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Projekt Druckansicht

Fluor MR Technologien zur Untersuchung zellulärer Therapien in vivo

Antragstellerinnen / Antragsteller Dr. Andreas Pohlmann; Dr. Sonia Waiczies
Fachliche Zuordnung Medizinische Physik, Biomedizinische Technik
Förderung Förderung von 2010 bis 2022
Projektkennung Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 170047119
 
Erstellungsjahr 2022

Zusammenfassung der Projektergebnisse

Fluorine (19F) MRI is an exceptional tool to detect, quantify and study the distribution of fluorinated compounds with absolute specificity. Typically, 19F MRI is used to study inflammation and cell migration in experimental models. Nanoparticles rich in perfluorocarbons are administered systemically into animal models to label inflammatory cells in vivo. Apart from perfluorocarbons, other organofluorine compounds could be present in pharmaceutical and household products. Currently there are no routine methods that non-invasively quantify levels of compounds or therapies after their introduction into humans. A non-invasive method utilizing 19F MRI to study 19F levels in vivo would be invaluable to study the distribution of fluorinated therapies as well as unwanted contaminants. However, 19F MRI is challenged by low signal-to-noise ratio (SNR). The project strategy was to overcome this limitation by developing upon novel magnetic resonance strategies that boost 19F signal detection in vivo and to proceed in monitoring low levels of levels of fluorinated compounds in vivo. Our approach involved implementation of signal-efficient MR pulse sequences, MR protocols tailored to the relaxation constants of each 19F product and application of novel acceleration techniques, compressed sensing. A novel aspect was the implementation of a first ultra-low-noise (cryogenically-cooled) 19F MR mouse head quadrature RF coil. This MR-based RF coil technology physically increased signal sensitivity and thereby lowers the detection limit in 19F MR experiments. Outlook: While we have uncovered new insights in the 19F MR method to detect 19F signals in vivo, the next challenge will be to transfer this knowledge into the clinical scenario. For this, we will make use of machine learning approaches to build upon the methods that we have developed in this project for acquisition, reconstruction, and quantification of the data in humans.

Projektbezogene Publikationen (Auswahl)

 
 

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