Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction

We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based on two distinct sub-networks, with the first sub-network estimating the regularization parameter-map from the input data while the second one unrolling T iterations of the Primal-Dual Three-Operator Splitting (PD3O) algorithm. The latter approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean-corrupted data but crucially without the need of having access to labels for the optimal regularization parameter-maps

Learning Regularization Parameter-Maps for Variational Image Reconstruction Using Deep Neural Networks and Algorithm Unrolling

We introduce a method for the fast estimation of data-adapted, spatially and temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variation (TV) minimization. The proposed approach is inspired by recent developments in algorithm unrolling using deep neural networks (NNs) and relies on two distinct subnetworks. The first subnetwork estimates the regularization parameter-map from the input data. The second subnetwork unrolls iterations of an iterative algorithm which approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean and corrupted data but crucially without the need for access to labels for the optimal regularization parameter-maps. We first prove consistency of the unrolled scheme by showing that the unrolled minimizing energy functional used for the supervised learning -converges, as tends to infinity, to the corresponding functional that incorporates the exact solution map of the TV-minimization problem. Then, we apply and evaluate the proposed method on a variety of large-scale and dynamic imaging problems with retrospectively simulated measurement data for which the automatic computation of such regularization parameters has been so far challenging using the state-of-the-art methods: a 2D dynamic cardiac magnetic resonance imaging (MRI) reconstruction problem, a quantitative brain MRI reconstruction problem, a low-dose computed tomography problem, and a dynamic image denoising problem. The proposed method consistently improves the TV reconstructions using scalar regularization parameters, and the obtained regularization parameter-maps adapt well to imaging problems and data by leading to the preservation of detailed features. Although the choice of the regularization parameter-maps is data-driven and based on NNs, the subsequent reconstruction algorithm is interpretable since it inherits the properties (e.g., convergence guarantees) of the iterative reconstruction method from which the network is implicitly defined

Motion estimation and correction for simultaneous PET/MR using SIRF and CIL

SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'

Mass flows, turbidity currents and other hydrodynamic consequences of small and moderate earthquakes in the Sea of Marmara

Earthquake-induced submarine slope destabilization is known to cause mass wasting and turbidity currents, but the hydrodynamic processes associated with these events remain poorly understood. Instrumental records are rare, and this notably limits our ability to interpret marine paleoseismological sedimentary records. An instrumented frame comprising a pressure recorder and a Doppler recording current meter deployed at the seafloor in the Sea of Marmara Central Basin recorded the consequences of a Mw 5.8 earthquake occurring on 26 September 2019 and of a Mw 4.7 foreshock 2 d before. The smaller event caused sediment resuspension and weak current (&lt;4 cm s−1) in the water column. The larger event triggered a complex response involving a debris flow and turbidity currents with variable velocities and orientations, which may have resulted from multiple slope failures. A long delay of 10 h is observed between the earthquake and the passing of the strongest turbidity current. The distance traveled by the sediment particles during the event is estimated to have extended over several kilometers, which could account for a local deposit on a sediment fan at the outlet of a canyon (where the instrument was located), but the sedimentation event did not likely cover the whole basin floor. We show that after a moderate earthquake, delayed turbidity current initiation may occur, possibly by ignition of a cloud of resuspended sediment.</p

Modélisation du déferlement dû à la bathymétrie dans un code de simulation des vagues non-linéaires et dispersives en zone côtière

International audienceThe modeling of wave breaking dissipation in shallow water within a fully nonlinear and disper-sive wave model is investigated. The wave propagation model is based on potential flow theory and initially assumes non-overturning waves. Inclusion of breaking dissipation is however possible by adding a pressure-like term to the dynamic free surface boundary condition. Two criteria from the literature are tested to determine the onset of breaking, one geometric and another energetic. Two methods are tested in order to dissipate the energy due to breaking, the first based on the analogy of a breaking wave with a breaking hydraulic jump, and the second relying on an eddy viscosity dissipative term. Numerical simulations are performed using combinations of the two criteria and the two dissipation methods. The results are compared with experiments of waves breaking over a submerged bar, considering first regular waves, and then irregular waves. It is shown that the different approaches are able to reproduce the wave transformation observed in the experiments, although additional tests remain to be performed to fully validate the model for a wider range of conditions.La modélisation de la dissipation d'énergie due au déferlement en eau peu profonde dans un code de vagues complètement non-linéaire et dispersif est étudiée. Le modèle de propagation des vagues est fondé sur la théorie potentielle et suppose au départ que la surface libre ne se retourne pas. L'inclusion de la dissipation par déferlement est toutefois possible en ajoutant un terme similaire à une pression dans la condition limite dynamique de surface libre. Deux critères issus de la littérature sont testés pour déterminer le début de déferlement, l'un de type géométrique et l'autre de type énergétique. Deux méthodes sont testées afin de dissiper l'énergie, l'une basée sur la similarité d'une vague déferlante avec un ressaut hydraulique déferlant et l'autre utilisant un terme dissipatif de type viscosité turbulente. Les simulations numériques sont effectuées à l'aide de combinaisons des deux critères et des deux méthodes de dissipation. Leurs résultats sont comparés à des expériences des vagues déferlant sur une barre immergée, d'abord pour des vagues régulières, puis pour des vagues irrégulières. Nous montrons que les différentes approches sont capables de reproduire les évolutions des trains de vagues observées expérimentalement, bien que des tests supplémentaires restent à mener pour valider complètement le modèle dans une gamme de conditions plus large