Robust autocalibrated structured low‐rank EPI ghost correction

Abstract

Purpose: We propose and evaluate a new structured low-rank method for echoplanar imaging (EPI) ghost correction called Robust Autocalibrated LORAKS (RAC-LORAKS). The method can be used to suppress EPI ghosts arising from the differences between different readout gradient polarities and/or the differences between different shots. It does not require conventional EPI navigator signals, and is robust to imperfect autocalibration data. Methods: Autocalibrated LORAKS is a previous structured low-rank method for EPI ghost correction that uses GRAPPA-type autocalibration data to enable highquality ghost correction. This method works well when the autocalibration data are pristine, but performance degrades substantially when the autocalibration information is imperfect. RAC-LORAKS generalizes Autocalibrated LORAKS in two ways. First, it does not completely trust the information from autocalibration data, and instead considers the autocalibration and EPI data simultaneously when estimating low-rank matrix structure. Second, it uses complementary information from the autocalibration data to improve EPI reconstruction in a multi-contrast joint reconstruction framework. RAC-LORAKS is evaluated using simulations and in vivo data, including comparisons to state-of-the-art methods. Results: RAC-LORAKS is demonstrated to have good ghost elimination performance compared to state-of-the-art methods in several complicated EPI acquisition scenarios (including gradient-echo brain imaging, diffusion-encoded brain imaging, and cardiac imaging). Conclusions: RAC-LORAKS provides effective suppression of EPI ghosts and is robust to imperfect autocalibration data.

High-fidelity, high-spatial-resolution diffusion MRI of the ex-vivo whole human brain on the 3T Connectom scanner using structured low-rank EPI ghost correction

“…We used a fixed value of 100 for all the experiments. This agrees with previous findings about the robustness of the performance of SLM methods with respect of the rank (Lobos, et al, 2021). We, however, noticed that the size of the k-…”

“…Nevertheless, we expect this approach to require prohibitively large RAM. Finally, it should be noted that other more advanced SLM methods that use additional scan(s) for ghosting reduction could help reducing the artifact further (Lobos, et al, 2021), although a detailed investigation and comparison of different SLM-based methods is beyond the scope of this work.…”

“…The literature of EPI ghosting correction is rich and a comprehensive review of it is beyond the scope of this work. However, it is instructive to place the proposed methodology into one of the two main categories of ghost correction methods: phase correction approaches that rely on navigator data (Hu & Le, 1996) (Chen & Wyrwicz, 2004) (Bruder, Fischer, Reinfelder, & Schmitt) (Hennel, 1998), and other techniques that make fewer assumptions on the model but instead treat the ghosting problem as an inverse reconstruction problem in which techniques adapted from parallel imaging can be used (Hoge & Polimeni, 2016) (Lee, Jin, & Ye, 2016) (Xie, Lyu, Liu, Feng, & Wu, 2018) (Lobos, Kim, W.S, & Haldar, 2018) (Lobos, et al, 2021). Note that in this classification we are omitting other techniques that focus on mitigating eddy current effects in the acquisition, e.g., gradient shielding (Mansfield & Chapman, 1986), pre-emphasis or dual spin-echo sequences (Reese, Heid, Weisskoff, & Wedeen, 2003), and other hardware solutions such as higher-order field monitoring (Wilm, Barmet, Pavan, & Pruessmann, 2011).…”

“…In this work, we incorporate constraints derived from the theory of SLM. SLM-based methods have been successfully applied in conventional MRI undersampling problems (Haldar J. P., 2013) (Shin, et al, 2014) (Jin, Lee, & Ye, 2015) (Kim, Setsompop, & Haldar, 2017), and recently in more problem-specific scenarios such as single-shot EPIghosting correction (Lee, Jin, & Ye, 2016) (Lobos, Kim, W.S, & Haldar, 2018) (Lobos, et al, 2021) (Mani, Aggarwal, Magnotta, & Jacob, 2020) or in vivo diffusion MRI multishot reconstruction (Mani, Jacob, Kelley, & Magnotta, 2017) (Mani, Aggarwal, Magnotta, & Jacob, 2020).…”

Improving robustness of 3D multi-shot EPI by structured low-rank reconstruction of segmented CAIPI sampling for fMRI at 7T

“…The moderate phase variations in fMRI typically do not lead to visible artifacts in the calculated coil sensitivities. However, in the worst case scenario when high fidelity coil sensitivity maps cannot be obtained from the multi-shot calibration data, alternative approaches can be used, such as employing the low-rank tensor representation (Hess et al, 2021; Liu et al, 2021; Yi et al, 2021) of multi-channel k-space for a calibration-less reconstruction without explicit use of coil sensitivity maps, or using a calibration consistency constraint that jointly identifies a coilnullspace from the imaging and calibration data, without trusting either dataset completely (Lobos et al, 2021). In addition, we did not use partial Fourier sampling in this work, which is also compatible with the seg-CAIPI trajectory.…”