The effect of augmented reality training on percutaneous needle placement in spinal facet joint injections

The purpose of this study was to determine if augmented reality image overlay and laser guidance systems can assist medical trainees in learning the correct placement of a needle for percutaneous facet joint injection. The Perk Station training suite was used to conduct and record the needle insertion procedures. A total of 40 volunteers were randomized into two groups of 20. 1) The Overlay group received a training session that consisted of four insertions with image and laser guidance, followed by two insertions with laser overlay only. 2) The Control group received a training session of six classical freehand insertions. Both groups then conducted two freehand insertions. The movement of the needle was tracked during the series of insertions. The final insertion procedure was assessed to determine if there was a benefit to the overlay method compared to the freehand insertions. The Overlay group had a better success rate (83.3% versus 68.4%, p=0.002), and potential for less tissue damage as measured by the amount of needle movement inside the phantom (3077.6 mm(2) versus 5607.9 mm(2) , p =0.01). These results suggest that an augmented reality overlay guidance system can assist medical trainees in acquiring technical competence in a percutaneous needle insertion procedure.     

Classification-based symbolic indoor positioning over the Miskolc IIS Data-set

Determination of indoor position is vital for the creation of smart environments. Symbolic indoor positioning algorithms determine the location as a well-defined part of the building, such as a room, a corridor or a floor. Performance analysis of classification-based symbolic indoor positioning methods are presented in this paper. Symbolic positioning can be considered as a classification task, where position denotes the category and the attributes are the measured values. Evaluation and comparison of the selected classification methods are performed over a hybrid data-set which was recorded by the ILONA (Indoor Localisation and Navigation) System. These experiments were performed in RapidMiner and the Weka framework. Accuracy is the base of comparison and the following classification methods were used: k–NN, Naive Bayes, Decision Tree, Rule Induction and Artificial Neural Network. Comparison is used to recommend a classification-based symbolic indoor positioning method to be implemented in the ILONA System. Experimental results show that the k–NN, Naive Bayes with 1 kernel and ANN classifiers achieved better than 90% accuracy. As a result of our experiments, k–NN, Naive Bayes with 1 kernel- and ANN-based classification methods are recommended.     

Wetland mapping by fusion of airborne laser scanning and multi-temporal multispectral satellite imagery

Wetlands play a major role in Europe’s biodiversity. Despite their importance, wetlands are suffering from constant degradation and loss, therefore, they require constant monitoring. This article presents an automatic method for the mapping and monitoring of wetlands based on the fused processing of laser scans and multispectral satellite imagery, with validations and evaluations performed over an area of Lake Balaton in Hungary. Markov Random Field models have already been shown to successfully integrate various image properties in several remote sensing applications. In this article, we propose the multi-layer fusion Markov Random Field model for classifying wetland areas, built into an automatic classification process that combines multi-temporal multispectral images with a wetland classification reference map derived from airborne laser scanning (ALS) data acquired in an earlier year. Using an ALS-based wetland classification map that relied on a limited amount of ground truthing proved to improve the discrimination of land-cover classes with similar spectral characteristics. Based on the produced classifications, we also present an unsupervised method to track temporal changes of wetland areas by comparing the class labellings of different time layers. During the evaluations, the classification model is validated against manually interpreted independent aerial orthoimages. The results show that the proposed fusion model performs better than solely image-based processing, producing a non-supervised/semi-supervised wetland classification accuracy of 81–93% observed over different years.     

An Injectable Hybrid Hydrogel with Oriented Short Fibers Induces Unidirectional Growth of Functional Nerve Cells

To regenerate soft aligned tissues in living organisms, low invasive biomaterials are required to create 3D microenvironments with a structural complexity to mimic the tissue's native architecture. Here, a tunable injectable hydrogel is reported, which allows precise engineering of the construct's anisotropy in situ. This material is defined as an Anisogel, representing a new type of tissue regenerative therapy. The Anisogel comprises a soft hydrogel, surrounding magneto‐responsive, cell adhesive, short fibers, which orient in situ in the direction of a low external magnetic field, before complete gelation of the matrix. The magnetic field can be removed after gelation of the biocompatible gel precursor, which fixes the aligned fibers and preserves the anisotropic structure of the Anisogel. Fibroblasts and nerve cells grow and extend unidirectionally within the Anisogels, in comparison to hydrogels without fibers or with randomly oriented fibers. The neurons inside the Anisogel show spontaneous electrical activity with calcium signals propagating along the anisotropy axis of the material. The reported system is simple and elegant and the short magneto‐responsive fibers can be produced with an effective high‐throughput method, ideal for a minimal invasive route for aligned tissue therapy.     

Continuous oxidation of benzyl alcohol in “supercritical” carbon dioxide

Supercritical (dense) carbon dioxide has been applied as solvent for the partial oxidation of benzyl alcohol with molecular oxygen in a fixed-bed reactor. High rate and good selectivity to benzaldehyde (93–97%) has been achieved with 0.5 wt% Pd/alumina or 0.5 wt% Pd/C, at around 100C and 100 bar, using only moderate excess of oxygen. The by-product benzoic acid has an autocatalytic effect on the hydration of benzaldehyde, and the subsequent oxidative dehydrogenation leads to benzoic acid, and benzyl benzoate by esterification. Promotion of Pd by Pb improves the selectivity. No catalyst deactivation or metal leaching has been observed. The method provides reasonable yields at much lower temperature than that applied in conventional gas phase oxidation, showing a potential for the synthesis of thermolabile, water-insoluble aromatic aldehydes.     

X-ray line profile analysis—An ideal tool to quantify structural parameters of nanomaterials

For a long time the shift and broadening of Bragg profiles have been used to evaluate internal stresses and coherent domain sizes, i.e. the smallest crystalline region without lattice defects. Modern technology provides both enhanced detector resolution and high brilliance x-ray sources thus allowing measurements of x-ray peaks with a high resolution in space and time. In parallel to the hardware, also diffraction theories have been substantially improved so that the shape of Bragg profiles can be quantitatively evaluated not only in terms of the crystallite size and its distribution, but also in terms of the density, type and arrangement of dislocations, twins and stacking faults. Thus state-of-the-art x-ray line profile analysis enables the thorough characterization especially of nanostructured materials which also contain lattice defects. The method can be used also to prove the existence of dislocations in crystalline materials. Examples of nanostructured metals, polymers and even molecular crystals like fullerenes are given.     

The use of the SPSA method in ECG analysis

The classification, monitoring, and compression of electrocardiogram (ECG) signals recorded of a single patient over a relatively long period of time is considered. The particular application we have in mind is high-resolution ECG analysis, such as late potential analysis, morphology changes in QRS during arrythmias, T-wave alternants, or the study of drug effects on ventricular activation. We propose to apply a modification of a classical method of cluster analysis or vector quantization. The novelty of our approach is that we use a new distortion measure to quantify the distance of two ECG cycles, and the class-distortion measure is defined using a min-max criterion. The new class-distortion-measure is much more sensitive to outliers than the usual distortion measures using average-distance. The price of this practical advantage is that computational complexity is significantly increased. The resulting nonsmooth optimization problem is solved by an adapted version of the simultaneous perturbation stochastic approximation (SPSA) method of. The main idea is to generate a smooth approximation by a randomization procedure. The viability of the method is demonstrated on both simulated and real data. An experimental comparison with the widely used correlation method is given on real data.