Suppression of MRI truncation artifacts using total variation constrained data extrapolation.

Area of Science:

• Medical Imaging
• Signal Processing
• Computational Science

Background:

• Finite k-space sampling in MRI leads to Gibbs ringing artifacts, particularly at low resolutions.
• Existing methods to reduce these artifacts often cause image blurring.

Purpose of the Study:

• To develop a method for reducing Gibbs ringing artifacts in MRI without compromising image resolution.
• To exploit the assumption of a piecewise-constant object for k-space data extrapolation.

Main Methods:

• Extrapolating k-space data beyond the measured region based on a piecewise-constant object assumption.
• Formulating the problem as a total variation minimization solvable by nonlinear optimization.
• Implementing a modified approach for edge-preserving denoising in noisy data.

Main Results:

• Significant reduction of MRI truncation artifacts demonstrated.
• Preservation of image resolution achieved, unlike traditional filtering methods.
• Effectiveness validated through simulations and experimental data (phantom and in vivo human brain).

Conclusions:

• The proposed method effectively reduces Gibbs ringing artifacts in MRI.
• This approach offers a resolution-preserving alternative to conventional artifact reduction techniques.
• The method is robust, handling both artifact reduction and denoising in MRI data.