TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced...

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Image Reconstruction

Background:

• Patient motion during Magnetic Resonance Imaging (MRI) is a significant source of image artifacts.
• Existing motion correction methods often struggle with accuracy or computational efficiency.
• Developing robust retrospective motion correction is crucial for diagnostic MRI.

Purpose of the Study:

• To introduce and evaluate TArgeted Motion Estimation and Reduction (TAMER), a novel data consistency-based retrospective motion correction technique for MRI.
• To demonstrate TAMER's effectiveness in jointly estimating motion-free images and motion trajectories.
• To compare TAMER's performance against existing methods in simulations and phantom experiments.

Main Methods:

• TAMER employs a data consistency-based approach, minimizing SENSE forward model errors with rigid-body motion.
• A reduced modeling strategy in parallel imaging accelerates computation by assessing a subset of voxels.
• Joint estimation of motion-free images and motion parameters captures the coupling between image data and motion.

Main Results:

• Simulations showed TAMER achieved 22% error in search directions, significantly outperforming alternating methods (73% error).
• Phantom experiments demonstrated TAMER reduced Root Mean Square Error (RMSE) from 21% to 14% (translation) and 17% to 10% (rotation).
• Qualitative improvements were observed in human imaging, with in vivo results comparable to state-of-the-art methods.

Conclusions:

• TAMER offers an efficient and accurate retrospective motion correction solution for MRI.
• The method effectively mitigates translation and rotation artifacts, enhancing diagnostic image quality.
• TAMER represents a promising advancement in MRI motion correction technology.