Accelerated multi-contrast high isotropic resolution 3D intracranial vessel wall MRI using a tailored k-space undersampling and partially parallel reconstruction strategy

Objective

To develop a 3D multi-contrast IVW protocol with 0.5-mm isotropic resolution and a scan time of 5 min per sequence.

Materials and methods

Pre-contrast T1w VISTA, DANTE prepared PDw VISTA, SNAP, and post-contrast T1w VISTA were accelerated using cartesian undersampling with target ordering method (CUSTOM) and self-supporting tailored k-space estimation for parallel imaging reconstruction (STEP). CUSTOM + STEP IVW was compared to full-sample IVW, SENSE-accelerated IVW, and CUSTOM + zero-filled Fourier reconstruction in normal volunteers and subjects with intracranial atherosclerotic disease (ICAD). Image quality, vessel delineation, CSF suppression, and blood suppression were compared.

Results

CUSTOM + STEP vessel wall delineation was comparable to full-sample IVW and better than SENSE IVW for vessel wall delineation on T1w VISTA and luminal contrast on SNAP. Average image quality and wall depiction were significantly improved using STEP reconstruction compared with zero-filled Fourier reconstruction, with no significant difference in CSF or blood suppression.

Conclusions

CUSTOM + STEP allowed multi-contrast 3D 0.5-mm isotropic IVW within 30 min. Although some quantitative and qualitative scores for CUSTOM − STEP were lower than fully sampled IVW, CUSTOM + STEP provided comparable vessel wall delineation as full-sample IVW and was superior to SENSE. CUSTOM + STEP IVW was well tolerated by patients and showed good delineation of ICAD plaque.

     

OXYGEN TRANSFER IN HORIZONTAL PIPELINE AERATORS WITH NOZZLES

Horizontal pipeline and tubular loop aerators are of interest for fermentation and waste water treatment and are ideally suited for continuous processing. A major drawback is that these pipeline contactors invariably operate in the “elongated bubble and plug” regime in which the mass transfer rate is low. This article evaluates the performance of a horizontal pipeline aerator fitted with nozzles equispaced along its length to enhance mass transfer rates by promoting turbulence and augmenting effective interfacial area. Such devices can also be advantageously used in long pipe lines as in the case of treating waste while it is being transported. Pressure drop and overall liquid-side mass transfer coefficient data are reported as functions of liquid (water) and gas flow rates and nozzle size and spacing. It is shown that for all the conditions studied, k_La = 0.026(ΔP/L)^1.036 and that the pressure gradient is given by a simple correlation, provided an empirical parameter which characterises a nozzle is known. Preliminary investigations on the effect of surfactant ad the presence of suspended solids (size 75 μm) on mass transfer coefficient are also reported. Very high values of power dissipation can be achieved in such aerators without mechanically moving parts and high values of mass transfer coefficient can be realized.     

A computational approach to characterize formation of a passivation layer in lithium metal anodes

Li metal anode is the “Holy Grail” material of advanced Lithium-ion-batteries (LIBs). However, it is plagued by uncontrollable dendrite growth resulting in poor cycling efficiency and short-circuiting of batteries. This has spurred a plethora of research to understand the underlying mechanism of dendrite formation. While experimental studies suggest that there are complex physical and chemical interactions between heterogeneous solid-electrolyte interphase (SEI) and dendrite growth, most of the studies do not reveal the mechanisms triggering these interactions. To deal with this knowledge gap, we propose a multiscale modeling framework which couples kinetic Monte Carlo and Molecular Dynamics simulations. Specifically, the model has been developed to account for (a) heterogeneous SEI, (b) dendrite-SEI interactions, and (c) effect of electrolyte on Li electrodeposition and potential dendrite formation. This allows the proposed computational model to be extended to various electrolytes and SEI species and generate results consistent with previous experimental studies.     

Interpenetrating polymer network/functionalized-reduced graphene oxide nanocomposite: As an advanced functional material

In this report, smart polyurethane-polystyrene interpenetrating polymer network (IPN)-based nanocomposites were fabricated using simultaneous polymerization technique with different doses of functionalized reduced graphene oxide (f-RGO). RGO was functionalized with monoglyceride of sunflower oil in the presence of toluene diisocyanate. Successful functionalization of RGO was supported by Fourier-transform infrared spectroscopy, X-Ray diffraction, and transmission electron microscopy analyses. Most interestingly, the fabricated IPN-based nanocomposites showed significant enhancements in mechanical (tensile strength: 165%; elongation at break: 198%; toughness: 340%) and thermal (thermally stable up to 262°C) properties upon incorporation of 1 weight% of f-RGO. Moreover, the fabricated nanocomposites exhibited outstanding chemical resistance, self-cleaning behavior through surface hydrophobicity (static contact angle: 125.6–136.5°), multi-stimuli responsive shape memory effect (100% recovery within 33–44 s by microwave and 265–308 s by sunlight) and thermally actuated artificial muscle-like behavior. Therefore, the studied smart nanocomposites with the aforementioned properties hold significant potential for possible advanced applications.     

Approximate low-rank factorization with structured factors

An approximate rank revealing factorization problem with structure constraints on the normalized factors is considered. Examples of structure, motivated by an application in microarray data analysis, are sparsity, nonnegativity, periodicity, and smoothness. In general, the approximate rank revealing factorization problem is nonconvex. An alternating projections algorithm is developed, which is globally convergent to a locally optimal solution. Although the algorithm is developed for a specific application in microarray data analysis, the approach is applicable to other types of structures.     

Non-Parametric Mixture Model Based Evolution of Level Sets and Application to Medical Images

We present a novel region-based curve evolution algorithm which has three primary contributions: (i) non-parametric estimation of probability distributions using the recently developed NP windows method; (ii) an inequality-constrained least squares method to model the image histogram with a mixture of nonparametric probability distributions; and (iii) accommodation of the partial volume effect, which is primarily due to low resolution images, and which often poses a significant challenge in medical image analysis (our primary application area). We first approximate the image intensity histogram as a mixture of non-parametric probability density functions (PDFs), justifying its use with respect to medical image analysis. The individual densities in the mixture are estimated using the recent NP windows PDF estimation method, which builds a continuous representation of discrete signals. A Bayesian framework is then formulated in which likelihood probabilities are given by the non-parametric PDFs and prior probabilities are calculated using an inequality constrained least squares method. The non-parametric PDFs are then learnt and the segmentation solution is spatially regularised using a level sets framework. The log ratio of the posterior probabilities is used to drive the level set evolution. As background to our approach, we recall related developments in level set methods. Results are presented for a set of synthetic and natural images as well as simulated and real medical images of various anatomical organs. Results on a range of images show the effectiveness of the proposed algorithm.     

Video traffic analytics for large scale surveillance

The video traffic analysis is the most important issue for large scale surveillance. In the large scale surveillance system, huge amount of live digital video data is submitted to the storage servers through the number of externally connected scalable components. The system also contains huge amount of popular and unpopular old videos in the archived storage servers. The video data is delivered to the viewers, partly or completely on demand through a compact system. In real time, huge amount of video data is imported to the viewer’s node for various analysis purposes. The viewers use a number of interactive operations during the real time tracking suspect. The compact video on demand system is used in peer to peer mesh type hybrid architecture. The chunk of video objects move fast through the real time generated compact topological space. Video traffic analytics is required to transfer compressed multimedia data efficiently. In this work, we present a dynamically developed topological space, using mixed strategy by game approach to move the video traffic faster. The simulation results are well addressed in real life scenario.     

Markov chain Monte Carlo methods for state-space models with point process observations

This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations. We quantified the efficiencies of these MCMC methods on synthetic data, and our results suggest that the Reimannian manifold Hamiltonian Monte Carlo method offers the best performance. We further compared such a method with a previously tested variational Bayes method on two experimental data sets. Results indicate similar performance on the large data sets and superior performance on small ones. The work offers an extensive suite of MCMC algorithms evaluated on an important class of models for physiological signal analysis.