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Published on: January 4, 2013
Minimizing macrovessel signal in cerebral perfusion imaging using independent component analysis
G Reishofer1, F Fazekas, S Keeling
1Department of Radiology, Medical University Graz, Graz, Austria.
Magnetic Resonance in Medicine
|January 30, 2007
Summary
This study introduces a postprocessing method using principal component analysis (PCA) and independent component analysis (ICA) to reduce macrovessel signal in dynamic MRI. The technique effectively minimizes artifacts, improving the detection of microvascular perfusion changes.
Area of Science:
- Medical Imaging
- Neuroimaging
- Biophysics
Background:
- Macrovessel susceptibility effects in MRI bolus-tracking cause artificial high perfusion signals.
- These artifacts obscure true perfusion changes and elevate parameters in adjacent tissues.
Purpose of the Study:
- To explore postprocessing techniques for mitigating macrovessel signal influence in dynamic MRI.
- To improve the accuracy of perfusion parameter imaging.
Main Methods:
- Applied principal component analysis (PCA) for data reduction.
- Utilized independent component analysis (ICA) to separate signal components.
- Reconstructed dynamic time series minimizing macrovessel and noise contributions.
- Investigated temporal resolution and signal-to-noise ratio (SNR) effects via simulation.
Main Results:
- Macrovessel signal influence reduced by at least 50% in vivo.
- Gray matter (GM) and white matter (WM) tissue parameters remained largely unaffected.
- Corrected images showed reduced macrovessel signal and enhanced microvascular perfusion perceptibility.
Conclusions:
- The developed postprocessing method effectively reduces macrovessel signal artifacts in dynamic MRI.
- This technique improves the visualization and detection of microvascular changes in perfusion imaging.
