Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
568-05-001 ISMRM Abstract

Multi-Shot EPIK MRI Reconstruction with Deep Learning–Based Geometric Distortion Correction at 7 T

Primary: Acquisition & Reconstruction - Artifacts and Correction Strategies
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
568-05-001 · Image Reconstruction I · Wednesday, 13 May, 4:00 PM–4:55 PM · Digital Posters Row I
Keywords: Deep-learning-based image reconstruction High-resolution EPI High field MRI EPIK acquisition Geometric distortion correction
Accepted
Chaeeun Lim1, Seong Dae Yun2, N. Jon Shah2,3,4,5, Jaejin Cho1
1Department of Artificial Intelligence and Robotics, Sejong University, Seoul, Korea, Republic of
2Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
3Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Germany
4JARA - BRAIN - Translational Medicine, Aachen, Germany
5Department of Neurology, RWTH Aachen University, Aachen, Germany
Presenting Author: Young Woo Park

Synopsis

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References

1. Shah NJ, Zilles K. Verfahren zur Untersuchung eines Objektes mittels Erfassung des Ortsfrequenzraumes. German Patent Application. 2003;199.
2. Zaitsev M, Zilles K, Shah NJ. Sharedk-space echo planar imaging with keyhole. Magn Reson Med. 2001;45(1):109-117.
3. Yun SD, Pais-Roldán P, Palomero-Gallagher N, Shah NJ. Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols. Hum Brain Mapp. 2022;43(11):3386-3403.
4. Aggarwal HK, Mani MP, Jacob M. MoDL: Model-based deep learning architecture for inverse problems. IEEE Trans Med Imaging. 2019;38(2):394-405.
5. Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med. 2018;79(6):3055-3071.
6. Yaman B, Hosseini SAH, Moeller S, Ellermann J, Uğurbil K, Akçakaya M. Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson Med. 2020;84(6):3172-3191.
7. Yaman B, Hosseini SAH, Akçakaya M. Zero-shot self-supervised learning for MRI reconstruction. arXiv [eessIV]. February 2021. http://arxiv.org/abs/2102.07737.
8. Cho J, Jun Y, Wang X, Kobayashi C, Bilgic B. Improved multi-shot diffusion-weighted MRI with zero-shot self-supervised learning reconstruction. In: Lecture Notes in Computer Science. Lecture notes in computer science. Cham: Springer Nature Switzerland; 2023:457-466.
9. Liao C, Yarach U, Cao X, et al. High-fidelity mesoscale in-vivo diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction. Neuroimage. 2023;275(120168):120168.
10. Andersson JLR, Skare S. Ashburner How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage. 2003;20(2):870-888.
11. 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-S219.
12. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med. 1999;42(5):952-962.
13. Zahneisen B, Aksoy M, Maclaren J, Wuerslin C, Bammer R. Extended hybrid-space SENSE for EPI: Off-resonance and eddy current corrected joint interleaved blip-up/down reconstruction. Neuroimage. 2017;153:97-108.
14. Zhu K, Dougherty RF, Wu H, et al. Hybrid-space SENSE reconstruction for simultaneous Multi-slice MRI. IEEE Trans Med Imaging. 2016;35(8):1824-1836.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/568-05-001