Reconstruction of blood flow velocity with deep learning information fusion from spectral ct projections and vessel geometry.

In this work, we investigate a new deep learning reconstruction method of blood flow velocity within deformed vessels from contrast enhanced X-ray projections and vessel geometry. The principle of the method is to perform linear or nonlinear dimension reductions on the Radon projections and on the mesh of the vessel. These low dimensional projections are then fused to obtain the velocity field in the vessel.

Deep learning methods for blood flow reconstruction in a vessel with contrast enhanced x-ray computed tomography.

The reconstruction of blood velocity in a vessel from contrast enhanced x-ray computed tomography projections is a complex inverse problem. It can be formulated as reconstruction problem with a partial differential equation constraint. A solution can be estimated with the a variational adjoint method and proper orthogonal decomposition (POD) basis.

Deep Gauss-Newton for phase retrieval.

We propose the deep Gauss-Newton (DGN) algorithm. The DGN allows one to take into account the knowledge of the forward model in a deep neural network by unrolling a Gauss-Newton optimization method. No regularization or step size needs to be chosen; they are learned through convolutional neural networks.

Nonlinear primal-dual algorithm for the phase and absorption retrieval from a single phase contrast image.

We propose a nonlinear primal-dual algorithm for the retrieval of phase shift and absorption from a single x ray in-line phase contrast, or Fresnel diffraction, image. The algorithm permits us to regularize phase and absorption separately. We demonstrate that taking into account the nonlinearity in the reconstruction improves reconstruction compared with linear methods.

Mixed scale dense convolutional networks for x-ray phase contrast imaging.

X-ray in-line phase contrast imaging relies on the measurement of Fresnel diffraction intensity patterns due to the phase shift and the attenuation induced by the object. The recovery of phase and attenuation from one or several diffraction patterns is a nonlinear ill-posed inverse problem. In this work, we propose supervised learning approaches using mixed scale dense (MS-D) convolutional neural networks to simultaneously retrieve the phase and the attenuation from x-ray phase contrast images.

Impact of the training loss in deep learning-based CT reconstruction of bone microarchitecture.

Purpose: Computed tomography (CT) is a technique of choice to image bone structure at different scales. Methods to enhance the quality of degraded reconstructions obtained from low-dose CT data have shown impressive results recently, especially in the realm of supervised deep learning. As the choice of the loss function affects the reconstruction quality, it is necessary to focus on the way neural networks evaluate the correspondence between predicted and target images during the training stage.

Comparison of five one-step reconstruction algorithms for spectral CT.

Over the last decade, dual-energy CT scanners have gone from prototypes to clinically available machines, and spectral photon counting CT scanners are following. They require a specific reconstruction process, consisting of two steps: material decomposition and tomographic reconstruction. Image-based methods perform reconstruction, then decomposition, while projection-based methods perform decomposition first, and then reconstruction.

Regularization of nonlinear decomposition of spectral x-ray projection images.

Purpose: Exploiting the x-ray measurements obtained in different energy bins, spectral computed tomography (CT) has the ability to recover the 3-D description of a patient in a material basis. This may be achieved solving two subproblems, namely the material decomposition and the tomographic reconstruction problems. In this work, we address the material decomposition of spectral x-ray projection images, which is a nonlinear ill-posed problem.

Nonlinear approaches for the single-distance phase retrieval problem involving regularizations with sparsity constraints.

The phase retrieval process is a nonlinear ill-posed problem. The Fresnel diffraction patterns obtained with hard x-ray synchrotron beam can be used to retrieve the phase contrast. In this work, we present a convergence comparison of several nonlinear approaches for the phase retrieval problem involving regularizations with sparsity constraints.

Clay dispersion and aspect ratios in polymer-clay nanocomposites.

It has clearly been shown in the literature that the properties achieved by polymer clay nanocomposites are often related to their structures and to the states of dispersion of the silicate platelets in the polymer matrices. Unfortunately, up to date most techniques used in a standard procedure do not allow a correct interpretation of polymer-clay nanocomposite structure and dispersion. In a recent work, we proposed an image analysis procedure (I.

Characterization of the drastic increase in molecular mobility of a deformed amorphous polymer.

Original experiments of dynamic mechanical analysis and small angle x-ray scattering on a deformed amorphous polymer below its glass transition temperature are reported. The mechanical treatment reveals high mobility zones induced by shearing and leads to a drastic increase in the molecular mobility of the system. These domains are evidenced by small angle x-ray scattering measurements, and their geometrical characteristics are independent of the applied deformation.