NDE of Polymer Composites Using Magnetic Resonance Techniques

Polymer based materials have become increasingly important in structural applications primarily due to their high strength to weight ratio. As the use of polymer-based composites has increased, so has the need for reliable non-destructive evaluation techniques. In this paper, a new NDE method for these materials is proposed. The technique relies on the observation of an electron paramagnetic resonance (epr) absorption at the site of damage in a polymer. Using applied magnetic field gradients the physical location of damage can be discerned and an image of the damage site can be obtained. This should allow the detection of cracks and delaminations with high resolution, good sensitivity and good contrast

Увеличение темпов прироста запасов углеводородов с помощью инновационных технологий на примере Омской области

Проведен анализ перспектив нефтегазоносности Омской области на основе данных инновационной технологии квантово-оптической фильтрации космоснимков. Приведены физические принципы технологии квантово-оптической фильтрации. На примере Омской области показана эффективность применения технологии квантово-оптической фильтрации при решении задачи повышения темпов прироста запасов углеводородного сырья

The State of the Art of Medical Imaging Technology: from Creation to Archive and Back

Medical imaging has learnt itself well into modern medicine and revolutionized medical industry in the last 30 years. Stemming from the discovery of X-ray by Nobel laureate Wilhelm Roentgen, radiology was born, leading to the creation of large quantities of digital images as opposed to film-based medium. While this rich supply of images provides immeasurable information that would otherwise not be possible to obtain, medical images pose great challenges in archiving them safe from corrupted, lost and misuse, retrievable from databases of huge sizes with varying forms of metadata, and reusable when new tools for data mining and new media for data storing become available. This paper provides a summative account on the creation of medical imaging tomography, the development of image archiving systems and the innovation from the existing acquired image data pools. The focus of this paper is on content-based image retrieval (CBIR), in particular, for 3D images, which is exemplified by our developed online e-learning system, MIRAGE, home to a repository of medical images with variety of domains and different dimensions. In terms of novelties, the facilities of CBIR for 3D images coupled with image annotation in a fully automatic fashion have been developed and implemented in the system, resonating with future versatile, flexible and sustainable medical image databases that can reap new innovations

Perfluorocarbon Particle Size Influences Magnetic Resonance Signal and Immunological Properties of Dendritic Cells

The development of cellular tracking by fluorine (19F) magnetic resonance imaging (MRI) has introduced a number of advantages for following immune cell therapies in vivo. These include improved signal selectivity and a possibility to correlate cells labeled with fluorine-rich particles with conventional anatomic proton (1H) imaging. While the optimization of the cellular labeling method is clearly important, the impact of labeling on cellular dynamics should be kept in mind. We show by 19F MR spectroscopy (MRS) that the efficiency in labeling cells of the murine immune system (dendritic cells) by perfluoro-15-crown-5-ether (PFCE) particles increases with increasing particle size (560>365>245>130 nm). Dendritic cells (DC) are professional antigen presenting cells and with respect to impact of PFCE particles on DC function, we observed that markers of maturation for these cells (CD80, CD86) were also significantly elevated following labeling with larger PFCE particles (560 nm). When labeled with these larger particles that also gave an optimal signal in MRS, DC presented whole antigen more robustly to CD8+ T cells than control cells. Our data suggest that increasing particle size is one important feature for optimizing cell labeling by PFCE particles, but may also present possible pitfalls such as alteration of the immunological status of these cells. Therefore depending on the clinical scenario in which the 19F-labeled cellular vaccines will be applied (cancer, autoimmune disease, transplantation), it will be interesting to monitor the fate of these cells in vivo in the relevant preclinical mouse models

Biocompatible Polyhydroxyethylaspartamide-based Micelles with Gadolinium for MRI Contrast Agents

Biocompatible poly-[N-(2-hydroxyethyl)-d,l-aspartamide]-methoxypoly(ethyleneglycol)-hexadecylamine (PHEA-mPEG-C16) conjugated with 1,4,7,10-tetraazacyclododecan-1,4,7,10-tetraacetic acid-gadolinium (DOTA-Gd) via ethylenediamine (ED) was synthesized as a magnetic resonance imaging (MRI) contrast agent. Amphiphilic PHEA-mPEG-C16-ED-DOTA-Gd forms micelle in aqueous solution. All the synthesized materials were characterized by proton nuclear magnetic resonance (1H NMR). Micelle size and shape were examined by dynamic light scattering (DLS) and atomic force microscopy (AFM). Micelles with PHEA-mPEG-C16-ED-DOTA-Gd showed higher relaxivities than the commercially available gadolinium contrast agent. Moreover, the signal intensity of a rabbit liver was effectively increased after intravenous injection of PHEA-mPEG-C16-ED-DOTA-Gd

What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non- invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi- dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging