Projects

Andrej Dvurečenskij

Project selection:

National projects

VAC-YAG – Research on the influence of hydrodynamic flows on the distribution of oxygen vacancies in Yttrium-Aluminum garnet single crystal grown via Horizontally Directed Crystallization for detectors
Výskum vplyvu hydrodynamických prúdov na rozloženie kyslíkových vakancií v monokryštáloch ytriovo-hlinitého granátu vypestovaných horizontálne riadenou kryštalizáciou pre detektory
Program: SRDA
Duration: 1.9.2026 – 31.8.2029
Project leader: Ing. Majerová Melinda, PhD.
Annotation: The project is focused on the research of oxygen vacancies in single crystals of Yttrium aluminium garnet. During the growing process, these oxygen vacancies are distributed unevenly in the volume of the crystal, which is affected by hydrodynamic flows in the melt. Since the melting temperature for YAG is higher than 1950°C, a physical model will be built for the research. On the basis of these data, an experimental thermal unit will be assembled with the possibility of controlling the thermal field, which will enable the control of convection currents in the melt. Crystals will be grown in thisthermal unit and will be characterised. The result of the project will be data and correlation of the influence of hydrodynamic flows on the distribution of oxygen vacancies in single crystal of Yttrium aluminium garnet, which has a high potential for use in detectors.
MRCartilage – Automatic data evaluation tool from the longitudinal quantitative MRI studies of articular cartilage
Automatický softvérový nástroj na výhodnocovanie kvantitatívnych MRI štúdií artikulárných chrupaviek v čase
Program: SRDA
Duration: 1.7.2022 – 30.6.2026
Project leader: Ing. Dr. Szomolányi Pavol, (PhD.)
Annotation: The aim of the project is to design a comprehensive tool for automatic evaluation of human articular cartilage data from quantitative MRI. Data obtained from the Osteoarthritis Initiative database, and measured at Institute of Measurement Science and Medical University of Vienna will be segmented using an automated segmentation tool based on convolutional neural networks. The annotated data will then be registered on quantitative MRI data that will be available from the database (T2 and T1rho mapping, gagCEST, sodium MR) using automated or semiautomated tools developed within this project. The data obtained will be evaluated at multiple time points according to MR measurements that will be available. In addition to quantitative MR data, this will include volumetric data, cartilage thickness, and texture analysis of quantitative maps. Patient evaluation will be based on risk factor groups (transverse ligament rupture, meniscus rupture and menisectomy). The expected number of patients is approximately 4000 divided into individual groups in the ratio 40/30/30. The output of the project will be a compiled version of an automatic cartilage evaluation tool that will be available in a public source (such as website of Institute of Measurement).