mlsanityData to 14 Oct 2026

Estimating Uncertainty in PET Image Reconstruction via Deep Posterior Sampling

arXiv 2306.04664 ↗First posted 7 Jun 2023eess.IV cs.CV cs.LGcited by 2

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Abstract (arXiv)

Positron emission tomography (PET) is an important functional medical imaging technique often used in the evaluation of certain brain disorders, whose reconstruction problem is ill-posed. The vast majority of reconstruction methods in PET imaging, both iterative and deep learning, return a single estimate without quantifying the associated uncertainty. Due to ill-posedness and noise, a single solution can be misleading or inaccurate. Thus, providing a measure of uncertainty in PET image reconstruction can help medical practitioners in making critical decisions. This paper proposes a deep learning-based method for uncertainty quantification in PET image reconstruction via posterior sampling. The method is based on training a conditional generative adversarial network whose generator approximates sampling from the posterior in Bayesian inversion. The generator is conditioned on reconstruction from a low-dose PET scan obtained by a conventional reconstruction method and a high-quality magnetic resonance image and learned to estimate a corresponding standard-dose PET scan reconstruction. We show that the proposed model generates high-quality posterior samples and yields physically-meaningful uncertainty estimates.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 53 references that are in the map. First posted on arXiv 7 Jun 2023.

Builds on

25 of 53 references.

References · 25

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13 more
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  2. 2025Comparison of Deterministic and Probabilistic Machine Learning Algorithms for Precise Dimensional Control and Uncertainty Quantification in Additive Manufacturingcited by 0