Deep Learning-Based Super-Resolution Applied to Dental Computed Tomography

The resolution of dental computed tomography (CT) images is limited by detector geometry, sensitivity, patient movement, the reconstruction technique and the need to minimize radiation dose. Recently, the use of convolutional neural network (CNN) architectures has shown promise as a resolution enhancement method. In the current work, two CNN architectures—a subpixel network and the so called U-net—have been considered for the resolution enhancement of 2-D cone-beam CT image slices of ex vivo teeth. To do so, a training set of 5680 cross-sectional slices of 13 teeth and a test set of 1824 slices of 4 structurally different teeth were used. Two existing reconstruction-based super-resolution methods using l2-norm and total variation regularization were used for comparison. The results were evaluated with different metrics (peak signal-to-noise ratio, structure similarity index, and other objective measures estimating human perception) and subsequent image-segmentation-based analysis. In the evaluation, micro-CT images were used as ground truth.The results suggest the superiority of the proposed CNN-based approaches over reconstruction-based methods in the case of dental CT images, allowing better detection of medically salient features, such as the size, shape, or curvature of the root canal

The bouncing dynamics of inertial self-propelled particles reveals directional asymmetry

The paper is devoted to the study of experimental conditions for bound states in which active particles are forced by their environment to move forward and backward in a steady oscillatory mode. The realization of this idea in our proposal is carried out by means of a hexbug (a vibrating self-propelled toy robot) confined in a narrow channel bounded by a rigid moving end-wall. The motion modes were studied both experimentally and theoretically. In the theoretical framework, we investigated the one-dimensional single-particle approximation within the Brownian model of active particles with inertia using numerical simulations. The model includes the hexbug's contact with the base plate via tilted flexible legs that provide propulsion. At the same time, the tilting contributes to directional asymmetry of the system. The end-wall velocity was used as an external parameter to influence the bouncing process. After regressing the spatial and temporal statistical characteristics, the simulation satisfactorily reproduced the experimental properties of hexbug motion, mainly due to the considered directional asymmetry

The distance-based approach to the quantification of the world convergences and imbalances - comparisons across countries and factors

The paper presents a general empirical method of distance-based multifaceted systematic identifying of the positions of countries in relation to inequalities and imbalances. In order to understand the world economic relations in their entirety, we decided to analyze twelve most populous countries and eleven macroeconomic, environmental and demographic indicators relevant to them. Our analysis covering the period 1992-2008 attempts to identify core parts of the global economic system and countries that pose a potential risk of instability

Magnetic dot arrays modeling via the system of the radial basis function networks

Two dimensional square lattice general model of the magnetic dot array is introduced. In this model the intradot self-energy is predicted via the neural network and interdot magnetostatic coupling is approximated by the collection of several dipolar terms. The model has been applied to disk-shaped cluster involving 193 ultrathin dots and 772 interaction centers. In this case among the intradot magnetic structures retrieved by neural networks the important role play single-vortex magnetization modes. Several aspects of the model have been understood numerically by means of the simulated annealing method.Comment: 16 pages, 8 figure

Assessing the Viscoelasticity of Photopolymer Nanowires Using a Three-Parameter Solid Model for Bending Recovery Motion

Photopolymer nanowires prepared by two-photon polymerization direct laser writing (TPP-DLW) are the building blocks of many microstructure systems. These nanowires possess viscoelastic characteristics that define their deformations under applied forces when operated in a dynamic regime. A simple mechanical model was previously used to describe the bending recovery motion of deflected nanowire cantilevers in Newtonian liquids. The inverse problem is targeted in this work; the experimental observations are used to determine the nanowire physical characteristics. Most importantly, based on the linear three-parameter solid model, we derive explicit formulas to calculate the viscoelastic material parameters. It is shown that the effective elastic modulus of the studied nanowires is two orders of magnitude lower than measured for the bulk material. Additionally, we report on a notable effect of the surrounding aqueous glucose solution on the elasticity and the intrinsic viscosity of the studied nanowires made of Ormocomp