1768 ISMRM Abstract

Extending Scan-specific Artifact Reduction in K-space (SPARK) to Advanced Encoding and Reconstruction Schemes

1768 · Machine Learning: Removing Artifacts & Improving Resolution · Tuesday, 18 May, 1:00 PM–2:00 PM · Concurrent 1

Accepted

Yamin Arefeen ¹, Onur Beker², Heng Yu³, Elfar Adalsteinsson^4,5,6, Berkin Bilgic^5,7,8

¹Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, United States of America

²Department of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

³Department of Automation, Tsinghua University, Beijing, China

⁴Massachusetts Institute of Technology, Cambridge, United States of America

⁵Harvard-MIT Division of Health Sciences and Technology, Cambridge, United States of America

⁶Institute for Medical Engineering and Science, Cambridge, United States of America

⁷Athinoula A. Martinos Center for Biomedical Imaging, Cambridge, United States of America

⁸Department of Radiology, Harvard Medical School, Boston, United States of America

Presenting Author: Yamin Arefeen

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References

1. Knoll F, Hammernik K, Zhang C, Moeller S, Pock T, Sodickson DK, et al. Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues. IEEE Signal Process Mag. 2020;37: 128–140.

2. Knoll F, Hammernik K, Kobler E, Pock T, Recht MP, Sodickson DK. Assessment of the generalization of learned image reconstruction and the potential for transfer learning. Magn Reson Med. 2019;81: 116–128.

3. Akçakaya M, Moeller S, Weingärtner S, Uğurbil K. Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging. Magn Reson Med. 2019;81: 439–453.

4. Zhang C, Moeller S, Weingärtner S, Uğurbil K, Akçakaya M. Accelerated MRI using residual RAKI: Scan-specific learning of reconstruction artifacts. Annual Meeting of the International Society of Magnetic Resonance in Medicine.

5. Hosseini SAH, Moeller S, Weingärtner S, Uğurbil K, Akçakaya M. Accelerated Coronary Mri Using 3D Spirit-Raki With Sparsity Regularization. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

6. Beker O, Liao C, Cho J, Zhang Z, Setsompop K, Bilgic B. Scan-specific, Parameter-free Artifact Reduction in K-space (SPARK). arXiv [eess.IV].

7. Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med. 2002;47: 1202–1210.

8. Singh NM, Iglesias JE, Adalsteinsson E, Dalca AV, Golland P. Joint Frequency and Image Space Learning for Fourier Imaging. arXiv [cs.CV].

9. pp. 1692–1695.

10. Available: https://par.nsf.gov/servlets/purl/10095914 5. Zhang C, Moeller S, Weingärtner S, Uğurbil K, Akçakaya M. Accelerated Simultaneous Multi-Slice MRI using Subject-Specific Convolutional Neural Networks. Conf Rec Asilomar Conf Signals Syst Comput. 2018;2018: 1636–1640.

11. Available: http://arxiv.org/abs/1911.07219 8. Bilgic B, Gagoski BA, Cauley SF, Fan AP, Polimeni JR, Grant PE, et al. Wave-CAIPI for highly accelerated 3D imaging. Magn Reson Med. 2015;73: 2152–2162.

12. Available: http://arxiv.org/abs/2007.01441 11. Gagoski BA, Bilgic B, Eichner C, Bhat H, Grant PE, Wald LL, et al. RARE/turbo spin echo imaging with simultaneous multislice Wave-CAIPI. Magn Reson Med. 2015;73: 929–938.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2021/1768