ArticleMagnetic resonance in medicine2021
Robust autocalibrated structured low-rank EPI ghost correction.
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Online image reconstruction via Multiple Orthogonal Reference Sensitivity Encoding (MORSE).Magma (New York, N.Y.) · 2026Article
- Exploiting four-way phase-encoding benefits for robust detection and correction of EPI artifacts: Application to residual ghosts in diffusion MRI.Magnetic resonance imaging · 2025Article
- Navigator-free multi-shot diffusion MRI via non-local low-rank reconstruction.Magnetic resonance in medicine · 2025Article
- Fast and ultra-high shot diffusion MRI image reconstruction with self-adaptive Hankel subspace.Medical image analysis · 2025Article
- Blip-up blip-down circular EPI (BUDA-cEPI) for distortion-free dMRI with rapid unrolled deep learning reconstruction.Magnetic resonance imaging · 2025Article
- Dynamic field mapping and distortion correction using single-shot blip-rewound EPI and joint multi-echo reconstruction.Magnetic resonance in medicine · 2024Article
- Model-based iterative reconstruction for direct imaging with point spread function encoded echo planar MRI.Magnetic resonance imaging · 2024Article
- Improving robustness of 3D multi-shot EPI by structured low-rank reconstruction of segmented CAIPI sampling for fMRI at 7T.NeuroImage · 2023Article
- High-fidelity, high-spatial-resolution diffusion magnetic resonance imaging of ex vivo whole human brain at ultra-high gradient strength with structured low-rank echo-planar imaging ghost correction.NMR in biomedicine · 2023Article
- Multi-band multi-shot diffusion MRI reconstruction with joint usage of structured low-rank constraints and explicit phase mapping.Magnetic resonance in medicine · 2023Article
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7 authors.
Funding
Abstract
purposeWe propose and evaluate a new structured low-rank method for echo-planar 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.
methodsAutocalibrated LORAKS is a previous structured low-rank method for EPI ghost correction that uses GRAPPA-type autocalibration data to enable high-quality 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.
resultsRAC-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).
conclusionsRAC-LORAKS provides effective suppression of EPI ghosts and is robust to imperfect autocalibration data.
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