560-02-010 ISMRM Abstract

Conformal Prediction for Rigorous Uncertainty Guarantees in Deep Learning-based QSM

Primary: Contrast Mechanisms - Susceptibility/QSM

Secondary: Contrast Mechanisms - Electromagnetic Tissue Properties

560-02-010 · New Developments in QSM II · Wednesday, 13 May, 9:15 AM–10:10 AM · Digital Posters Row A

Keywords: Uncertainty quantification Deep learning reconstruction Quantitative Susceptibility Mapping (QSM)

Accepted

Mathias Lambert ^1,2, Cristian Tejos^3,4,5, Carlos Milovic^3,4

¹Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, Chile

²Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile, Chile

³Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile

⁴Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

⁵Millennium Institute Millennium Institute for Intelligent Healthcare Engineering (iHEALTH), Pontificia Universidad Católica de Chile, Santiago, Chile

Presenting Author: Mathias Lambert

Synopsis

Motivation:

Goals:

Results:

Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

References

1. Guo C, Pleiss G, Sun Y, Weinberger KQ. On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70; 2017; Sydney, NSW, Australia. p. 1321-1330. doi:10.5555/3305381.3305518 [doi]

2. Vovk V, Gammerman A, Shafer G. Algorithmic Learning in a Random World. Berlin: Springer-Verlag; 2005. doi:10.1007/978-3-031-06649-8 [doi]

3. Angelopoulos A, Bates S, Malik J, Jordan MI. Uncertainty sets for image classifiers using conformal prediction. arXiv:2009.14193. 2020. doi:10.48550/arXiv.2009.14193 [doi]

4. QSM Challenge 2.0 Organization Committee, Bilgic B, Langkammer C, Marques JP, Meineke J, Milovic C, Schweser F. QSM reconstruction challenge 2.0: Design and report of results. Magn Reson Med. 2021; 86: 1241-1255. https://doi.org/10.1002/mrm.28754 [doi]

5. Dymerska B, Eckstein K, Bachrata B, et al. Phase unwrapping with a rapid opensource minimum spanning tree algorithm. Magn Reson Med. 2021; 85: 2294–2308. https://doi.org/10.1002/mrm.28563 [doi]

6. Li W, Wu B, Liu C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. Neuroimage. 2011;55(4):1645-1656. doi:10.1016/j.neuroimage.2010.11.088 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/560-02-010