Contacts
Intranet
SK
National projects
| 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. |
| 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). |
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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. |