Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
360-02-012 ISMRM Abstract

A Unified Approach for Maintaining MRI Reconstruction Quality and Quantifying both Aleatoric and Epistemic Uncertainty

Primary: Acquisition & Reconstruction - AI methods
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
360-02-012 · Machine Learning for Image Reconstruction · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row A
Keywords: Image Reconstruction Machine Learning/Artificial Intelligence Uncertainty quantification
Accepted
Satoshi Kuroki1, Naoto Fujita 1, Tomoki Amemiya2, Suguru Yokosawa2, Toru Shirai2, Yasuhiko Terada1
1Graduate School of Science and Technology, The University of Tsukuba, Tsukuba, Japan
2Medical Systems Research & Development Center, Fujifilm Corporation, Minato, Japan
Presenting Author: Naoto Fujita

Synopsis

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References

1. M. Abdar et al., “A review of uncertainty quantification in deep learning: Techniques, applications and challenges,” Inf. Fusion, vol. 76, pp. 243–297, Dec. 2021, doi: 10.1016/j.inffus.2021.05.008. [doi]
2. S. Seoni, V. Jahmunah, M. Salvi, P. D. Barua, F. Molinari, and U. R. Acharya, “Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023),” Comput. Biol. Med., vol. 165, p. 107441, Oct. 2023, doi: 10.1016/j.compbiomed.2023.107441. [doi]
3. J. Schlemper et al., “Bayesian Deep Learning for Accelerated MR Image Reconstruction,” in Machine Learning for Medical Image Reconstruction, F. Knoll, A. Maier, and D. Rueckert, Eds., Cham: Springer International Publishing, 2018, pp. 64–71. doi: 10.1007/978-3-030-00129-2_8. [doi]
4. Ziyu Fu, Fujita Naoto, and Terada Yasuhiko, “Enhancing Reliability in Model-based DL Reconstruction: A Systematic Study of MC Dropout for Uncertainty Quantification,” presented at the ISMRM, 2024. Accessed: Apr. 07, 2025. [Online]. Available: https://archive.ismrm.org/2024/2818.html
5. C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra, “Weight Uncertainty in Neural　Networks,” May 21, 2015, arXiv: arXiv:1505.05424. doi: 10.48550/arXiv.1505.05424. [doi]
6. H. K. Aggarwal, M. P. Mani, and M. Jacob, “MoDL: Model Based Deep Learning Architecture for Inverse Problems,” IEEE Trans. Med. Imaging, vol. 38, no. 2, pp. 394–405, Feb. 2019, doi: 10.1109/TMI.2018.2865356. [doi]
7. Mengye Lyu, Lifeng Mei, and Shoujin Huang, “M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research | Scientific Data.” Accessed: Apr. 07, 2025. [Online]. Available: https://www.nature.com/articles/s41597-023-02181-4
8. Zbontar, Jure, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J. Muckley, Aaron Defazio, et al. “fastMRI: An Open Dataset and Benchmarks for Accelerated MRI.” 2018. arXiv preprint arXiv:1811.08839. https://arxiv.org/abs/1811.08839.

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http://echo.ismrm.org/p/ISMRM2026/360-02-012