SAS
HRS4R
Iveta Pajanová
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: | SRDA |
| 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. |
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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. |