Biology subjects

Steinweg, J. K.

Publications and source records attributed to Steinweg, J. K..

5 recordsLinked to original sources

bioRxiv2022

3D MRI atlases of congenital aortic arch anomalies and normal fetal heart: application to automated multi-label segmentation

Background3D image-domain reconstruction of black blood contrast T2w SSTSE fetal MRI datasets using slice-to-volume registration methods showed to provide high-resolution 3D images of the heart with superior visualisation of fetal aortic arch anomalies [1]. However, there is a lack of formalisation of the MRI appearance of fetal cardiovascular anatomy and standardisation of vessel segmentation protocols. MethodsIn this work, we present the first set of 3D fetal MRI atlases defining normal and abnormal fetal aortic arch anatomy created from 3D reconstructed images from 87 subjects scanned between 29-34 weeks of gestation with postnatally confirmed outcomes. We also implement and evaluate atlas-guided registration and deep learning (UNETR) methods for automated 3D multi-label fetal heart vessel segmentation. ResultsWe created four atlases representing the average anatomy of the normal fetal heart, coarctation of the aorta, right aortic arch and suspected double aortic arch. Inspection of atlases confirmed the expected pronounced differences in the anatomy of the aortic arch. The results of the multi-label heart vessel UNETR segmentation showed 100% per-vessel detection rate for both normal and abnormal aortic arch anatomy. ConclusionsThis work introduces the first set of 3D black blood T2w MRI atlases of the normal and abnormal fetal cardiovascular anatomy along with detailed segmentation of the major cardiovascular structures. We also demonstrated the feasibility of using deep learning for multi-label vessel segmentation.

bioinformatics↗

bioRxiv2022

Automated atlas-based multi-label fetal cardiac vesselsegmentation in Congenital Heart Disease

Congenital heart disease (CHD) is the most commonly diagnosed birth defect. T2w black blood MRI provides optimal vessel visualisation, aiding prenatal CHD diagnosis. Common clinical practice involves manual segmentation of fetal heart and vessels for visualisation and reporting purposes. We propose an automated multi-label fetal cardiac vessels deep learning segmentation approach for T2w black blood MRI. Our network is trained using single-label manual segmentations obtained through current clinical practice, combined with a multi-label anatomical atlas with desired multi-label segmentation protocol. Our framework combines deep learning label propagation with 3D residual U-Net segmentation to produce high-quality multi-label output well adapted to the individual subject anatomy. We train and evaluate the network using forty fetal subjects with suspected coarctation of the aorta, achieving a dice score of 0.79 {+/-} 0.02 for the fetal cardiac vessels region. The proposed network outperforms the label propagation and achieves a statistically equivalent performance to a 3D residual U-Net trained exclusively on manual single-label data (p-value>0.05). This multi-label framework therefore represents an advancement over the single-label approach, providing label-specific anatomical information, particularly useful for assessing specific anomaly areas in CHD.

bioengineering↗

bioRxiv2021

Automated 3D reconstruction of the fetal thorax in the standard atlas space from motion-corrupted MRI stacks for 21-36 weeks GA range

Slice-to-volume registration (SVR) methods allow reconstruction of high-resolution 3D images from multiple motion-corrupted stacks. SVR-based pipelines have been increasingly used for motion correction for T2-weighted fetal MRI since they allow more informed and detailed diagnosis of brain and body anomalies including congenital heart defects (Lloyd et al., 2019). Recently, fully automated rigid SVR reconstruction of the fetal brain in the atlas space was achieved in (Salehi et al., 2019) that used convolutional neural networks (CNNs) for segmentation and pose estimation. However, these CNN-based methods have not yet been applied to the fetal trunk region. Meanwhile, the existing rigid and deformable SVR (DSVR) solutions (Uus et al., 2020) for the fetal trunk region are limited by the requirement of manual input as well the narrow capture range of the classical gradient descent based registration methods that cannot resolve severe fetal motion frequently occurring at the early gestational age (GA). Furthermore, in our experience, the conventional 2D slice-wise CNN-based brain masking solutions are reportedly prone to errors that require manual corrections when applied on a wide range of acquisition protocols or abnormal cases in clinical setting. In this work, we propose a fully automated pipeline for reconstruction of the fetal thorax region for 21-36 weeks GA range T2-weighted MRI datasets. It includes 3D CNN-based intra-uterine localisation of the fetal trunk and landmark-guided pose estimation steps that allow automated DSVR reconstruction in the standard radiological space irrespective of the fetal trunk position or the regional stack coverage. The additional step for generation of the common template space and rejection of outliers provides the means for automated exclusion of stacks affected by low image quality or extreme motion. The pipeline was evaluated on a series of experiments including fetal MRI datasets and simulated rotation motion. Furthermore, we performed a qualitative assessment of the image reconstruction quality in terms of the definition of vascular structures on 100 early (median 23.14 weeks) and late (median 31.79 weeks) GA group MRI datasets covering 21 to 36 weeks GA range.

bioinformatics↗

bioRxiv2020

Individualized characterization of volumetric development in the preterm brain

The diverse cerebral consequences of preterm birth create significant challenges for understanding pathogenesis or predicting later outcome. Instead of focusing on describing effects common to the group, comparing individual infants against robust normative data offers a powerful alternative to study brain maturation. Here we used Gaussian process regression to create normative curves characterising brain volumetric development in 274 term-born infants, modelling for age at scan and sex. We then compared 89 preterm infants scanned at termequivalent age to these normative charts, relating individual deviations from typical volumetric development to perinatal risk factors and later neurocognitive scores. To test generalisability, we used a second independent dataset comprising of 253 preterm infants scanned using different acquisition parameters and scanner. We describe rapid, non-uniform brain growth during the neonatal period. In both preterm cohorts, cerebral atypicalities were widespread, often multiple, and varied highly between individuals. Deviations from normative development were associated with respiratory support, nutrition, birth weight, and later neurocognition, demonstrating their clinical relevance. Group-level understanding of the preterm brain disguise a large degree of individual differences. We provide a method and normative dataset that offer a more precise characterisation of the cerebral consequences of preterm birth by profiling the individual neonatal brain.

neuroscience↗

bioRxiv2018

Abnormal microstructural development of the cerebral cortex in neonates with congenital heart disease is associated with impaired cerebral oxygen delivery

BackgroundAbnormal macrostructural development of the cerebral cortex has been associated with hypoxia in infants with congenital heart disease (CHD). Animal studies have suggested that hypoxia results in cortical dysmaturation at the cellular level. New magnetic resonance imaging (MRI) techniques offer the potential to investigate the relationship between cerebral oxygen delivery and microstructural development of the cortex in newborn infants with CHD.\n\nMethodsWe measured macrostructural and microstructural properties of the cortex in 48 newborn infants with complex CHD and 48 age-matched healthy controls. Cortical volume and gyrification index were calculated from high resolution structural MRI. Neurite density and orientation dispersion indices were modelled using high angular resolution diffusion MRI. Cerebral oxygen delivery was estimated in infants with CHD using phase contrast MRI and pre-ductal pulse oximetry. We used tract-based spatial statistics to examine voxel-wise group differences in cortical microstructure.\n\nResultsMicrostructural development of the cortex was abnormal in 48 infants with CHD, with regions of increased fractional anisotropy (FA) and reduced orientation dispersion index (ODI) compared to 48 healthy controls, correcting for gestational age at birth and scan (FWE-corrected for multiple comparisons at P<0.05). Regions of reduced cortical ODI in infants with CHD were related to impaired cerebral oxygen delivery (R2=0.637, n=39). Cortical ODI was associated with gyrification index (R2=0.589, P<0.0001, n=48).\n\nConclusionsThis study suggests that the primary component of cerebral cortex dysmaturation in CHD is impaired dendritic arborisation, which may underlie abnormal macrostructural findings reported in this population. The degree of impairment was related to cerebral oxygen delivery, supporting the hypothesis that maternal oxygen therapy may be beneficial in this population.

neuroscience↗