This paper presents a novel method for removing Gibbs-ringing artifacts in MRI images. Gibbs-ringing artifacts are spurious oscillations near sharp image transitions, such as tissue boundaries, which occur due to the truncation of k-space data during MRI acquisition. The authors propose a method that re-interpolates the image based on local subvoxel shifts to sample the ringing pattern at the zero-crossings of the oscillating sinc-function, effectively reducing the artifacts with minimal smoothing. The method is based on the observation that the severity of the artifacts depends on how the sinc-function is sampled. By re-interpolating the image, the ringing can be minimized by ensuring that the sinc-function is sampled at its zero-crossings rather than its extrema. This approach is particularly effective for images with multiple edges, as it requires local correction rather than a global shift. The paper includes a detailed description of the method, including its application to both one-dimensional and two-dimensional cases. The method is compared with other non-iterative filtering methods, such as the Lanczos sigma-approximation and median filters, demonstrating its effectiveness in removing artifacts while preserving fine image details. The proposed method is also shown to be robust against parameter choices and has low computational cost, making it suitable for integration into standard image processing pipelines in clinical settings. The results from numerical phantoms and MRI images ( diffusion-weighted images and T2-weighted images) validate the effectiveness of the method in reducing Gibbs-ringing artifacts with minimal smoothing. The authors conclude that their method is a promising candidate for robust implementation in clinical MRI workflows.This paper presents a novel method for removing Gibbs-ringing artifacts in MRI images. Gibbs-ringing artifacts are spurious oscillations near sharp image transitions, such as tissue boundaries, which occur due to the truncation of k-space data during MRI acquisition. The authors propose a method that re-interpolates the image based on local subvoxel shifts to sample the ringing pattern at the zero-crossings of the oscillating sinc-function, effectively reducing the artifacts with minimal smoothing. The method is based on the observation that the severity of the artifacts depends on how the sinc-function is sampled. By re-interpolating the image, the ringing can be minimized by ensuring that the sinc-function is sampled at its zero-crossings rather than its extrema. This approach is particularly effective for images with multiple edges, as it requires local correction rather than a global shift. The paper includes a detailed description of the method, including its application to both one-dimensional and two-dimensional cases. The method is compared with other non-iterative filtering methods, such as the Lanczos sigma-approximation and median filters, demonstrating its effectiveness in removing artifacts while preserving fine image details. The proposed method is also shown to be robust against parameter choices and has low computational cost, making it suitable for integration into standard image processing pipelines in clinical settings. The results from numerical phantoms and MRI images ( diffusion-weighted images and T2-weighted images) validate the effectiveness of the method in reducing Gibbs-ringing artifacts with minimal smoothing. The authors conclude that their method is a promising candidate for robust implementation in clinical MRI workflows.