Gibbs Artifacts Research Articles

Parallel acquisition for effective density weighted imaging: PLANED imaging

Density weighted phase-encoding has proven to be a highly efficient method for k-space sampling as it improves the localization properties and increases the signal-to-noise ratio for extended samples at the same time. But either density weighted imaging lengthens the minimum scan time or, if the Nyquist criterion is violated in parts of the sampled k-space, undersampling artefacts occur. Purpose of this work was to combine density weighted imaging and parallel imaging techniques to improve the spatial response function and consequently the signal-to-noise ratio without spoiling image quality by undersampling artefacts. Images were acquired with parallel acquisition for effective density weighted imaging (PLANED imaging) and compared to results sampled with conventional Cartesian phase-encoding with the same spatial resolution and the same number of excitations. Both in vivo and phantom measurements recorded with the PLANED method revealed a considerable enhancement of the signal-to-noise ratio and a remarkable reduction of Gibbs artefacts compared to standard Cartesian imaging. It has been demonstrated that PLANED improves image quality by suppressing truncation artefacts and increasing the SNR without lengthening the measurement time.

Sensitivity enhancement and reduction of Gibbs artifact in T2‐ weighted imaging using variable tip angle excitation

Introduction of a Gaussian weighting function into the phase encoding of the spin-echo imaging method via variable tip angle excitation, with concomitant optimization of the recycle delay, is shown to increase the sensitivity of T2-weighted imaging by up to 40%. In addition, the Gibbs artifact associated with sharp boundaries is reduced considerably without the need for postprocessing of the data.

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

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.

Image reconstruction using POCS, model constraints, and linear prediction theory for the removal of phase, motion, and Gibbs artifacts in magnetic resonance and ultrasound imaging

The Fourier transform is the standard image reconstruction technique used in magnetic resonance imaging (MRI), and it is an integral part of the inverse scattering formalism in ultrasound (US) imaging. Unfortunately, artifacts such as Gibbs ringing induced by a finite sampling window or systematic errors in phase may significantly impede the interpretation of the resulting Fourier transform images. Further, when only a few parameters are needed to characterize the object function, it is likely not to be the best technique for optimal signal-to-noise. In this paper, the application of a parameter estimation reconstruction scheme using a priori constraints to remove Gibbs ringing and improve resolution and signalto- noise is presented for MRI and US. Projection onto convex set theory is also used to regenerate uncollected data in partial Fourier imaging, and model constraints are used to correct motion artifacts in MRI. These methods are found not to require unrealistic values of the signal-to-noise ratio and are likely to prove practical in future applications.

High-resolution inversion of finite Fourier transform data through a localised polynomial approximation

The authors address the problem of high-resolution inversion of finite Fourier transform data, which is frequently encountered in tomographic image reconstruction. A new parametric modelling approach, which uses an adaptive localised polynomial approximation model of the object function, is proposed to overcome the Gibbs artifact and the limited-resolution problem associated with conventional FFT methods. An algorithm for finding the model parameters is given which makes use of linear prediction theory, singular-value decomposition and least-squares fitting methods. Reconstruction results from simulated and real magnetic resonance experimental data are also presented to demonstrate its capability for Gibbs ringing reduction and resolution enhancement.

Gibbs artifact removal in magnetic resonance imaging.

Gibbs artifact in magnetic resonance imaging results when band-limited interpolation is used. This is typically done when there are more reconstructed pixels in the phase encoding direction of the image than corresponding phase encoding measurements. Such sampling is effectively an ideal (in a noise sense) low-pass filter which provides a maximal improvement in contrast resolution at the expense of a decrease in spatial resolution. In this paper we demonstrate that an alternate low-pass filter can be used to improve contrast resolution with a loss in spatial resolution and yet not result in Gibbs artifact. We show that the noise performance of this filter can be made to approach that of an ideal filter by properly specifying the number of samples averaged for each phase encoding index.