Imaging With Confidence: Uncertainty Quantification for High-Dimensional Undersampled MR Images

MCML Authors

Hannah Laus

→ Group Felix Krahmer
 Optimization & Data Analysis

Felix Krahmer

Prof. Dr.

Principal Investigator

Optimization & Data Analysis

Holger Rauhut

Prof. Dr.

Principal Investigator

Mathematical Data Science and Artificial Intelligence

Abstract

Establishing certified uncertainty quantification (UQ) in imaging processing applications continues to pose a significant challenge. In particular, such a goal is crucial for accurate and reliable medical imaging if one aims for precise diagnostics and appropriate intervention. In the case of magnetic resonance imaging, one of the essential tools of modern medicine, enormous advancements in fast image acquisition were possible after the introduction of compressive sensing and, more recently, deep learning methods. Still, as of now, there is no UQ method that is both fully rigorous and scalable. This work takes a step towards closing this gap by proposing a total variation minimization-based method for pixel-wise sharp confidence intervals for undersampled MRI. We demonstrate that our method empirically achieves the predicted confidence levels. We expect that our approach will also have implications for other imaging modalities as well as deep learning applications in computer vision.

inproceedings

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ECCV 2024

18th European Conference on Computer Vision. Milano, Italy, Sep 29-Oct 04, 2024.

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Authors

F. Hoppe • C. M. Verdun • H. Laus • S. Endt • M. I. Menzel • F. Krahmer • H. Rauhut

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Links

DOI GitHub

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Research Area

 A2 | Mathematical Foundations