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Image reconstruction by regularized nonlinear inversion--joint estimation of coil sensitivities and image content
Martin Uecker1, Thorsten Hohage, Kai Tobias Block
1Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany. muecker@gwdg.de
Magnetic Resonance in Medicine
|August 7, 2008
Summary
New nonlinear MRI reconstruction methods reduce image artifacts in parallel imaging. This technique improves image quality, especially at high acceleration factors, by better estimating coil sensitivity profiles.
Area of Science:
- Medical Imaging
- Magnetic Resonance Imaging
- Image Reconstruction
Background:
- Parallel imaging in MRI accelerates scans but can cause image degradation at high acceleration factors.
- Existing methods like GRAPPA and SENSE extend MRI reconstruction but are sensitive to artifacts from undersampling.
Purpose of the Study:
- To introduce and demonstrate a novel nonlinear inversion algorithm for improved autocalibrating parallel MRI.
- To address image artifacts and enhance performance in MRI scans with high acceleration factors.
Main Methods:
- Developed a Newton-type method with regularization for nonlinear image reconstruction.
- Applied the algorithm to autocalibrating parallel MRI, focusing on improved coil sensitivity profile estimation.
Main Results:
- The nonlinear inversion algorithm significantly reduced image artifacts.
- Performance improvements were observed particularly at high acceleration factors and with fewer reference lines.
- Enhanced estimation of coil sensitivity profiles contributed to artifact reduction.
Conclusions:
- Nonlinear inversion represents a significant advancement over existing linear methods for parallel MRI reconstruction.
- This approach offers a promising solution for obtaining high-quality MRI images with reduced artifacts, even under challenging undersampling conditions.
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