11 research outputs found
Novel Image Processing Methods for Improved Fetal Brain MRI
Fetal magnetic resonance imaging (MRI) has been increasingly used as a powerful complement
imaging modality to ultrasound imaging (US) for the clinical evaluation of prenatal
abnormalities. Specifically, clinical application of fetal MRI has been significantly improved in
the nineties by hardware and software advances with the development of ultrafast multi-slice
T2-weighted (T2w) acquisition sequences able to freeze the unpredictable fetal motion and
provide excellent soft-tissue contrast. Fetal motion is indeed the major challenge in fetal
MRI and slice acquisition time should be kept as short as possible. As a result, typical fetal
MRI examination involves the acquisition of a set of orthogonally planned scans of thick
two-dimensional slices, largely free of intra-slice motion artifacts. The poor resolution in
the slice-select dimension as well as possible motion occurring between slices limits further
quantitative data analysis, which is the key for a better understanding of the developing
brain but also the key for the determination of operator-independent biomarkers that might
significantly facilitate fetal diagnosis and prognosis.
To this end, several research groups have developed in the past ten years advanced image
processing methods, often denoted by motion-robust super-resolution (SR) techniques, to
reconstruct from a set of clinical low-resolution (LR) scans, a high-resolution (HR) motion-free
volume. SR problem is usually modeled as a linear inverse problem describing the imaging
degradation due to acquisition and fetal motion. Typically, such approaches consist in iterating
between slice motion estimation that estimates the motion parameters and SR that recovers
the HR image given the estimated degradation model. This thesis focuses on the development
of novel advanced image processing methods, which have enabled the design of a completely
automated reconstruction pipeline for fetal MRI. The proposed techniques help in improving
state-of-the-art fetal MRI reconstruction in terms of efficiency, robustness and minimized
user-interactions, with the ultimate goal of being translated to the clinical environment.
The first part focuses on the development of a more efficient Total Variation (TV)-regularized
optimization algorithm for the SR problem. The algorithm uses recent advances in convex optimization
with a novel adaptive regularization strategy to offer simultaneously fast, accurate
and robust solutions to the fetal image recovery problem. Extensive validations on both
simulated fetal and real clinical data show the proposed algorithm is highly robust in front of
motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI
recovery as in comparison with state-of-the art methods.
The second part focuses on the development of a novel automatic brain localization and
extraction approach based on template-to-slice block matching and deformable slice-totemplate
registration. Asmost fetal brain MRI reconstruction algorithms rely only on brain
tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion
correction and image reconstruction, the fetal brain needs to be localized and extracted as
a first step. These tasks generally necessitate user interaction, manually or semi-automatically
done. Our methods have enabled the design of completely automated reconstruction pipeline
that involves intensity normalization, inter-slice motion estimation, and super-resolution.
Quantitative evaluation on clinical MRI scans shows that our approach produces brain masks
that are very close to manually drawn brain masks, and ratings performed by two expert
observers show that the proposed pipeline achieves similar reconstruction quality to reference
reconstruction based on manual slice-by-slice brain extraction without any further effort.
The third part investigates the possibility of automatic cortical folding quantification, one of
the best biomarkers of brain maturation, by combining our automatic reconstruction pipeline
with a state-of-the-art fetal brain tissue segmentation method and existing automated tools
provided for adult brain’s cortical folding quantification. Results indicate that our reconstruction
pipeline can provide HR MR images with sufficient quality that enable the use of surface
tessellation and active surface algorithms similar to those developed for adults to extract
meaningful information about fetal brain maturation.
Finally, the last part presents new methodological improvements of the reconstruction
pipeline aiming at improving the quality of the image for quantitative data analysis, whose
accuracy is highly dependent on the quality and resolution of the reconstructed image. In
particular, it presents a more consistent and global magnetic bias field correction method
which takes advantage of the super-resolution framework to provide a final reconstructed
image quasi free of the smooth bias field. Then, it presents a new TV SR algorithm that uses
the Huber norm in the data fidelity term to be more robust to non-Gaussian outliers. It
also presents the design of a novel joint reconstruction-segmentation framework and the
development of a novel TV SR algorithm driven by segmentation to produce images with
enhanced edge information that could ultimately improve their segmentation. Finally, it
preliminary investigates the capability of increasing the resolution in the in-plane dimensions
using SR to ultimately reduce the partial volume effect
Segmentation of Fetal Pericerebral Spaces Based on Reconstructed High-Resolution MRI
International audienc
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
Efficient total variation algorithm for fetal MRI reconstruction
Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1], Total Variation (TV)based energies [2,3] and more recently non-local means [4]. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n(2)) and O(1/root epsilon), while existing techniques are in O(1/n) and O(1/epsilon). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy
The connectome spectrum as a canonical basis for a sparse representation of fast brain activity
The functional organization of neural processes is constrained by the brain's intrinsic structural connectivity, i.e., the connectome. Here, we explore how structural connectivity can improve the representation of brain activity signals and their dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI, and diffusion MRI), we represent electrical brain activity at the cortical surface as a time-varying composition of harmonic modes of structural connectivity. These harmonic modes are known as connectome harmonics. Here we describe brain activity signal as a time-varying combination of connectome harmonics. We term this description as the connectome spectrum of the signal. We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. And last, the connectome spectrum characterizes fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that a smaller number of dimensions capture the differences between low-level and high-level visual processing in the connectome spectrum. Also, we demonstrate that connectome harmonics capture more sensitively the topological properties of brain activity. In summary, this work provides statistical, functional, and topological evidence indicating that the description of brain activity in terms of structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning
Quantitative Evaluation of Enhanced Multi-plane Clinical Fetal Diffusion MRI with a Crossing-Fiber Phantom
Diffusion Magnetic Resonance Imaging (dMRI) has become widely used to study in vivo white matter tissue properties noninvasively. However, fetal dMRI is greatly limited in Signal-to-Noise ratio and spatial resolution. Due to the uncontrollable fetal motion, echo planar imaging acquisitions often result in highly degraded images, hence the ability to depict precise diffusion MR properties remains unknown. To the best of our knowledge, this is the first study to evaluate diffusion properties in a fetal customized crossing-fiber phantom. We assessed the effect of scanning settings on diffusion quantities in a phantom specifically designed to mimic typical values in the fetal brain. Orthogonal acquisitions based on clinical fetal brain schemes were preprocessed for denoising, bias field inhomogeneity and distortion correction. We estimated the fractional anisotropy (FA) and mean diffusivity (MD) from the diffusion tensor, and the fiber orientations from the fiber orientation distribution function. Quantitative evaluation was carried out on the number of diffusion gradient directions, different orthogonal acquisitions, and enhanced 4D volumes from scattered data interpolation of multiple series. We found out that while MD does not vary with the number of diffusion gradient directions nor the number of orthogonal series, FA is slightly more accurate with more directions. Additionally, errors in all scalar diffusion maps are reduced by using enhanced 4D volumes. Moreover, reduced fiber orientation estimation errors were obtained when used enhanced 4D volumes, but not with more diffusion gradient directions. From these results, we conclude that using enhanced 4D volumes from multiple series should be preferred over using more diffusion gradient directions in clinical fetal dMRI
The network integration of epileptic activity in relation to surgical outcome
Objective: Epilepsy is a network disease with epileptic activity and cognitive impairment involving large-scale brain networks. A complex network is involved in the seizure and in the interictal epileptiform discharges (IEDs). Directed connectivity analysis, describing the information transfer between brain regions, and graph analysis are applied to high-density EEG to characterise networks. Methods: We analysed 19 patients with focal epilepsy who had high-density EEG containing IED and underwent surgery. We estimated cortical activity during IED using electric source analysis in 72 atlas-based cortical regions of the individual brain MRI. We applied directed connectivity analysis (information Partial Directed Coherence) and graph analysis on these sources and compared patients with good vs poor post-operative outcome at global, hemispheric and lobar level. Results: We found lower network integration reflected by global, hemispheric, lobar efficiency during the IED (p<0.05) in patients with good post-surgical outcome, compared to patients with poor outcome. Prediction was better than using the IED field or the localisation obtained by electric source imaging. Conclusions: Abnormal network patterns in epilepsy are related to seizure outcome after surgery. Significance: Our finding may help understand networks related to a more “isolated” epileptic activity, limiting the extent of the epileptic network in patients with subsequent good post-operative outcome