3D reconstruction of medical images from slices automatically landmarked with growing neural models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.

Liquid–Liquid Equilibria of Water + Ethanol + 1-Butyl-3-methylimidazolium Bis(trifluoromethanesulfonyl)imide Ternary System: Measurements and Correlation at Different Temperatures

In this work authors present the experimental liquid–liquid equilibria (LLE) data of water + ethanol + 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide ([bmim][Tf2N]) system at different temperatures. The LLE of the system was obtained in the temperature range from 283.2 to 323.2 K. The nonrandom two liquid (NRTL) and universal quasichemical (UNIQUAC) models were used to correlate ternary systems. The equilibrium compositions were successfully correlated by the interaction parameters from both models, however UNIQUAC gave a more accurate correlation. Finally, a study about the solvent capability of ionic liquid was made in order to evaluate the possibility of separating the mixture formed by ethanol and water using that ionic liquid.