Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
568-06-005 ISMRM Abstract

SNR-Guided Compressed Sensing Reconstruction for Ultra-High Field Non-Cartesian MRI

Accepted
Hongyi Gu1, Hongyu Li1, Zhibo Zhu1, Jue Hou1, Zheng Zhong1, zihao zhu2, Qi Liu 1
1United Imaging Healthcare North America, Houston, United States of America
2United Imaging Healthcare, Shanghai, China
Presenting Author: Qi Liu

Synopsis

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References

1. Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med. 2007;58(6):1182-1195.
2. Baron CA, Dwork N, Pauly JM, Nishimura DG. Rapid Compressed Sensing Reconstruction of 3D Non-Cartesian MRI. Magn Reson Med. 2017;79(5):2685-2692.
3. Hong T, Hernandez-Garcia L, Fessler JM. A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI. IEEE Trans Comput Imaging. 2024;10(2):372-384.
4. Virtue P, Lustig M. The Empirical Effect of Gaussian Noise in Undersampled MRI Reconstruction. Tomography. 2017;3(4):211–221.
5. Schoormans J, Strijkers GJ, Hansen AC, Nederveen AJ, Coolen BF. Compressed sensing MRI with variable density averaging (CS-VDA) outperforms full sampling at low SNR. Phys Med Biol. 2020;65(4):045004.
6. Pohmann R, Speck O, Scheffler K. Signal-to-noise ratio and MR tissue parameters in human brain imaging at 3, 7, and 9.4 Tesla using current receive coil arrays. Magn Reson Med. 2016;75(2):801-809.
7. Uğurbil K. Imaging at ultrahigh magnetic fields: History, challenges, and solutions. Neuroimage. 2017;168:7-32.
8. Candès EJ, Wakin MB, Boyd SP. Enhancing Sparsity by Reweighted ℓ1 Minimization. J Fourier Anal Appl. 2008;14(5):877-905.
9. Gu H, Yaman B, Moeller S, Ellermann J, Ugurbil K, Akçakaya M. Revisiting ℓ₁-wavelet compressed-sensing MRI in the era of deep learning. Proc Natl Acad Sci U S A. 2022;119(33):e2201062119.
10. Fessler JA, Sutton BP. Nonuniform fast Fourier transforms using min-max interpolation. IEEE Trans Signal Process. 2003;51(2):560-574.
11. Pruessmann KP, Weiger M, Börnert P, Boesiger P. Advances in sensitivity encoding with arbitrary k-space trajectories. Magn Reson Med. 2001;46(4):638-651.
12. Pipe JG, Menon P. Sampling density compensation in MRI: Rationale and an iterative numerical solution. Magn Reson Med. 1999;41(1):179–186.
13. Fessler JA. Optimization methods for magnetic resonance image reconstruction: Key models and optimization algorithms. IEEE Signal Process Mag. 2020;37(1):33–40.
14. Aggarwal HK, Mani MP, Jacob M. MoDL: Model-based deep learning architecture for inverse problems. IEEE Trans Med Imaging. 2019;38(2):394‐405.
15. Kellman P, McVeigh ER. Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters. Magn Reson Med. 2005;54(6):1439-1447.

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