Automated filter selection for suppression of Gibbs ringing artefacts in MRI

Area of Science:

• Medical Imaging
• Signal Processing

Background:

• Gibbs ringing in Magnetic Resonance Imaging (MRI) creates artifacts that can mislead clinical interpretation.
• Current methods for suppressing Gibbs ringing involve reconstruction algorithms or post-processing, but their evaluation is often subjective or difficult to automate.
• Existing comparisons of novel algorithms frequently use arbitrary or sub-optimal filtered Fourier reconstruction parameters.

Purpose of the Study:

• To evaluate the suitability of twelve existing metrics for assessing Gibbs ringing suppression.
• To propose and validate a novel metric for evaluating Gibbs ringing suppression in MRI.
• To optimize and compare smoothing filters for Gibbs artifact reduction using the new metric.

Main Methods:

• Evaluation of twelve established metrics for Gibbs ringing suppression.
• Development and validation of a novel quantitative metric using 1D and 2D simulations.
• Optimization and comparison of seventeen smoothing filters for Gibbs artifact suppression.

Main Results:

• Demonstration that none of the twelve evaluated metrics are fit for purpose.
• Validation of the proposed novel metric's efficacy in simulations.
• Identification of optimized filters for Gibbs artifact suppression, revealing counter-intuitive performance characteristics.

Conclusions:

• The proposed novel metric simplifies and enhances the comparison of MRI reconstruction and post-processing algorithms.
• The findings facilitate the automation of ringing suppression in MRI.
• The developed methodology is applicable to other Fourier domain data acquisition applications.