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 Mathematical Optimization and Applications in Biomedical Sciences
SFB Research Center 
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[an error occurred while processing this directive] OPTIM
[an error occurred while processing this directive] [an error occurred while processing this directive] FREELEVEL
[an error occurred while processing this directive] FEMBEM
[an error occurred while processing this directive] HEART
[an error occurred while processing this directive] MRI











[an error occurred while processing this directive] OPTIM
[an error occurred while processing this directive] [an error occurred while processing this directive] FREELEVEL
[an error occurred while processing this directive] FEMBEM
[an error occurred while processing this directive] HEART
[an error occurred while processing this directive] MRI














[an error occurred while processing this directive] SFB
[an error occurred while processing this directive] Interfaces
[an error occurred while processing this directive] MRI
[an error occurred while processing this directive] Numerics
[an error occurred while processing this directive] Optimization

Subproject MRI

Quantification of Functional and Biophysical Information in Magnetic Resonance Imaging

Direction: Rudolf Stollberger
 

Keywords: microenvironment properties, optimized parallel imaging, principal and independent component analysis, molecular imaging

Information: The aim of this project is the development and implementation of dedicated mathematical methods for improved quantification of biophysical and physiological parameters in magnetic resonance imaging. These parameters play a pivotal role for structural and functional analysis and will also be used as surrogate biomarkers. The extraction and quantification of functional information from MR-images is typically based on the application of sophisticated mathematical methods. In the whole field, many of the mathematical challenges refer to inverse or optimization problems. Challenges in functional MRI include further improvements in spatial and temporal resolution, inhomogeneous RF-field and coil sensitivities, physiological motion, or static field inhomogeneities induced by susceptibility variation in the body. In the short-term perspective of this proposal it is intended to improve image reconstruction with nonlinear inversion for faster imaging through parallel sampled and optimally encoded data and the integration of parameter estimation and artifact correction within the reconstruction process. Fast parallel implementation of algorithms, specific pharmacokinetic modeling, feasibility studies and application of the improved algorithms will be performed with additional personal of the institute or in cooperation with other projects. In the long-term perspective there is the vision of a completely new MR procedure based on integrated image reconstruction, parameter estimation and artifact suppression.

  Med Uni Graz    TU Graz    Uni Graz Webmaster WT / Mar 15, 2016