568-06-007 ISMRM Abstract

An Alternative Framework for Sparsity-Promoting Dynamic MRI Reconstruction with Reduced Hyper-Parameter Sensitivity

Primary: Acquisition & Reconstruction - Image Reconstruction: Non-AI

Secondary: Acquisition & Reconstruction - Open source software, sequences, and reconstruction algorithms

568-06-007 · Image Reconstruction II · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row I

Keywords: Sparse & Low-Rank Models FMRI Methods Zero TE fMRI Fast fMRI

Accepted

Aidan Mason-Mackay ^1,2, Daniela Calvetti³, Erkki Somersalo³, Mikko Kettunen², Antti Aarnio^1,2, Ekaterina Paasonen^2,4, Olli Grohn², Ville Kolehmainen¹

¹Department of Technical Physics, University of Eastern Finland, Kuopio, Finland

²A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

³Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, United States of America

⁴Kuopio University Hospital, Kuopio, Finland

Presenting Author: Aidan Mason-Mackay

Synopsis

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References

1. Fessler, J. A. (2019). Optimization methods for MR image reconstruction (long version). arXiv preprint arXiv:1903.03510.

2. Lyu, W., Fang, X., Huang, C., Lu, M., Wang, J., Shi, J., & Li, J. (2025). Fast MRI reconstruction: A thorough survey from single-modal to multi-modal. Expert Systems with Applications, 127703. DOI: 10.1016/j.eswa.2025.127703. [doi]

3. Mangia, S., Michaeli, S., & Gröhn, O. (2025). Outlook on zero/ultrashort echo time techniques in functional MRI. Magnetic resonance in medicine. DOI: 10.1002/mrm.70065. [doi]

4. Calvetti, D., & Somersalo, E. (2023). Bayesian scientific computing (Vol. 215). Berlin: Springer.

5. Bocchinfuso, A., Calvetti, D. and Somersalo, E., 2024. Bayesian sparsity and class sparsity priors for dictionary learning and coding. Journal of Computational Mathematics and Data Science, 11, p.100094. DOI: 10.1016/j.jcmds.2024.100094. [doi]

6. Waniorek, N., Calvetti, D. and Somersalo, E., 2023. Bayesian hierarchical dictionary learning. Inverse Problems, 39(2), p.024006. DOI: 0.1088/1361-6420/acad21.

7. Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine learning, 3(1), 1-122. DOI: 10.1561/2200000016. [doi]

8. O'sullivan, J. D. (2007). A fast sinc function gridding algorithm for Fourier inversion in computer tomography. IEEE transactions on medical imaging, 4(4), 200-207. DOI: 10.1109/TMI.1985.4307723. [doi]

9. Lustig, M., Donoho, D., & Pauly, J. M. (2007). Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 58(6), 1182-1195. DOI: doi.org/10.1002/mrm.21391. [doi]

10. Fong, D. C. L., & Saunders, M. (2011). LSMR: An iterative algorithm for sparse least-squares problems. SIAM Journal on Scientific Computing, 33(5), 2950-2971. DOI: 10.1137/10079687X. [doi]

11. Wohlberg, B. (2017). ADMM penalty parameter selection by residual balancing. arXiv preprint arXiv:1704.06209.

12. Hanhela, M., Kettunen, M., Gröhn, O., Vauhkonen, M., & Kolehmainen, V. (2020). Temporal Huber regularization for DCE-MRI. Journal of Mathematical Imaging and Vision, 62(9), 1334-1346. DOI: 10.1007/s10851-020-00985-2. [doi]

13. Benning, M., & Burger, M. (2018). Modern regularization methods for inverse problems. Acta numerica, 27, 1-111. DOI: 10.1017/S0962492918000016. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/568-06-007