Evidence map›Paper›PMID 42334017›Full record

ArticleHuman brain mapping2026

miniMORPH: A Morphometry Pipeline for Low-Field MRI in Infants.

Chiara Casella, Aksel Leknes, Niall J Bourke, Ayo Zahra, Daniel Cromb, Dora Barnes, Alejandra Martin Segura, Flora Silvester, Vanessa Kyriakopoulou, Daniel Elijah Scheiene and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Human brain mapping, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Chiara CasellaResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Aksel LeknesInstitute of Psychology, University of Stavanger, Stavanger, Norway.
Niall J BourkeCentre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Ayo ZahraInstitute of Psychology, University of Stavanger, Stavanger, Norway.
Daniel CrombResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID https://orcid.org/0000-0002-9814-8841
Dora BarnesDepartment of General Paediatrics, Evelina London Children's Hospital, London, UK.
Alejandra Martin SeguraDepartment of General Paediatrics, St George's Hospital, London, UK.
Flora SilvesterDepartment of General Paediatrics, Evelina London Children's Hospital, London, UK.
Vanessa KyriakopoulouResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Daniel Elijah ScheieneInstitute of Psychology, University of Stavanger, Stavanger, Norway.
Simone R WilliamsDepartment of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa.
Layla E BradfordDepartment of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa.
Joanitta MurungiDepartment of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda.
Steven C R WilliamsCentre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Sean C L DeoniMNCH D&T, Bill & Melinda Gates Foundation, Seattle, Washington, USA.
Victoria NankabirwaDepartment of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda.
Kirsten A DonaldDepartment of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa.
Muriel M K BruchhageInstitute of Psychology, University of Stavanger, Stavanger, Norway.
Jonathan O'MuircheartaighResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Funding

Bill and Melinda Gates Foundation INV-047885Bill and Melinda Gates Foundation INV-047888Gates Foundation INV-005774Gates Foundation INV-018164Wellcome Trust 222076/Z/20/ZWellcome Trust 314678/Z/24/Z
6 · The paper itself

Abstract

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.

Indexed as

BrainImage Processing, Computer-AssistedMagnetic Resonance ImagingNeuroimagingChild, PreschoolFemaleHumansInfantMaleUgandaautomated segmentationbrain volumetrycross‐modality validationinfant brainlow‐ and middle‐income settingstemplate‐based morphometryultra‐low‐field MRI

Identifiers

PMID42334017
PMCPMC13288155

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.