Zhao, et al. 2023. “Pushing the Limits of Low‐Cost Ultralow‐Field MRI by Dual‐Acquisition Deep Learning 3D Superresolution.” Magnetic Resonance in Medicine 90: 400–416. Li, G., L. Wang, P.‐T. Yap, et al. 2019. “Computational Neuroanatomy of Baby Brains: A Review.” NeuroImage 185: 906–925. https://doi.org/10.1016/j.neuroimage.2018.03.042. Makropoulos, A., I. S. Gousias, C. Ledig, et al. 2014. “Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain.” IEEE Transactions on Medical Imaging 33, no. 9: 1818–1831. https://doi.org/10.1109/TMI.2014.2322280. Makropoulos, A., E. C. Robinson, A. Schuh, et al. 2018. “The Developing Human Connectome Project: A Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction.” NeuroImage 173: 88–112. Mazurek, M. H., B. A. Cahn, M. M. Yuen, et al. 2021. “Portable, Bedside, Low‐Field Magnetic Resonance Imaging for Evaluation of Intracerebral Hemorrhage.” Nature Communications 12, no. 1: 5119. https://doi.org/10.1038/s41467‐021‐25441‐6. Niaz, M. R., A. R. Ridwan, Y. Wu, D. A. Bennett, and K. Arfanakis. 2022. “Development and Evaluation of a High Resolution 0.5mm Isotropic T1‐Weighted Template of the Older Adult Brain.” NeuroImage 248: 118869. https://doi.org/10.1016/j.neuroimage.2021.118869. Parsa, J., T. O'Reilly, and A. Webb. 2023. “A Single‐Coil‐Based Method for Electromagnetic Interference Reduction in Point‐Of‐Care Low Field MRI Systems.” Journal of Magnetic Resonance 346: 107355. https://doi.org/10.1016/j.jmr.2022.107355. Ringshaw, J. E., N. J. Bourke, M. R. Zieff, et al. 2026. “Feasibility and Validity of Ultra‐Low‐Field MRI for Measurement of Regional Infant Brain Volumes in Structures Associated With Antenatal Maternal Anemia.” Human Brain Mapping 47, no. 1: e70443. https://doi.org/10.1002/hbm.70443. Rutherford, S., C. Fraza, R. Dinga, et al. 2022. “Charting Brain Growth and Aging at High Spatial Precision.” eLife 11: e72904. Sabir, H., F. Kipfmueller, S. Bagci, et al. 2023. “Feasibility of Bedside Portable MRI in Neonates and Children During ECLS.” Critical Care 27, no. 1: 134. Sarracanie, M., C. D. LaPierre, N. Salameh, D. E. J. Waddington, T. Witzel, and M. S. Rosen. 2015. “Low‐Cost High‐Performance MRI.” Scientific Reports 5: 15177. https://doi.org/10.1038/srep15177. Shen, S., N. Koonjoo, F. K. Longarino, et al. 2026. “Breast Imaging With Ultra‐Low Field MRI.” Scientific Reports 16, no. 1: 4518. https://doi.org/10.1038/s41598‐026‐37130‐9. Sheth, K. N., M. H. Mazurek, M. M. Yuen, et al. 2020. “Assessment of Brain Injury Using Portable, Low‐Field Magnetic Resonance Imaging at the Bedside of Critically Ill Patients.” JAMA Neurology 78, no. 1: 41–47. https://doi.org/10.1001/jamaneurol.2020.3263. Song, L., Y. Peng, M. Ouyang, et al. 2024. “Diffusion‐Tensor‐Imaging 1‐Year‐Old and 2‐Year‐Old Infant Brain Atlases With Comprehensive Gray and White Matter Labels.” Human Brain Mapping 45, no. 7: e26695. https://doi.org/10.1002/hbm.26695. Sun, Y., K. Gao, Z. Wu, et al. 2021. “Multi‐Site Infant Brain Segmentation Algorithms: The iSeg‐2019 Challenge.” IEEE Transactions on Medical Imaging 40, no. 5: 1363–1376. https://doi.org/10.1109/TMI.2021.3055428. Sze, C., Z. Singh, A. Punyala, et al. 2023. “Feasibility and Preliminary Clinical Tolerability of Low‐Field MRI‐Guided Prostate Biopsy.” Prostate 83, no. 7: 656–662. https://doi.org/10.1002/pros.24499. Tu, D., M. S. Goyal, J. D. Dworkin, et al. 2023. “Automated Analysis of Low‐Field Brain MRI in Cerebral Malaria.” Biometrics 79, no. 3: 2417–2429. Wald, L. L., P. C. McDaniel, T. Witzel, J. P. Stockmann, and C. Z. Cooley. 2020. “Low‐Cost and Portable MRI.” Journal of Magnetic Resonance Imaging 52, no. 3: 686–696. https://doi.org/10.1002/jmri.26942. Walhovd, K. B., A. M. Fjell, T. T. Brown, et al. 2012. “Long‐Term Influence of Normal Variation in Neonatal Characteristics on Human Brain Development.” Proceedings of the National Academy of Sciences 109, no. 49: 20089–20094. https://doi.org/10.1073/pnas.1208180109. Wang, L., F. Shi, Y. Gao, et al. 2014. “Integration of Sparse Multi‐Modality Representation and Anatomical Constraint for Isointense Infant Brain MR Image Segmentation.” NeuroImage 89: 152–164. https://doi.org/10.1016/j.neuroimage.2013.11.040. Wang, L., Z. Wu, L. Chen, Y. Sun, W. Lin, and G. Li. 2023. “iBEAT V2.0: A Multisite‐Applicable, Deep Learning‐Based Pipeline for Infant Cerebral Cortical Surface Reconstruction.” Nature Protocols 18, no. 5: 1488–1509. https://doi.org/10.1038/s41596‐023‐00806‐x. Zöllei, L., J. E. Iglesias, Y. Ou, P. E. Grant, and B. Fischl. 2020. “Infant FreeSurfer: An Automated Segmentation and Surface Extraction Pipeline for T1‐Weighted Neuroimaging Data of Infants 0–2 Years.” NeuroImage 218: 116946. https://doi.org/10.1016/j.neuroimage.2020.116946. |
| Grant Information: | INV-018164 United States GATES Gates Foundation; INV-005774 United States GATES Gates Foundation; INV-047888 Bill and Melinda Gates Foundation; INV-047885 Bill and Melinda Gates Foundation; 314678/Z/24/Z United Kingdom WT_ Wellcome Trust; 222076/Z/20/Z United Kingdom WT_ Wellcome Trust |
| Contributed Indexing: | Keywords: automated segmentation; brain volumetry; cross‐modality validation; infant brain; low‐ and middle‐income settings; template‐based morphometry; ultra‐low‐field MRI |
| Entry Date(s): | Date Created: 20260623 Date Completed: 20260623 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13288155 |
| DOI: | 10.1002/hbm.70547 |
| PMID: | 42334017 |
| Βάση Δεδομένων: | MEDLINE |
καταχωρήστε σχόλιο πρώτοι!