Artifact Correction for Echo-Planar Imaging at Low-Field and Ultra-Low-Field MRI
2026-05-25 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors worked on improving MRI scans taken at very low magnetic fields, which often suffer from a type of image distortion called Nyquist ghost artifacts. They developed a new method that fixes these distortions without needing extra reference scans, using a technique called peak-alignment followed by interpolation and resampling. Their approach was tested on brain images and successfully reduced these artifacts, making the images clearer and more reliable. This method helps make low-field MRI more practical and useful in clinical settings.
Echo-planar imaging (EPI)Low-field MRIUltra-low-field MRINyquist ghost artifactsk-spaceReference scanPeak-alignmentInterpolationResamplingDiffusion-weighted imaging (DWI)
Authors
Sisi Qiao, Yilin Yu, Tiecheng Lin, Yuhao Liu, Jiajia Sun, Xiaoling Li
Abstract
Purpose: Echo-planar imaging (EPI) in low-field (LF) and ultra-low-field MRI (ULF) suffers from severe Nyquist ghost artifacts due to odd-even k-space misalignment. This study develops a reference-free artifact correction pipeline that reduces reliance on conventional reference scans while achieving improved ghost suppression. Methods: Starting from the traditional reference-scan-based ghost artifact correction method, we first introduce a peak-alignment-based ghost artifact correction method to correct odd-even line displacement without reference data. To further reduce residual artifacts, an interpolation-and-resampling strategy is applied. The combined method was evaluated using EPI and diffusion-weighted EPI data in LF and ULF. Results: The proposed pipeline effectively mitigated Nyquist ghosts, improved structural continuity, and enhanced signal uniformity. Peak-alignment-based ghost artifact correction method alone provided comparable artifact suppression to reference-scan-based ghost artifact correction method, while interpolation and resampling further suppressed residual artifacts, enabling reliable visualization of brain structures under ULF conditions. Conclusion: A practical, reference-free correction pipeline is presented for LF and ULF EPI, combining peak-alignment-based ghost artifact correction method and interpolation-resampling to achieve efficient ghost suppression and expand the clinical applicability of low-field MRI systems, providing both theoretical guidance and practical experience for ULF EPI-based DWI imaging.