ORCID Profile
0000-0003-3476-3500
Current Organisations
King's College London
,
Friedrich-Alexander-Universität Erlangen-Nürnberg
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Publisher: Proceedings of the National Academy of Sciences
Date: 10-05-2021
Abstract: This work uses state-of-the-art acquisition and analysis methods developed specifically for fetal MRI to delineate the developing brain’s association, projection, and callosal white matter pathways. We describe unique, heterogenous maturational trajectories for different tracts, suggesting that regionally distinct biological mechanisms are at play in building the structural connectome in utero.
Publisher: Elsevier BV
Date: 09-2021
DOI: 10.1016/J.EJOGRB.2021.06.026
Abstract: The fetal thymus gland has been shown to involute in response to intrauterine infection, and therefore could be used as a non-invasive marker of fetal compartment infection. The objective of this study was to evaluate how accurately 2D ultrasound-derived measurements of the fetal thymus reflect the 3D volume of the gland derived from motion corrected MRI images. A retrospective study was performed using paired ultrasound and MRI datasets from the iFIND project (www.ifindproject.com). To obtain 3D volumetry of the thymus gland, T2-weighted single shot turbo spin echo (ssTSE) sequences of the fetal thorax were acquired. Thymus volumes were manually segmented from deformable slice-to-volume reconstructed images. To obtain 2D ultrasound measurements, previously stored fetal cine loops were used and measurements obtained at the 3-vessel-view (3VV) and 3-vessel-trachea view (3VT): anterior-posterior diameter (APD), intrathoracic diameter (ITD), transverse diameter (TD), perimeter and 3-vessel-edge (3VE). Inter-observer and intra-observer reliability (ICC) was calculated for both MRI and ultrasound measurements. Pearson correlation coefficients (PCC) were used to compare 2D-parameters with acceptable ICC to TV. 38 participants were identified. Adequate visualisation was possible on 37 MRI scans and 31 ultrasound scans. Of the 30 datasets where both MRI and ultrasound data were available, MRI had good interobserver reliability (ICC 0.964) and all ultrasound 3VV 2D-parameters and 3VT 3VE had acceptable ICC (>0.75). Four 2D parameters were reflective of the 3D thymus volume: 3VV TD r = 0.540 (P = 0.002) 3VV perimeter r = 0.446 (P = 0.013) 3VV APD r = 0.435 (P = 0.110) and 3VT TD r = 0.544 (P = 0.002). MRI appeared superior to ultrasound for visualization of the thymus gland and reproducibility of measurements. Three 2D US parameters, 3VV TD, perimeter and 3VT APD, correlated well with TV. Therefore, these represent a more accurate reflection of the true size of the gland than other 2D measurements, where MRI is not available.
Publisher: Wiley
Date: 06-07-2020
DOI: 10.1002/NBM.4348
Publisher: Elsevier BV
Date: 08-2022
DOI: 10.1016/J.NEUROIMAGE.2022.119319
Abstract: The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-in idual differences in structural brain connectivity, though in idual brain connectivity maps (connectomes) are by nature high in dimensionality and complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible and can give us clues as to how and why in idual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large s le of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of postmenstrual age (PMA) at scan in term-born infants (mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p < 0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p < 0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to predominantly involve frontal and temporal regions, thalami, and basal ganglia. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of in idual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome and suggest that a neural substrate of brain maturation with implications for future neurodevelopment is detectable at term equivalent age from the neonatal connectome.
Publisher: Elsevier BV
Date: 2021
Publisher: Cold Spring Harbor Laboratory
Date: 11-06-2019
DOI: 10.1101/661348
Abstract: Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, non-invasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi-shell data, with diffusion-sensitisation applied along many directions over multiple b -value shells. Such schemes are characterised by the number of shells acquired, and the specific b -value and number of directions s led for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi-shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project, which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of b = 0, 400, 1000, 2600 s/mm 2 with 20, 64, 88 & 128 DW directions per shell respectively. A data driven method is presented to design multi-shell diffusion MRI acquisition schemes ( b -values and no. directions). This method optimises the multi-shell scheme for maximum sensitivity to the information content in the signal. When applied in neonates, the data suggest that a b =0 + 3 shell strategy is appropriate
Publisher: BMJ Publishing Group Ltd and Royal College of Paediatrics and Child Health
Date: 19-06-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: Elsevier BV
Date: 03-2018
DOI: 10.1016/J.EJOGRB.2018.01.014
Abstract: Preterm birth, defined as birth occurring prior to 37 weeks gestation is a common obstetric complication affecting 8% of pregnancies and is associated with significant morbidity and mortality. Infection/inflammation has been implicated in both the aetiology of preterm birth itself and associated neonatal pulmonary and neurological morbidity. Treatment options are currently limited to prolongation of the pregnancy using cervical cerclage, pessaries or progesterone or administration of drugs including steroids to promote lung maturity and neuroprotective agents such as magnesium sulphate, the timing of which are highly critical. Although delivery is expedited in cases of overt infection, decisions regarding timing and mode of delivery in subclinical infection are not clear-cut. This review aims to explore the use of magnetic resonance imaging (MRI) in the antenatal assessment of pregnancies at high risk of preterm birth and its potential to guide management decisions in the future.
Publisher: Springer Science and Business Media LLC
Date: 07-12-2019
Publisher: Cold Spring Harbor Laboratory
Date: 29-09-2020
DOI: 10.1101/2020.09.28.317180
Abstract: The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-in idual differences in structural brain connectivity. In idual brain connectivity maps (connectomes) are by nature high in dimensionality and are complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible, and can give us clues as to how and why in idual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large s le of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (Mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p .001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p .001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to be predominantly thalamocortical. From our models of PMA at scan for infants born at term, we computed a brain maturation index ( predicted age minus actual age ) of in idual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome, and suggest that a neural substrate for later developmental outcome is detectable at term equivalent age.
Publisher: Wiley
Date: 24-09-2020
DOI: 10.1111/AOGS.13983
Abstract: Infection and inflammation have been implicated in the etiology and subsequent morbidity associated with preterm birth. At present, there are no tests to assess for fetal compartment infection. The thymus, a gland integral in the fetal immune system, has been shown to involute in animal models of antenatal infection, but its response in human fetuses has not been studied. This study aims: (a) to generate magnetic resonance imaging (MRI) -derived fetal thymus volumes standardized for fetal weight (b) to compare standardized thymus volumes from fetuses that delivered before 32 weeks of gestation with fetuses that subsequently deliver at term (c) to assess thymus size as a predictor of preterm birth and (d) to correlate the presence of chorioamnionitis and funisitis at delivery with thymic volumes in utero in fetuses that subsequently deliver preterm. Women at high-risk of preterm birth at 20-32 weeks of gestation were recruited. A control group was obtained from existing data sets acquired as part of three research studies. A fetal MRI was performed on a 1.5T or 3T MRI scanner: T2 weighted images were obtained of the entire uterine content and specifically the fetal thorax. A slice-to-volume registration method was used for reconstruction of three-dimensional images of the thorax. Thymus segmentations were performed manually. Body volumes were calculated by manual segmentation and thymus:body volume ratios were generated. Comparison of groups was performed using multiple regression analysis. Normal ranges were created for thymus volume and thymus:body volume ratios using the control data. Receiver operating curves (ROC) curves were generated for thymus:body volume ratio and gestation-adjusted thymus volume centiles as predictors of preterm birth. Placental histology was analyzed where available from pregnancies that delivered very preterm and the presence of chorioamnionitis/funisitis was noted. Normative ranges were created for thymus volume, and thymus volume was standardized for fetal size from fetuses that subsequently delivered at term, but were imaged at 20-32 weeks of gestation. Image data sets from 16 women that delivered 37 weeks were included. Mean gestation at MRI of the study group was 28 We have produced MRI-derived normal ranges for fetal thymus and thymus:body volume ratios between 20 and 32 weeks of gestation. Fetuses that deliver very preterm had reduced thymus volumes when standardized for fetal size. A reduced thymus volume was also a predictor of spontaneous preterm delivery. Thymus volume may be a suitable marker of the fetal inflammatory response, although further work is needed to assess this, increasing the s le size to correlate the extent of chorioamnionitis with thymus size.
Publisher: Wiley
Date: 30-05-2018
DOI: 10.1002/MRM.26765
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Jana Hutter.