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Updated: Sep 16, 2025
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Exploiting four-way phase-encoding benefits for robust detection and correction of EPI artifacts: Application to
Anh Thai1, Lin Ching Chang2, Carlo Pierpaoli3
1Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA; Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, USA.
Purpose:
To propose and develop an image processing-based methodology for detecting and correcting residual Nyquist ghost artifacts in echo planar imaging (EPI), specifically using non-diffusion-weighted (b=0s/mm2) images acquired with four distinct phase-encoding directions (PEDs) in diffusion MRI.
Approach:
Previous studies have demonstrated that acquiring images with four different PEDs can improve the reproducibility of diffusion derived quantitative maps. This improvement is achieved by averaging across PEDs to reduce the impact of residual EPI Nyquist ghost artifacts. These residual artifacts originate from imperfect ghost correction applied during image reconstruction and often persist into the preprocessing pipeline, potentially biasing downstream analyses. Building on these observations, the proposed method further improves this 4-way encoding approach by leveraging the properties of signal distributions to detect artifactual regions, specifically in non-diffusion weighted (b=0s/mm2). While the corrections are applied only to the b=0 images, its effectiveness is evaluated through downstream diffusion tensor estimation, by assessing the improvements in diffusion-derived metrics such as FA and MD. Additionally, the method can be tailored for specific artifact manifestations by considering their localization due to underlying acquisition parameters.
Results:
Simulations with known ground-truth images demonstrated high artifact detection accuracy, achieving a Dice score of 0.91 for reconstructions without parallel imaging. In the in-vivo dataset, the method also improved longitudinal reproducibility, reducing variability by 30 % in ghost-affected regions.
Conclusion:
The proposed correction method effectively detected and corrected residual ghost artifacts without the need of any additional k-space data, specifically in non-diffusion weighted images. This retrospective approach can be directly integrated into existing processing pipelines to further improve the quality of EPI images and enhancing image quality in studies that utilize 4-way PEDs acquisition.
