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Subject: Magnetic resonance imaging ×

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Now showing 1 - 3 of 3
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    Production of highly concentrated and hyperpolarized metabolites within seconds in high and low magnetic fields
    (Cambridge : RSC Publ., 2019) Korchak, Sergey; Emondts, Meike; Mamone, Salvatore; Blümich, Bernhard; Glöggler, Stefan
    Hyperpolarized metabolites are very attractive contrast agents for in vivo magnetic resonance imaging studies enabling early diagnosis of cancer, for example. Real-time production of concentrated solutions of metabolites is a desired goal that will enable new applications such as the continuous investigation of metabolic changes. To this end, we are introducing two NMR experiments that allow us to deliver high levels of polarization at high concentrations (50 mM) of an acetate precursor (55% 13C polarization) and acetate (17% 13C polarization) utilizing 83% para-state enriched hydrogen within seconds at high magnetic field (7 T). Furthermore, we have translated these experiments to a portable low-field spectrometer with a permanent magnet operating at 1 T. The presented developments pave the way for a rapid and affordable production of hyperpolarized metabolites that can be implemented in e.g. metabolomics labs and for medical diagnosis.
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    Cortical hot spots and labyrinths: Why cortical neuromodulation for episodic migraine with aura should be personalized
    (Lausanne : Frontiers Research Foundation, 2015) Dahlem, M.A.; Schmidt, B.; Bojak, I.; Boie, S.; Kneer, F.; Hadjikhani, N.; Kurths, J.
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    Local estimation of the noise level in MRI using structural adaptation
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2014) Tabelow, Karsten; Voss, Henning U.; Polzehl, Jörg
    We present a method for local estimation of the signal-dependent noise level in magnetic resonance images. The procedure uses a multi-scale approach to adaptively infer on local neighborhoods with similar data distribution. It exploits a maximum-likelihood estimator for the local noise level. The validity of the method was evaluated on repeated diffusion data of a phantom and simulated data using T1-data corrupted with artificial noise. Simulation results are compared with a recently proposed estimate. The method was applied to a high-resolution diffusion dataset to obtain improved diffusion model estimation results and to demonstrate its usefulness in methods for enhancing diffusion data.