title: An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization.
abstract: 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.
authors.affiliation: Centre d'Imagerie BioMédicale (CIBM), Switzerland; Radiology department, Lausanne University Hospital Center (CHUV), University of Lausanne (UNIL), Switzerland. Electronic address: sebastien.tourbier@unil.ch.
authors.forename: Sébastien
authors.initials: S
authors.lastname: Tourbier
authors.affiliation: Signal Processing Laboratory (LTS2), Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
authors.forename: Xavier
authors.initials: X
authors.lastname: Bresson
authors.affiliation: Radiology department, Lausanne University Hospital Center (CHUV), University of Lausanne (UNIL), Switzerland.
authors.forename: Patric
authors.initials: P
authors.lastname: Hagmann
authors.affiliation: Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Radiology department, Lausanne University Hospital Center (CHUV), University of Lausanne (UNIL), Switzerland.
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Thiran
authors.affiliation: Radiology department, Lausanne University Hospital Center (CHUV), University of Lausanne (UNIL), Switzerland.
authors.forename: Reto
authors.initials: R
authors.lastname: Meuli
authors.affiliation: Centre d'Imagerie BioMédicale (CIBM), Switzerland; Radiology department, Lausanne University Hospital Center (CHUV), University of Lausanne (UNIL), Switzerland; Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
authors.forename: Meritxell Bach
authors.initials: MB
authors.lastname: Cuadra
mesh_terms: D000465:Algorithms; D001921:Brain / Q000196:embryology*; D005333:Fetus / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*
keywords: Fast convex optimization; Fetal brain MRI; Super-resolution; Total variation