Ghost reduction in echo‐planar imaging by joint reconstruction of images and line‐to‐line delays and phase errors

Abstract

Purpose To correct line-to-line delays and phase errors in echo-planar imaging (EPI). Theory and Methods EPI-trajectory auto-corrected image reconstruction (EPI-TrACR) is an iterative maximum-likelihood technique that exploits data redundancy provided by multiple receive coils between nearby lines of k-space to determine and correct line-to-line trajectory delays and phase errors that cause ghosting artifacts. EPI-TrACR was efficiently implemented using a segmented FFT and was applied to in vivo brain data acquired at 7 Tesla across acceleration (1x–4x) and multishot factors (1 to 4 shots), and in a time series. Results EPI-TrACR reduced ghosting across all acceleration factors and multishot factors, compared to conventional calibrated reconstructions and the PAGE method. It also achieved consistently lower ghosting in the time series. Averaged over all cases, EPI-TrACR reduced root-mean-square ghosted signal outside the brain by 27% compared to calibrated reconstruction, and by 40% compared to PAGE. Conclusion EPI-TrACR automatically corrects line-to-line delays and phase errors in multishot, accelerated, and dynamic EPI. While the method benefits from additional calibration data for initialization, it was not a requirement for most reconstructions.

Navigator-Free EPI Ghost Correction With Structured Low-Rank Matrix Models: New Theory and Methods

“…The first category contains simple model-based approaches such as Refs. [3]- [10], which assume a low-dimensional model to describe a systematic phase mismatch between even and odd lines. The parameters of the mismatch model are often estimated using separate navigator data, and can then be used to correct the mismatch in the measured EPI data.…”

Magnetic resonance fingerprinting for simultaneous renal T1 and T2* mapping in a single breath‐hold

“…Therefore, improving the EPI baseline image quality was shown to improve the MRF map quality. Reduction of ghosting artifacts, Nyquist artifact, and motion correction were recently published, which all have the potential to improve the image quality of the proposed MRF method. Despite advanced shimming, field inhomogeneities disturb the k‐space echo train and therefore lead to geometric distortions .…”

k‐Space deep learning for reference‐free EPI ghost correction

“…However, these classic navigator‐free methods are generally ineffective compared to the reference‐based methods, or the computational complexity is higher. Recently, some correction methods used phased array coils information to correct ghost artifact . Dual polarity generalized autocalibrating partial parallel acquisition (dual polarity GRAPPA) uses multiple GRAPPA kernels to correct phase mismatch and fill the missing k‐space simultaneously.…”

“…Recently, some correction methods used phased array coils information to correct ghost artifact. [16][17][18] Dual polarity generalized autocalibrating partial parallel acquisition (dual polarity GRAPPA) 16 uses multiple GRAPPA kernels to correct phase mismatch and fill the missing k-space simultaneously. This method is robust to remove artifact from nonlinear phase errors, but it requires an additional EPI calibration scan.…”