Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
430-03-007 ISMRM Abstract

A Trainable Uncertainty Module for Image Reconstruction Methods using Conformal Prediction

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - AI methods
430-03-007 · Advanced Image Reconstruction · Tuesday, 12 May, 1:40 PM–3:30 PM · Meeting Room 2.60
Keywords: AI/ML Image Reconstruction Uncertainty quantification Unrolled Networks and Reconstruction Deep-learning-based image reconstruction Conformal prediction
Accepted
Ilias I Giannakopoulos 1, Lokesh Gautham Boominathan Muthukumar1,2, Yvonne W Lui1,3, Riccardo Lattanzi1,3
1The Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, United States of America
2Courant Institute of Mathematical Sciences, NYU, New York, United States of America
3The Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Grossman School of Medicine, New York, United States of America
Presenting Author: Ilias I Giannakopoulos

Synopsis

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References

1. Johnson, Patricia M., et al. "Deep learning reconstruction enables prospectively accelerated clinical knee MRI." Radiology 307.2 (2023): e220425.
2. Bhadra, Sayantan, et al. "On hallucinations in tomographic image reconstruction." IEEE transactions on medical imaging 40.11 (2021): 3249-3260.
3. Kiryu, Shigeru, et al. "Clinical impact of deep learning reconstruction in MRI." Radiographics 43.6 (2023): e220133.
4. Radmanesh, Alireza, et al. "Exploring the acceleration limits of deep learning variational network–based two-dimensional brain MRI." Radiology: Artificial Intelligence 4.6 (2022): e210313.
5. Abdar, Moloud, et al. "A review of uncertainty quantification in deep learning: Techniques, applications and challenges." Information fusion 76 (2021): 243-297.
6. Wang, Ke, et al. "Rigorous uncertainty estimation for MRI reconstruction." Proceedings of the proceedings of the 30th annual meeting of ISMRM. Vol. 749. 2022.
7. Angelopoulos, Anastasios N., et al. "Image-to-image regression with distribution-free uncertainty quantification and applications in imaging." International Conference on Machine Learning. PMLR, 2022.
8. Sriram, Anuroop, et al. "End-to-end variational networks for accelerated MRI reconstruction." International conference on medical image computing and computer-assisted intervention. Cham: Springer International Publishing, 2020.
9. Zbontar, Jure, et al. "fastMRI: An open dataset and benchmarks for accelerated MRI." arXiv preprint arXiv:1811.08839 (2018).
10. Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Cham: Springer international publishing, 2015.
11. Sharma, Sagar, Simone Sharma, and Anidhya Athaiya. "Activation functions in neural networks." Towards Data Sci 6.12 (2017): 310-316.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/430-03-007