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An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Tourbier Sébastien , Bresson Xavier , Hagmann Patric , Thiran Jean Philippe , Meuli Reto , Bach Cuadra Meritxell ,
Project Novel Image Processing Methods for Fetal MR Imaging: 3D Reconstruction and Segmentation with Soft Priors
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Original article (peer-reviewed)

Journal NeuroImage
Page(s) 584 - 597
Title of proceedings NeuroImage
DOI 10.1016/j.neuroimage.2015.06.018


© 2015 Elsevier Inc.. Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) images, a high-resolution (HR) motion-free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work, it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super-resolution problem. In this work, two major contributions are presented. Firstly, we will briefly review the Bayesian and Variational dual formulations of current state-of-the-art methods dedicated to fetal MRI reconstruction. Secondly, we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation 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.