Advanced Signal-Processing for Ultra-Fast Magnetic Resonance
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Marie Curie Fellows :: Partner 2 - Manchester, UK

Marie-Curie Fellow
Research & Training Network
E. Vescovo (ESR)


Marie Curie Fellow (ESR) of the FAST project
The University of Manchester, UK
Contact
 

Involved in the following tasks

  • T2 Acquisition
    Task 2 exploits the strong synergy between signal processing and MR methodology to reduce the MRSI acquisition-time. It shall use the latest technical developments and (confidential) expertise of our Industrial partners Philips and Siemens concerning high-speed gradients, coil arrays, novel pulse sequence designs for extending the limits of spatial resolution, temporal resolution, and SNR. T2 and T4 also investigate and develop advanced, innovative ways of acquiring MRSI data, at the forefront of MR-methodology. This is a highly challenging task exploring uncharted paths. Data of patients with brain, prostate and breast cancer, diabetes, etc., and of healthy controls will be acquired, co-registered and evaluated with MRI. High-Resolution Magic-Angle Spinning (HRMAS) NMR spectroscopy will provide additional chemical analysis of targeted tissues. Ideally, the feasibility of new concepts can be tested with the virtual scanner of task T3. Ultimately, T2 leads to ultra-fast MRSI. Philips and Siemens play an important role in this task.

  • T6 Applications
    Task 6 judges and criticizes/approves FAST's innovations in preclinical and clinical settings. T6 also improves knowledge about diseases and metabolomics, gathered through MRSI. In essence, metabolomics pertains to identification of as many metabolites as possible and to understand each in its own biochemical context. MRSI can measure metabolite concentrations, chemical shifts, pH, etc. non-invasively and therefore has a huge potential for this task. Through time, much more emphasis will be placed on diagnosing and treating symptoms before secondary symptoms occur -- prevention rather than the cure of a (potential) disease. The role of Industrial partner Sanofi-Aventis is important in this task.

Research focus

The aim of my project is to investigate and develop a reliable method to obtain brain temperature maps using in vivo Magnetic resonance spectroscopy imaging. This noninvasive approach to measure  the internal brain temperature relies on the linear relationship between the proton MR resonance frequency of tissue water and the tissue's temperature. The absolute temperature in the brain is obtained by measuring the chemical shift of the water protons relative to a reference compound such as N-Acetylaspartate (NAA). To convert the frequency difference between these two signals into temperature, it is necessary to apply a particular calibration curve. Such a curve is estimated empirically, many studies deal with this estimation achieving different values of accuracy. The final scope of this mapping project should be the opportunity to add such a map as a routine tool in clinic and during surgery; this should provide useful information for many thermal therapies such as hypo/hyperthermia and it could give us useful diagnostic information. For example, it is known that patients with acute ischemic stroke present a higher temperature in the region of the brain that appears abnormal, compared with the normal-appearing. Knowing the temperature distribution in the brain could help to locate the stroke.Moreover, the ability to monitor the brain's temperature could provide a tool to better understand how the brain works, for example investigating the relationship between blood flow and brain temperature.

 

Collaboration

Plug in for jMRUI-Temperatura estimation
with Partner 1 (F. Di Cesari, Lyon
Providing a Plugin able to estimate temperature starting from CSI spectra coming from the brain

Publications

Quantitation of magnetic resonance spectroscopy signals: the jMRUI software package. D.Stefan, F. Di Cesare, A. Andrasescu, E. Popa, A. Lazariev, E. Vescovo, O. Strbak, S. Williams, Z. Starcuk, M. Cabanas, D. van Ormondt and D Graveron-Demilly. Meas. Sci. Technol. 20 (2009)

 
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