Synthesis of realistic fetal MRI with conditional Generative Adversarial Networks

Fetal brain magnetic resonance imaging serves as an emerging modality for prenatal counseling and diagnosis in disorders affecting the brain. Machine learning based segmentation plays an important role in the quantification of brain development. However, a limiting factor is the lack of sufficiently large, labeled training data. Our study explored the application of SPADE, a conditional general adversarial network (cGAN), which learns the mapping from the label to the image space. The input to the network was super-resolution T2-weighted cerebral MRI data of 120 fetuses (gestational age range: 20-35 weeks, normal and pathological), which were annotated for 7 different tissue categories. SPADE networks were trained on 256*256 2D slices of the reconstructed volumes (image and label pairs) in each orthogonal orientation. To combine the generated volumes from each orientation into one image, a simple mean of the outputs of the three networks was taken. Based on the label maps only, we synthesized highly realistic images. However, some finer details, like small vessels were not synthesized. A structural similarity index (SSIM) of 0.972+-0.016 and correlation coefficient of 0.974+-0.008 were achieved. To demonstrate the capacity of the cGAN to create new anatomical variants, we artificially dilated the ventricles in the segmentation map and created synthetic MRI of different degrees of fetal hydrocephalus. cGANs, such as the SPADE algorithm, allow the generation of hypothetically unseen scenarios and anatomical configurations in the label space, which data in turn can be utilized for training various machine learning algorithms. In the future, this algorithm would be used for generating large, synthetic datasets representing fetal brain development. These datasets would potentially improve the performance of currently available segmentation networks

A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)

Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced image processing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth. In this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates clinical T2-weighted fast spin echo sequences of the fetal brain. This unique tool is based on a general, flexible and realistic setup that includes stochastic fetal movements, thus providing images of the fetal brain throughout maturation comparable to clinical acquisitions. We demonstrate its value to evaluate the robustness and optimize the accuracy of an algorithm for super-resolution fetal brain magnetic resonance imaging from simulated motion-corrupted 2D low-resolution series compared to a synthetic high-resolution reference volume. We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation

Characterization of dynamic patterns of human fetal to neonatal brain asymmetry with deformation-based morphometry

IntroductionDespite established knowledge on the morphological and functional asymmetries in the human brain, the understanding of how brain asymmetry patterns change during late fetal to neonatal life remains incomplete. The goal of this study was to characterize the dynamic patterns of inter-hemispheric brain asymmetry over this critically important developmental stage using longitudinally acquired MRI scans.MethodsSuper-resolution reconstructed T2-weighted MRI of 20 neurotypically developing participants were used, and for each participant fetal and neonatal MRI was acquired. To quantify brain morphological changes, deformation-based morphometry (DBM) on the longitudinal MRI scans was utilized. Two registration frameworks were evaluated and used in our study: (A) fetal to neonatal image registration and (B) registration through a mid-time template. Developmental changes of cerebral asymmetry were characterized as (A) the inter-hemispheric differences of the Jacobian determinant (JD) of fetal to neonatal morphometry change and the (B) time-dependent change of the JD capturing left-right differences at fetal or neonatal time points. Left-right and fetal-neonatal differences were statistically tested using multivariate linear models, corrected for participants’ age and sex and using threshold-free cluster enhancement.ResultsFetal to neonatal morphometry changes demonstrated asymmetry in the temporal pole, and left-right asymmetry differences between fetal and neonatal timepoints revealed temporal changes in the temporal pole, likely to go from right dominant in fetal to a bilateral morphology in neonatal timepoint. Furthermore, the analysis revealed right-dominant subcortical gray matter in neonates and three clusters of increased JD values in the left hemisphere from fetal to neonatal timepoints.DiscussionWhile these findings provide evidence that morphological asymmetry gradually emerges during development, discrepancies between registration frameworks require careful considerations when using DBM for longitudinal data of early brain development

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Measurement of nuclear transparency ratios for protons and neutrons

This paper presents, for the first time, measurements of neutron transparency ratios for nuclei relative to C measured using the (e,e′n) reaction, spanning measured neutron momenta of 1.4 to 2.4 GeV/c. The transparency ratios were extracted in two kinematical regions, corresponding to knockout of mean-field nucleons and to the breakup of Short-Range Correlated nucleon pairs. The extracted neutron transparency ratios are consistent with each other for the two measured kinematical regions and agree with the proton transparencies extracted from new and previous (e,e′p) measurements, including those from neutron-rich nuclei such as lead. The data also agree with and confirm the Glauber approximation that is commonly used to interpret experimental data. The nuclear-mass-dependence of the extracted transparencies scales as Aα with α=−0.289±0.007, which is consistent with nuclear-surface dominance of the reactions

Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: an expert assessment

As the permafrost region warms, its large organic carbon pool will be increasingly vulnerable to decomposition, combustion, and hydrologic export. Models predict that some portion of this release will be offset by increased production of Arctic and boreal biomass; however, the lack of robust estimates of net carbon balance increases the risk of further overshooting international emissions targets. Precise empirical or model-based assessments of the critical factors driving carbon balance are unlikely in the near future, so to address this gap, we present estimates from 98 permafrost-region experts of the response of biomass, wildfire, and hydrologic carbon flux to climate change. Results suggest that contrary to model projections, total permafrost-region biomass could decrease due to water stress and disturbance, factors that are not adequately incorporated in current models. Assessments indicate that end-of-the-century organic carbon release from Arctic rivers and collapsing coastlines could increase by 75% while carbon loss via burning could increase four-fold. Experts identified water balance, shifts in vegetation community, and permafrost degradation as the key sources of uncertainty in predicting future system response. In combination with previous findings, results suggest the permafrost region will become a carbon source to the atmosphere by 2100 regardless of warming scenario but that 65%–85% of permafrost carbon release can still be avoided if human emissions are actively reduced