69 research outputs found
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
3D T2w fetal body MRI:automated organ volumetry, growth charts and population-averaged atlas
Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22-38 weeks gestational age range. In addition, the results of comparison between 60 normal and 12 fetal growth restriction datasets revealed significant differences in organ volumes.</p
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
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