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Augmented generalized SENSE reconstruction to correct for rigid body motion
Roland Bammer1, Murat Aksoy, Chunlei Liu
1Lucas Center, Department of Radiology, Stanford University, Stanford, California 94305-5488, USA. rbammer@stanford.edu
Magnetic Resonance in Medicine
|December 28, 2006
Summary
Motion artifacts in MRI are a major challenge, especially for uncooperative patients. This study introduces a new parallel imaging method that reduces k-space inconsistencies, improving MRI image quality for better diagnostics.
Area of Science:
- Medical Imaging
- Magnetic Resonance Imaging (MRI)
- Image Reconstruction
Background:
- Motion artifacts significantly degrade MRI image quality, particularly in pediatric or non-cooperative patients.
- Conventional motion correction techniques often introduce k-space inconsistencies, leading to persistent artifacts.
- Accurate motion artifact correction is crucial for reliable MRI diagnostics.
Purpose of the Study:
- To introduce a novel formalism for parallel imaging in the presence of patient motion.
- To improve MRI image quality by diminishing k-space inconsistencies caused by motion.
- To develop a robust method for motion artifact correction in MRI scans.
Main Methods:
- An augmented iterative SENSE reconstruction synthesizes missing k-space data.
- Motion is detected using low-resolution navigator images and an automatic registration routine.
- The method leverages the complementary encoding of individual receiver coils to reduce artifacts.
Main Results:
- The proposed method effectively diminishes k-space inconsistencies.
- Improved overall image quality is achieved compared to conventional spiral scans.
- Demonstrated efficacy in interleaved spiral MRI scans from volunteers.
Conclusions:
- The developed parallel imaging formalism offers a significant advancement in correcting motion artifacts in MRI.
- This technique enhances image quality by exploiting receiver coil data and iterative reconstruction.
- The method holds promise for improving diagnostic accuracy in challenging patient populations.
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