Modified iterative model based on data extrapolation method to reduce Gibbs ringing

Abstract

An iterative algorithm that involves image filtering and data replacement (as suggested by Constable and Henkelman) is investigated for reducing the Gibbs artifact in magnetic resonance imaging. The image is processed with an edge-preserving filter to estimate the height and location of a set of model elements (delta functions or boxes) for generating the missing high-frequency information. Filtering was performed in the complex image domain to account for discontinuities in phase as well as magnitude. The process is repeated after each data replacement to handle varying degrees of contrast. The convergence and signal-to-noise characteristics of the algorithm are investigated by means of simulated and clinical examples. The results indicate that the algorithm performs reasonably well in reducing ringing artifacts due to nearby edge contrast seen in most of the homogeneous, isointense regions. Nevertheless, it may generate some spurious thickening of structures that do not match the assumed step-edge models.

Accurate determination of spin‐density and T1 in the presence of RF‐field inhomogeneities and flip‐angle miscalibration

“…3, it is seen that a small degree of systematic error occurs in B(r) due to Gibbs ringing. This can be removed by applying a sigma filter with a noise-smoothing threshold containing an additional term to eliminate Gibbs ringing (24). It must also be noted that Gibbs ringing, which is inherent to the Fourier transform image-reconstruction method used here, can lead to an artificial broadening of distributions as it causes a breakdown of the single-compartment p,, TI signal model at tissue boundaries.…”

Nonuniform Phase-Encode Distributions for MRI Scan Time Reduction

“…Uniform truncated k y distribu-of edge intensities inside the object (2,13,14). Measured tions, on the other hand, require extrapolation (5)(6)(7)(8). Since data (as opposed to missing data) are treated as best possible interpolation is more reliable than extrapolation, the image estimates and therefore remain unaltered.…”

Compression of CT sinogram data by decimation in the view direction

“…If this is the case, the edge in the spectrum can show up in the reconstructed image as Gibbs ringing artifacts (ripples at sharp edges due to the shape of the sinc kernel), which is generally not acceptable. The Gibbs ringing artifacts are common in magnetic resonance imaging (MRI) and there are many available methods to remove the artifacts if they occur . One way of removing the edge in the spectrum is to use a filter that has a frequency response that gradually drops down toward the Nyquist frequency.…”