Motion correction of magnetic resonance imaging data by using adaptive moving least squares method

Area of Science:

• Medical Imaging
• Image Processing
• Biomedical Engineering

Background:

• Motion artifacts are a significant challenge in magnetic resonance imaging (MRI), degrading image quality.
• Existing methods may require additional hardware or over-sampling, increasing complexity and cost.

Purpose of the Study:

• To develop an effective motion correction algorithm for interleaved-MR acquisition.
• To improve image quality in MRI without additional equipment or redundant over-sampling.

Main Methods:

• A post-processing approach using three-dimensional (3-D) scattered data approximation.
• Implementation of the data-adapted moving least squares (MLS) method for improved local structure matching.

Main Results:

• The proposed algorithm successfully corrects motion-induced artifacts in MRI.
• The data-adapted MLS method enhances the performance compared to classical approaches.
• Numerical results demonstrate the effectiveness and advantages of the developed algorithm.

Conclusions:

• The developed motion correction algorithm offers a valuable solution for improving MRI image quality.
• The method is efficient and does not necessitate extra equipment or over-sampling.
• This technique holds promise for enhancing diagnostic accuracy in MRI scans affected by motion.