Novel Image Processing Methods for Improved Fetal Brain MRI

Détails

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ID Serval
serval:BIB_92AC4B1CA9EE
Type
Thèse: thèse de doctorat.
Collection
Publications
Titre
Novel Image Processing Methods for Improved Fetal Brain MRI
Auteur(s)
TOURBIER  Sébastien
Directeur(s)
Meuli  Reto
Codirecteur(s)
Bach-Cuadra  Meritxel
Détails de l'institution
Université de Lausanne, Faculté de biologie et médecine
Statut éditorial
Acceptée
Date de publication
30/09/2016
Langue
anglais
Nombre de pages
149
Résumé
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.
Mots-clé
Brain, Fetal,MRI, Slice-to-Volume Reconstruction, Super-Resolution, Total Variation Regularization, Convex Optimization, Atlas-based Segmentation, Slice-by-Slice Deii formable Registration, Brain Localization, Brain Extraction, Bias Field Correction
Création de la notice
28/03/2013 12:37
Dernière modification de la notice
20/08/2019 14:55
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