16 research outputs found
TBSS++: A novel computational method for Tract-Based Spatial Statistics
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess
the brain white matter. One of the most common computations in dMRI involves
cross-subject tract-specific analysis, whereby dMRI-derived biomarkers are
compared between cohorts of subjects. The accuracy and reliability of these
studies hinges on the ability to compare precisely the same white matter tracts
across subjects. This is an intricate and error-prone computation. Existing
computational methods such as Tract-Based Spatial Statistics (TBSS) suffer from
a host of shortcomings and limitations that can seriously undermine the
validity of the results. We present a new computational framework that
overcomes the limitations of existing methods via (i) accurate segmentation of
the tracts, and (ii) precise registration of data from different
subjects/scans. The registration is based on fiber orientation distributions.
To further improve the alignment of cross-subject data, we create detailed
atlases of white matter tracts. These atlases serve as an unbiased reference
space where the data from all subjects is registered for comparison. Extensive
evaluations show that, compared with TBSS, our proposed framework offers
significantly higher reproducibility and robustness to data perturbations. Our
method promises a drastic improvement in accuracy and reproducibility of
cross-subject dMRI studies that are routinely used in neuroscience and medical
research
Segmentation of the cortical plate in fetal brain MRI with a topological loss
The fetal cortical plate undergoes drastic morphological changes throughout
early in utero development that can be observed using magnetic resonance (MR)
imaging. An accurate MR image segmentation, and more importantly a
topologically correct delineation of the cortical gray matter, is a key
baseline to perform further quantitative analysis of brain development. In this
paper, we propose for the first time the integration of a topological
constraint, as an additional loss function, to enhance the morphological
consistency of a deep learning-based segmentation of the fetal cortical plate.
We quantitatively evaluate our method on 18 fetal brain atlases ranging from 21
to 38 weeks of gestation, showing the significant benefits of our method
through all gestational ages as compared to a baseline method. Furthermore,
qualitative evaluation by three different experts on 130 randomly selected
slices from 26 clinical MRIs evidences the out-performance of our method
independently of the MR reconstruction quality.Comment: 4 pages, 4 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Spatio-temporal motion correction and iterative reconstruction of in-utero fetal fMRI
Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful
imaging technique for studying functional development of the brain in utero.
However, unpredictable and excessive movement of fetuses have limited its
clinical applicability. Previous studies have focused primarily on the accurate
estimation of the motion parameters employing a single step 3D interpolation at
each individual time frame to recover a motion-free 4D fMRI image. Using only
information from a 3D spatial neighborhood neglects the temporal structure of
fMRI and useful information from neighboring timepoints. Here, we propose a
novel technique based on four dimensional iterative reconstruction of the
motion scattered fMRI slices. Quantitative evaluation of the proposed method on
a cohort of real clinical fetal fMRI data indicates improvement of
reconstruction quality compared to the conventional 3D interpolation
approaches.Comment: Accepted by MICCAI 202
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
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
Deep learning methods for diffusion MRI in early development of the human brain: resolution enhancement and model estimation
Diffusion magnetic resonance imaging has emerged as the gold standard tool for studying the brain white matter both in vivo and non-invasively, offering valuable insights into underlying tissue microstructure and brain connectivity. However, applying this technique to investigate the human developing brain, such as in fetuses and newborns, poses unique challenges. In this sensitive population, the scanning time is limited for unpredictable motion risk minimization and for ethical reasons. Additionally, images have a low signal-to-noise ratio and a low spatial resolution. Moreover, the developing brain undergoes rapidly changing microstructural properties during the last months of pregnancy and early months of birth. The application of current diffusion magnetic resonance imaging methods to developing brains is severely constrained by all these aspects, necessitating the development of tailored approaches. This thesis tackles this specific problem by proposing two deep learning based methods that leverage high quality research datasets to improve constrained clinical acquisitions. First, we have developed a method to enhance the through-plane resolution using a deep autoencoder. We show its performance over conventional image interpolation methods of the raw signal and in estimated diffusion tensor scalar maps. Second, we designed a model to predict accurate orientation distribution functions from a low number of diffusion measurements that are typically available in clinical settings. We extensively demonstrate its performance on newborn subjects compared to state-of-the-art methods (such as constrained spherical deconvolution) that need significantly more diffusion directions. We additionally show the out-of-domain generalizability of the method on clinical cohorts of newborns and fetuses. Finally, aiming at deriving optimal schemes for fetal sequences, we have conducted a quantitative validation study on a phantom with crossing-fibers, to quantify the time trade-off that is imposed by the clinical constraints, between the number of gradient directions and the number of acquired volumes.
Overall, we believe that the aforementioned methods that harness the capabilities of deep neural networks to extract transferable knowledge from large datasets, possess the potential to offer significant insights into the complex mechanisms underlying the early development of the human brain
Synthetic Magnetic Resonance Images for Domain Adaptation: Application to Fetal Brain Tissue Segmentation
The quantitative assessment of the developing human brain in utero is crucial to fully understand neurodevelopment. Thus, automated multi-tissue fetal brain segmentation algorithms are being developed, which in turn require annotated data to be trained. However, the available annotated fetal brain datasets are limited in number and heterogeneity, hampering domain adaptation strategies for robust segmentation. In this context, we use FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom, to simulate various realistic magnetic resonance images of the fetal brain along with its class labels. We demonstrate that these multiple synthetic annotated data, generated at no cost and further reconstructed using the target super-resolution technique, can be successfully used for domain adaptation of a deep learning method that segments seven brain tissues. Overall, the accuracy of the segmentation is significantly enhanced, especially in the cortical gray matter, the white matter, the cerebellum, the deep gray matter and the brainstem
Synthetic Magnetic Resonance Images for Domain Adaptation: Application to Fetal Brain Tissue Segmentation
The quantitative assessment of the developing human brain in utero is crucial to fully understand neurodevelopment. Thus, automated multi-tissue fetal brain segmentation algorithms are being developed, which in turn require annotated data to be trained. However, the available annotated fetal brain datasets are limited in number and heterogeneity, hampering domain adaptation strategies for robust segmentation. In this context, we use FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom, to simulate various realistic magnetic resonance images of the fetal brain along with its class labels. We demonstrate that these multiple synthetic annotated data, generated at no cost and further reconstructed using the target super-resolution technique, can be successfully used for domain adaptation of a deep learning method that segments seven brain tissues. Overall, the accuracy of the segmentation is significantly enhanced, especially in the cortical gray matter, the white matter, the cerebellum, the deep gray matter and the brainstem