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Evaluating structural uncertainty in accelerated MRI: are voxelwise measures useful surrogates?

arXiv 2503.10527 ↗First posted 13 Mar 2025eess.IV cs.CV physics.med-phcited by 0

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Abstract (arXiv)

Introducing accelerated reconstruction algorithms into clinical settings requires measures of uncertainty quantification that accurately assess the relevant uncertainty introduced by the reconstruction algorithm. Many currently deployed approaches quantifying uncertainty by focusing on measuring the variability in voxelwise intensity variation. Although these provide interpretable maps, they lack a structural interpretation and do not show a clear relationship to how the data will be analysed subsequently. In this work we show that voxel level uncertainty does not provide insight into morphological uncertainty. To do so, we use segmentation as a clinically-relevant downstream task and deploy ensembles of reconstruction modes to measure uncertainty in the reconstructions. We show that variability and bias in the morphological structures are present and within-ensemble variability cannot be predicted well with uncertainty measured only by voxel intensity variations.

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References · 19

  1. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  2. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  3. 2020Bayesian Deep Learning and a Probabilistic Perspective of Generalizationcited by 480
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  5. 2021Score-based diffusion models for accelerated MRIcited by 311
  6. 2021Ensemble deep learning: A reviewcited by 284
  7. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 202
  8. 2018Distribution Matching Losses Can Hallucinate Features in Medical Image Translationcited by 99
  9. 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imagingcited by 50
  10. 2019Uncertainty Quantification in Deep MRI Reconstructioncited by 49
  11. 2017Recurrent Inference Machines for Solving Inverse Problemscited by 33
  12. 2023A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methodscited by 32
7 more
  1. 2021Bayesian Uncertainty Estimation of Learned Variational MRI Reconstructioncited by 30
  2. 2021Deep Ensembles from a Bayesian Perspectivecited by 27
  3. 2024Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Modelscited by 12
  4. 2024Deep Learning for Accelerated and Robust MRI Reconstruction: a Reviewcited by 9
  5. 2024Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Predictioncited by 8
  6. 2023Inverse Problems with Learned Forward Operatorscited by 4
  7. 2025Deep Ensembles Secretly Perform Empirical Bayescited by 4

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