Automatic quantification of normal cortical folding patterns from fetal brain MRI

We automatically quantify patterns of normal cortical folding in the developing fetus from in utero MR images (N=80) over a wide gestational age (GA) range (21.7 to 38.9weeks). This work on data from healthy subjects represents a first step towards characterising abnormal folding that may be related...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Fla.), 2014-05, Vol.91, p.21-32
Hauptverfasser: Wright, R., Kyriakopoulou, V., Ledig, C., Rutherford, M.A., Hajnal, J.V., Rueckert, D., Aljabar, P.
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container_start_page 21
container_title NeuroImage (Orlando, Fla.)
container_volume 91
creator Wright, R.
Kyriakopoulou, V.
Ledig, C.
Rutherford, M.A.
Hajnal, J.V.
Rueckert, D.
Aljabar, P.
description We automatically quantify patterns of normal cortical folding in the developing fetus from in utero MR images (N=80) over a wide gestational age (GA) range (21.7 to 38.9weeks). This work on data from healthy subjects represents a first step towards characterising abnormal folding that may be related to pathology, facilitating earlier diagnosis and intervention. The cortical boundary was delineated by automatically segmenting the brain MR image into a number of key structures. This utilised a spatio-temporal atlas as tissue priors in an expectation–maximization approach with second order Markov random field (MRF) regularization to improve the accuracy of the cortical boundary estimate. An implicit high resolution surface was then used to compute cortical folding measures. We validated the automated segmentations with manual delineations and the average surface discrepancy was of the order of 1mm. Eight curvature-based folding measures were computed for each fetal cortex and used to give summary shape descriptors. These were strongly correlated with GA (R2=0.99) confirming the close link between neurological development and cortical convolution. This allowed an age-dependent non-linear model to be accurately fitted to the folding measures. The model supports visual observations that, after a slow initial start, cortical folding increases rapidly between 25 and 30weeks and subsequently slows near birth. The model allows the accurate prediction of fetal age from an observed folding measure with a smaller error where growth is fastest. We also analysed regional patterns in folding by parcellating each fetal cortex using a nine-region anatomical atlas and found that Gompertz models fitted the change in lobar regions. Regional differences in growth rate were detected, with the parietal and posterior temporal lobes exhibiting the fastest growth, while the cingulate, frontal and medial temporal lobes developed more slowly. •We automatically quantify cortical folding in 80 normal foetuses (22–38weeks GA).•Eight curvature-based cortical folding measures were evaluated for each foetus.•Folding measures were better correlated with GA than volume and GA.•A Gompertz function accurately models the increase in folding measures with GA.•This model allows an accurate prediction of GA from folding measures.
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This work on data from healthy subjects represents a first step towards characterising abnormal folding that may be related to pathology, facilitating earlier diagnosis and intervention. The cortical boundary was delineated by automatically segmenting the brain MR image into a number of key structures. This utilised a spatio-temporal atlas as tissue priors in an expectation–maximization approach with second order Markov random field (MRF) regularization to improve the accuracy of the cortical boundary estimate. An implicit high resolution surface was then used to compute cortical folding measures. We validated the automated segmentations with manual delineations and the average surface discrepancy was of the order of 1mm. Eight curvature-based folding measures were computed for each fetal cortex and used to give summary shape descriptors. These were strongly correlated with GA (R2=0.99) confirming the close link between neurological development and cortical convolution. 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subjects Adult
Algorithms
Atlases as Topic
Automation
Biological and medical sciences
Brain development
Cerebral Cortex - anatomy & histology
Cerebral Cortex - embryology
Cerebral Cortex - growth & development
Cortical folding
Data Interpretation, Statistical
Female
Fetal MRI
Fetus - anatomy & histology
Fetuses
Fundamental and applied biological sciences. Psychology
Gestational Age
Gompertz function
Humans
Image Processing, Computer-Assisted
Linear Models
Magnetic Resonance Imaging - methods
NMR
Nuclear magnetic resonance
Pregnancy
Reproducibility of Results
Studies
Vertebrates: nervous system and sense organs
title Automatic quantification of normal cortical folding patterns from fetal brain MRI
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