Distortion Correction for EPI

Jie Luo¹
¹Shanghai Jiao Tong University, China

Synopsis

Keywords: Image acquisition: Artefacts, Image acquisition: Image processing

The Echo Planar Imaging (EPI) sequence is a cornerstone of MRI studies, widely employed in functional MRI (fMRI) and diffusion MRI (dMRI). However, static field inhomogeneities leads to serious image distortions, leading to impaired data integrity, difficulty registering functional images to structural images, and errors in downstream analyses. In addition, MR spectroscopic imaging (MRSI) that employs EPSI readout also suffer from static field inhomogeneities induced spectral distortions. In this course, we will illustrate the common distortions of EPI sequences, review state-of-the-art distortion correction methods, and touch upon some of the efforts that leverage machine learning in EPI distortion correction.

Outline

1. Quick overview of EPI and its application in fMRI, dMRI, and MRSI.
2. Understanding B0 inhomogeneity-induced artifacts (or susceptibility artifacts)
3. Techniques for distortion correction

Considerations in data acquisition
State-of-the-art post-processing methods
Recent progress leveraging machine learning approaches

4. Impacts on potential applications

Acknowledgements

This work is partially supported by NSFC # 62101321.

References

Review papers:

1. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23 Suppl 1:S208-219. doi:10.1016/j.neuroimage.2004.07.051.

2. Glasser MF, Sotiropoulos SN, Wilson JA, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013;80:105-124. doi:10.1016/j.neuroimage.2013.04.127.

3. Chen Z, Pawar K, Ekanayake M, Pain C, Zhong S, Egan GF. Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges. J Digit Imaging. 2023;36(1):204-230. doi:10.1007/s10278-022-00721-9.

Articles:

4. Jezzard P, Balaban RS. Correction for geometric distortion in echo planar images from B0 field variations. Magnetic Resonance in Medicine. 1995;34(1):65-73. doi:10.1002/mrm.1910340111.

5. Andersson JL, Hutton C, Ashburner J, Turner R, Friston K. Modeling geometric deformations in EPI time series. Neuroimage. 2001;13(5):903-919. doi:10.1006/nimg.2001.0746.

6. Andersson JLR, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage. 2003;20(2):870-888. doi:10.1016/S1053-8119(03)00336-7.

7. Jenkinson M, Wilson JL, Jezzard P. Perturbation method for magnetic field calculations of nonconductive objects. Magn Reson Med. 2004;52(3):471-477. doi:10.1002/mrm.20194.

8. Esteban O, Blair RW, Nielson DM, et al. Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines. Sci Data. 2019;6(1):30. doi:10.1038/s41597-019-0035-4.

9. Hutton C, Bork A, Josephs O, Deichmann R, Ashburner J, Turner R. Image Distortion Correction in fMRI: A Quantitative Evaluation. NeuroImage. 2002;16(1):217-240. doi:10.1006/nimg.2001.1054.

10. Wu M, Chang LC, Walker L, et al. Comparison of EPI Distortion Correction Methods in Diffusion Tensor MRI Using a Novel Framework. Med Image Comput Comput Assist Interv. 2008;11(Pt 2):321-329.

11. Eslami R, Jacob M. Correction of B0 inhomogeneity distortion in magnetic resonance spectroscopic imaging. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE; 2009:405-408. doi:10.1109/ICASSP.2009.4959606

12. Liu Y, Ma C, Clifford BA, Lam F, Johnson CL, Liang Z-P. Improved Low-Rank Filtering of Magnetic Resonance Spectroscopic Imaging Data Corrupted by Noise and B_0 Field Inhomogeneity. IEEE Transactions on Biomedical Engineering. 2016;63(4):841-849. doi:10.1109/TBME.2015.2476499

13. Peng X, Nguyen H, Haldar J, Hernando D, Wang XP, Liang Z-P. Correction of field inhomogeneity effects on limited k-space MRSI data using anatomical constraints. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE; 2010:883-886. doi:10.1109/IEMBS.2010.5627873

14. Lam F, Sutton BP. Intravoxel B0 inhomogeneity corrected reconstruction using a low-rank encoding operator. Magnetic Resonance in Medicine. 2020;84(2):885-894. doi:10.1002/mrm.28182

15. Lee J, Han Y, Ryu JK, Park JY, Ye JC. k-Space deep learning for reference-free EPI ghost correction. Magn Reson Med. 2019;82(6):2299-2313. doi:10.1002/mrm.27896.

16. Hu Z, Wang Y, Zhang Z, et al. Distortion correction of single-shot EPI enabled by deep-learning. Neuroimage. 2020;221:117170. doi:10.1016/j.neuroimage.2020.117170.

17. Schilling KG, Blaber J, Hansen C, et al. Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps. PLoS One. 2020;15(7):e0236418. doi:10.1371/journal.pone.0236418.

18. Zaid Alkilani A, Çukur T, Saritas EU. FD-Net: An unsupervised deep forward-distortion model for susceptibility artifact correction in EPI. Magnetic Resonance in Medicine. 2024;91(1):280-296. doi:10.1002/mrm.29851.

Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)