Removal of partial Fourier--induced Gibbs (RPG) ringing artifacts in MRI

Purpose: To investigate and remove Gibbs-ringing artifacts caused by partial Fourier (PF) acquisition and zero filling interpolation in MRI data. Theory and Methods: Gibbs ringing of fully sampled data, leading to oscillations around tissue boundaries, is caused by the symmetric truncation of k-space. Such ringing can be removed by conventional methods, with the local subvoxel shifts method being the state-of-the-art. However, the asymmetric truncation of k-space in routinely used PF acquisitions leads to additional ringings of wider intervals in the PF sampling dimension that cannot be corrected solely based on magnitude images reconstructed via zero filling. Here, we develop a pipeline for the Removal of PF-induced Gibbs ringing (RPG) to remove ringing patterns of different periods by applying the conventional method twice. The proposed pipeline is validated on numerical phantoms, demonstrated on in vivo diffusion MRI measurements, and compared with the conventional method and neural network-based approach. Results: For PF = 7/8 and 6/8, Gibbs-ringings and subsequent bias in diffusion metrics induced by PF acquisition and zero filling are robustly removed by using the proposed RPG pipeline. For PF = 5/8, however, ringing removal via RPG leads to excessive image blurring due to the interplay of image phase and convolution kernel. Conclusions: RPG corrects Gibbs-ringing artifacts in magnitude images of PF acquired data and reduces the bias in quantitative MR metrics. Considering the benefit of PF acquisition and the feasibility of ringing removal, we suggest applying PF = 6/8 when PF acquisition is necessary.

Automated summary

This study aims to investigate and remove Gibbs-ringing artifacts caused by PF acquisition and zero filling interpolation in MRI data. The proposed RPG pipeline effectively removes Gibbs-ringings and subsequent bias in diffusion metrics for PF = 7/8 and 6/8, but leads to excessive image blurring for PF = 5/8 due to the interplay of image phase and convolution kernel.