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Highly accelerated MRI by skipped phase encoding and edge deghosting with array coil enhancement (SPEED-ACE)
1Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada. zchang@harringtoncc.org
Medical Physics
|November 9, 2006
Summary
The new SPEED-ACE MRI technique enhances speed by sparsely sampling k-space with parallel coils. This method effectively reduces ghosting artifacts, enabling faster imaging with improved image quality.
Area of Science:
- Magnetic Resonance Imaging (MRI)
- Medical Imaging Technology
- Image Reconstruction
Background:
- Fast MRI methods are crucial for reducing scan times and improving patient comfort.
- Existing parallel imaging techniques like SENSE face limitations in undersampling factors.
- Ghosting artifacts can degrade image quality in accelerated MRI.
Purpose of the Study:
- To develop and evaluate SPEED-ACE, an enhanced fast MRI method.
- To improve undersampling capabilities beyond the number of receiver coils.
- To achieve high-quality deghosted images with significant k-space acceleration.
Main Methods:
- Developed SPEED-ACE by combining skipped phase encoding with array coil enhancement.
- Utilized differential filtering to create ghosted edge maps from sparsely sampled k-space data.
- Employed least-square-error minimization with data from at least three coils for deghosting.
- Applied inverse filtering to reconstruct the final deghosted image.
Main Results:
- SPEED-ACE achieved an undersampling factor of N using N skipped phase encoding steps and parallel coils.
- Demonstrated effective reduction of overlapping aliasing ghosts through edge enhancement.
- Successfully reconstructed deghosted images from sparsely sampled k-space data.
- Showcased potential for undersampling factors greater than the number of receiver coils in vivo.
Conclusions:
- SPEED-ACE is a novel parallel imaging method offering significant acceleration in MRI.
- The technique effectively mitigates ghosting artifacts, enhancing image quality.
- SPEED-ACE surpasses current parallel imaging methods in achieving high undersampling factors.
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