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Intranet
SK
National projects
| AgeFlex – Development and standardization of MR-based methods for detecting and evaluating metabolic and structural adaptations of aging muscles to exercise. | |
| Vývoj a štandardizácia MR metód založených na magnetickej rezonancii na detekciu a hodnotenie metabolických a štrukturálnych adaptácií starnúcich svalov na cvičenie. | |
| Program: | APVV |
| Duration: | 1.9.2025 – 31.8.2029 |
| Project leader: | Mgr. Klepochová Radka, PhD. |
| Annotation: | Aging is associated with a loss of muscle mass and the functional capacity of skeletal muscles; however, regular exercise can slow down these processes. The focus of this project is on examining the metabolic, functional, and structural parameters in the lower limb muscles, which we can non-invasively and repeatedly measure using innovative magnetic resonance methods (MR). This allows us to compare the trajectories of aging in skeletal muscles of sedentary individuals and those who are physically active. One of the key parameters that define a muscle\’s ability to efficiently mobilize and use energy for muscle work is called metabolic flexibility. The aim of the project is to develop innovative MR methods to study metabolic flexibility and structural changes in skeletal muscles during aging, and relate them to whole-body metabolic flexibility, as well as the metabolic phenotype and structural and molecular changes in the skeletal muscles of older adults. As part of the project, we will standardize the measurement of dynamic changes in metabolites in muscle during exercise using proton (1H) MR spectroscopy, create standard procedures for quality control of acquired MR spectroscopy data, and a key aspect of the project will also be the development of an automated segmentation method based on a convolutional neural network, which will enable more efficient and reliable evaluation of MR images of skeletal muscles. These innovative methods will be validated using data from ongoing longitudinal studies at the Biomedical Center of the Slovak Academy of Sciences, and their results will be directly compared with parallel changes in metabolic health, functional capacity, histological structure, and molecular mediators of metabolic flexibility in skeletal muscles. The results may not only improve our understanding of the processes that define metabolic flexibility during aging but may also offer relevant strategies to support metabolically healthy aging. |
| 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: | APVV |
| 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). |
| DUOFLUOR – Dual‑tuned ¹H/¹⁹F RF coil for preclinical MRI | |
| Duálne ladená ¹H/¹⁹F RF cievka pre predklinické MRI | |
| Program: | APVV |
| Duration: | 1.9.2026 – 31.8.2030 |
| Project leader: | Ing. Gogola Daniel, PhD. |
| Annotation: | The project focuses on the design, optimization, and experimental verification of a dual‑tuned ¹H/¹⁹F radiofrequency (RF) coil intended for preclinical MRI. Combined ¹H/¹⁹F imaging represents a promising technology enabling simultaneousanatomical (¹H) and quantitative functional measurements (¹⁹F), particularly in studies involving the biodistribution offluorinated compounds, cell‑tracking applications, inflammatory processes, and functional lung imaging. The absence of endogenous ¹⁹F signal in biological tissues allows absolute quantification without background reconstruction, which increases the accuracy and interpretability of measurements. Despite its potential, only a limited number of solutions optimized for small‑animal imaging currently exist, and available systems often do not achieve the required sensitivity and B₁ homogeneity for fluorine MRI.The project includes the development of detailed FEM/FDTD models of various coil geometries, their optimization for bothresonance frequencies, and the subsequent construction of a physical prototype. The experimental phase will involveS‑parameter and Q‑factor measurements, B₁ field mapping, and testing of tuning stability. The functional performance ofthe coil will be evaluated using phantoms with defined fluorine content and, in the final stage, through preclinical measurements in small animals. The project also includes the development of a software tool that enables the calculationand optimization of RF coil parameters for different dimensions and frequencies.The outcome of the project will be an experimentally validated dual‑tuned ¹H/¹⁹F RF coil and a complete methodology forits design, applicable in preclinical research, pharmacological studies, and the development of fluorinated markers. Theproject will contribute to the advancement of preclinical MRI technologies in Slovakia and create a foundation for furtherinterdisciplinary applications in biomedical imaging. |
| TRACE-DC – Non-invasive measurement and metrological traceability of DC component in modern networks with battery storage | |
| Neinvazívne meranie a metrologická sledovateľnosť jednosmernej zložky v moderných sieťach s batériovými úložiskami | |
| Program: | APVV |
| Duration: | 1.9.2026 – 28.2.2029 |
| Project leader: | Ing. Gogola Daniel, PhD. |
| Annotation: | In the context of Slovakia\’s energy transformation, the share of renewable energy sources and battery storage systems integrated into the electricity grid is increasing. These devices operate with direct current (DC) but connect to alternating current (AC) networks, requiring metrologically reliable on-site measurement of DC power and energy. The first objective is to test and characterize a non-invasive DC sensor for measuring high currents (up to 1200 A) with 0.1% accuracy, suitable for battery storage, photovoltaic systems, and electric mobility. The second objective is to establish a reference standard for DC power and energy traceable to national standards. Additionally, connecting DC sources without transformers injectsDC components into AC networks, causing overheating, increased losses, and reduced power quality. The project therefore investigates the impact of DC components on AC electricity meters, with results serving as a basis for updating legislative requirements. The project supports the development of smart energy grids and contributes to effective electricity flow management in Slovakia\’s economy. |
| FERINO – Advanced diagnostics of neurodegenerative disorders using magnetic resonance techniques and artificial intelligence | |
| Pokročilá diagnostika neurodegeneratívnych ochorení pomocou techník magnetickej rezonancie a umelej inteligencie | |
| Program: | APVV |
| Duration: | 1.7.2023 – 30.6.2027 |
| Project leader: | Ing. Gogola Daniel, PhD. |
| Annotation: | Neurodegenerative diseases (ND) are becoming a severe problem in developed countries. Since we currently haveno effective therapies available, early diagnosis is critical to ensure a good quality of life for ND patients. ND arecharacterized by iron accumulation and magnetite mineralization in brain tissue, with ferritin as a precursor. Due toits low relaxivity, physiological ferritin is at the edge of visibility using magnetic resonance imaging (MRI)techniques. On the contrary, "pathological" ferritin causes a significant shortening of MRI relaxation times. Thiscreates hypointense artifacts, which theoretically allow the distinguishability of both proteins. Since ironaccumulation precedes the clinical symptoms of the disease, MRI has the potential to become a non -invasivediagnostic method for the early stages of ND. At present, however, this is limited by the insufficient characterizationof the relaxation properties of biogenic iron and the uncertainty in the interpretation of clinical data. Therefore, ourbasic goal (application output) is the development of a comprehensive methodology (FERINO software tool) for theunequivocal diagnosis of the early stages of ND. To reach our goal, we will use a combination of several diagnostictechniques and artificial intelligence tools. The diagnostic techniques include in-vitro, in-silico, and in-vivocharacteristics of ferritin relaxation, structural MRI, magnetic resonance spectroscopy (MRS), neurological tests,and clinical biochemistry biomarkers. The cornerstone of the methodology will be the FerroQuant software tool,which was proposed by the principal investigator within the APVV 2012. It enables the analysis and quantificationof iron-related clinical MRI data but lacks new findings in iron MRI (false-positive artifacts, ferritin\’s mineral phases).FerroQuant also does not use artificial intelligence and does not combine different diagnostic data, whic h, however,will be an integral part of the FERINO tool. |
| QuantMR – Optimization and Standardization of Quantitative Magnetic Resonance Imaging Methods. Suppression of Metallic Artifacts on low-field MR Scanners | |
| Optimalizácia a štandardizácia kvantitatívnych metód zobrazovania magnetickou rezonanciou. Potlačenie kovových artefaktov na nízkopolových MR skeneroch | |
| Program: | Plán obnovy EÚ |
| Duration: | 1.9.2024 – 31.8.2026 |
| Project leader: | Ing. Gogola Daniel, PhD. |
| The application of Artificial Intelligence methods for improved Magnetic Resonance Imaging | |
| Použitie metód umelej inteligencie na zlepšenie zobrazovania pomocou magnetickej rezonancie | |
| Program: | VEGA |
| Duration: | 1.1.2026 – 31.12.2028 |
| Project leader: | RNDr. Krafčík Andrej, PhD. |
| Annotation: | Magnetic Resonance (MR) is a widely used, useful diagnostic tool. However, since the measured signal isinfluenced by many factors (e.g., by the amount of biogenic contrast agents), quantitative analysis is difficult andlengthy. Therefore, the proposed project aims to model the influence of biogenic nanoparticles of ferritin on MRsignal and to use artificial intelligence for automated analysis (identification, segmentation and volumetry) ofstructures in MR images of joint, muscles and the heart. Advanced deep learning methods will be used for thesetasks. In addition, the project will focus on design and implementation of novel acquisition and calibrationsequences and protocols for metabolic and structural MR imaging. The project will also analyse the physiologicalresponse of MR measurements on cardiovascular system through wearable optical sensors. |
| ReAcMap – Assessment of restitution of normal ventricular activation by ECG mapping | |
| Vyhodnotenie reštitúcie normálnej komorovej aktivácie pomocou EKG mapovania | |
| Program: | APVV |
| Duration: | 1.9.2025 – 31.8.2028 |
| Project leader: | Ing. Švehlíková Jana, PhD. |
| Annotation: | The project intends to optimize and personalize cardiac resynchronization therapy (CRT) for patients with heart failure. This effective, nonpharmacological, pacing-based treatment aims to restore interventricular resynchronization of ventricular activation by pacing both ventricles with an expected subsequent increase in cardiac output. However, about 30-40% of the patients do not benefit from the therapy and are designed as “non-responders”. To improve the efficacy of ventricular resynchronization, conduction system pacing (CSP) was recently introduced into clinical practice, which replaces biventricular stimulation with direct stimulation of the conduction system. However, CSP to achieve a narrow QRS complex is not feasible in up to 15% of patients for multiple anatomical, pathological, and technical reasons. Therefore, an optimal individualized strategy to achieve effective ventricular resynchronization is an unmet need in electrical therapies in heart failure patients. The proposed research project is methodologically based on noninvasive body surface potential ECG measurements of patients with heart failure indicated for a CRT/CSP device implantation. From the measured data, conducted using a dedicated in-house measuring device, the new parameters for the evaluation of the dynamics of the ventricular activation will be derived to set the proper programming stimulation of the device. A possible reduction of the number of ECG electrodes from the currently used 128 will also be studied to facilitate the routine clinical feasibility of the recording system. The simulations of the failing heart will be performed to understand better the processes that are undergoing in the ventricles. The area of the starting spontaneous ventricular activity will be assessed by solving the inverse problem of electrocardiography using a personalized heart-torso model obtained from the CT scan. The dedicated measuring system will implement a GUI to apply the suggested methods easily. |
| Project website: | https://www.um.sav.sk/reacmap/ |
| OrgPipeSK2025 – Research of the metal organ pipe collections of historical pipe organs in Slovakia | |
| Výskum kovového píšťalového fondu historických organov na Slovensku | |
| Program: | APVV |
| Duration: | 1.9.2025 – 31.8.2028 |
| Project leader: | RNDr. Krafčík Andrej, PhD. |
| Annotation: | The sound-stylistic quality of historical organs is determined by various factors, including the material used for the organ pipes and the scaling (mensuration) of individual pipes and entire stops. The proposed project will examine organ metal as a key sound-stylistic determinant of historical organs, with consideration of the constructional evolution of organs in Slovakia from the 17th to the 20th century. The project will be conducted in four phases. The first phase will focus on the chemical composition analysis of organ pipe metal from selected instruments. These pipes and entire stops will undergo mensuration analysis, leading to the development of a mathematical model. The next phase will involve the visual recording (collection) of signings—etched or stamped markings indicating the specific tone for which a pipe is constructed. The signings of pipes from instruments with known builders will be documented to create a standard that will enable the use of neural networks (AI) to identify the authorship of organs whose builders are currently unknown. The research will also address the technical condition of organ metal, particularly corrosion, which affects not only the sound properties but also the preservation of the metal components of these historical instruments. The project\’s outcomes will include an online map of organ metal composition, corrosion, mensurations, an atlas of various types of corrosion and defects in organ pipes, as well as a comprehensive mapping of the metal and mensurations of studied stops. Furthermore, we will establish a method for gradually authorizing organs whose builders remain unidentified. All findings will be contextualized within the sound-stylistic development of historical organ building on Slovak territory. |
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| Štipendiá pre excelentných PhD. študentov a študentky R1 | |
| Program: | Plán obnovy EÚ |
| Duration: | 1.9.2023 – 31.8.2026 |
| Project leader: | Ing. Pajanová Iveta |
| Annotation: | PhD Topic: Application of deep-learning algorithms on automated MRI data processing. Annotation: Automated identification and segmentation of clinical data, obtained primary by MRI, is very desirable. The reason is typically large size of data and therefore enormous time, which radiologist has to invest into the manual segmentation. Availability of powerful hardware open new capabilities to automate this processes and speedup via deep learning techniques using convolutional neural networks (CNN). Therefore, student will learn the fundamental functionality principles of MRI device (theoretically and practically), try manual segmentation of volumetric MRI data, and theoretically and practically learn principles of CNN. Student will design own architecture of CNN for automated segmentation of volumetric data, further train, validate and implement on testing data.The output of this dissertation should be a CNN capable of deployment in clinical practice, in the diagnosis and quantitative analysis of selected tissues (cartilage, ligaments, tendons, menisci, subcutaneous fat, etc.). It is theoretical work, in which programming basics and knowledge of some programming language are necessary. As the programming environment, for design and implementation of CNN, will be used Python with module TensorFlow. |