A dedicated neonatal brain imaging system

Hughes, Emer; Winchman, Tobias; Padormo, Francesco; Teixeira, Rui; Wurie, Julia; Sharma, Maryanne; Fox, Matthew P.; Hutter, Jana; Cordero‐Grande, Lucilio; Price, Anthony N.; Allsop, Joanna; Bueno-Conde, Jose; Tusor, Nora; Edwards, A. D.; Rutherford, Mary; Counsell, Serena J.; Hajnal, Joseph V.

doi:10.1002/mrm.26844

Cited by 77 publications

(125 citation statements)

References 9 publications

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“…All methods have been tuned on images collected using the dHCP protocol, and take advantage of improvements in image quality (gained from advances in acquisition, reconstruction and motion correction Hughes et al, 2016;Kuklisova-Murgasova et al, 2012)) to offer topologically correct (genus-0) surface representations images acquired between 28 and 45 weeks PMA. Manual QC of the images and surfaces suggest that, in the vast majority of cases, surfaces correspond closely with tissue boundaries, improving upon results obtained with state-of-the-art tissue segmentation tools.…”

Section: Discussionmentioning

confidence: 99%

“…The first stage of the project has been to optimise acquisition protocols and collect data for the neonatal cohort Hughes et al, 2016;Kuklisova-Murgasova et al, 2012). The methods in this paper therefore reflect neonatal structural processing protocols, and are designed to accompany the first data release of neonatal subjects.…”

Section: The Neonatal Structural Pipelinementioning

confidence: 99%

“…The data used in the paper were collected at St. Thomas Hospital, London, on a Philips 3T scanner using a 32 channel dedicated neonatal head coil (Hughes et al, 2016). To reduce the effects of motion, T2 images were obtained using a Turbo Spin Echo (TSE) sequence, acquired in two stacks of 2D slices (in sagittal and axial planes), using parameters: TR=12s, TE=156ms, SENSE factor 2.11 (axial) and 2.58 (sagittal) with overlapping slices (resolution (mm) 0.8 × 0.8 × 1.6).…”

Section: Acquisitionmentioning

confidence: 99%

“…This is a particular issue for the dHCP, where the goal is to image un-sedated neonatal, and free-moving fetal subjects. Therefore, correcting for this has required the development of advanced scanning protocols and motion correction schemes Hughes et al, 2016;KuklisovaMurgasova et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…The processing steps are as follows: 1) acquisition and reconstruction of T1 and T2 images Hughes et al, 2016;Kuklisova-Murgasova et al, 2012); 2) tissue segmentation and regional labelling (Makropoulos et al, 2012(Makropoulos et al, , 2014(Makropoulos et al, , 2016; 3) cortical white and pial surface extraction ; 4) inflation and projection to a sphere (for use with spherical alignment approaches) (Fischl et al, 1999a;Elad et al, 2005); and 5) definition of cortical feature descriptors, including descriptors of cortical geometry and myelination (Glasser et al, 2013). Manual quality control is performed by two independent expert raters to assess the quality of the acquired images, segmentations, and reconstructed cortical surfaces.…”

Section: Introductionmentioning

confidence: 99%

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The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

et al. 2018

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The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline * Corresponding author Email address: a.makropoulos11@imperial.ac.uk (Antonios Makropoulos) 1 These authors contributed equally Preprint submitted to NeuroImage January 7, 2018peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/125526 doi: bioRxiv preprint first posted online Apr. 10, 2017; consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

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Section: Discussionmentioning

confidence: 99%

Section: The Neonatal Structural Pipelinementioning

confidence: 99%

Section: Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

et al. 2018

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Construction and validation of a database of head models for functional imaging of the neonatal brain

Collins-Jones

Poppe

Billing

et al. 2020

Human Brain Mapping

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The neonatal brain undergoes dramatic structural and functional changes over the last trimester of gestation. The accuracy of source localisation of brain activity recorded from the scalp therefore relies on accurate age‐specific head models. Although an age‐appropriate population‐level atlas could be used, detail is lost in the construction of such atlases, in particular with regard to the smoothing of the cortical surface, and so such a model is not representative of anatomy at an individual level. In this work, we describe the construction of a database of individual structural priors of the neonatal head using 215 individual‐level datasets at ages 29–44 weeks postmenstrual age from the Developing Human Connectome Project. We have validated a method to segment the extra‐cerebral tissue against manual segmentation. We have also conducted a leave‐one‐out analysis to quantify the expected spatial error incurred with regard to localising functional activation when using a best‐matching individual from the database in place of a subject‐specific model; the median error was calculated to be 8.3 mm (median absolute deviation 3.8 mm). The database can be applied for any functional neuroimaging modality which requires structural data whereby the physical parameters associated with that modality vary with tissue type and is freely available at http://www.ucl.ac.uk/dot-hub.

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Effects of gestational age at birth on perinatal structural brain development in healthy term‐born babies

Gale‐Grant

Fenn-Moltu

França

et al. 2021

Human Brain Mapping

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Infants born in early term (37–38 weeks gestation) experience slower neurodevelopment than those born at full term (40–41 weeks gestation). While this could be due to higher perinatal morbidity, gestational age at birth may also have a direct effect on the brain. Here we characterise brain volume and white matter correlates of gestational age at birth in healthy term‐born neonates and their relationship to later neurodevelopmental outcome using T2 and diffusion weighted MRI acquired in the neonatal period from a cohort (n = 454) of healthy babies born at term age (>37 weeks gestation) and scanned between 1 and 41 days after birth. Images were analysed using tensor‐based morphometry and tract‐based spatial statistics. Neurodevelopment was assessed at age 18 months using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley‐III). Infants born earlier had higher relative ventricular volume and lower relative brain volume in the deep grey matter, cerebellum and brainstem. Earlier birth was also associated with lower fractional anisotropy, higher mean, axial, and radial diffusivity in major white matter tracts. Gestational age at birth was positively associated with all Bayley‐III subscales at age 18 months. Regression models predicting outcome from gestational age at birth were significantly improved after adding neuroimaging features associated with gestational age at birth. This work adds to the body of evidence of the impact of early term birth and highlights the importance of considering the effect of gestational age at birth in future neuroimaging studies including term‐born babies.

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A dedicated neonatal brain imaging system

Cited by 77 publications

References 9 publications

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

Construction and validation of a database of head models for functional imaging of the neonatal brain

Effects of gestational age at birth on perinatal structural brain development in healthy term‐born babies

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