mlsanityData to 7 Oct 2026

Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model

arXiv 2403.18139 ↗First posted 26 Mar 2024eess.IV cs.CVcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Anatomically guided PET reconstruction using MRI information has been shown to have the potential to improve PET image quality. However, these improvements are limited to PET scans with paired MRI information. In this work we employed a diffusion probabilistic model (DPM) to infer T1-weighted-MRI (deep-MRI) images from FDG-PET brain images. We then use the DPM-generated T1w-MRI to guide the PET reconstruction. The model was trained with brain FDG scans, and tested in datasets containing multiple levels of counts. Deep-MRI images appeared somewhat degraded than the acquired MRI images. Regarding PET image quality, volume of interest analysis in different brain regions showed that both PET reconstructed images using the acquired and the deep-MRI images improved image quality compared to OSEM. Same conclusions were found analysing the decimated datasets. A subjective evaluation performed by two physicians confirmed that OSEM scored consistently worse than the MRI-guided PET images and no significant differences were observed between the MRI-guided PET images. This proof of concept shows that it is possible to infer DPM-based MRI imagery to guide the PET reconstruction, enabling the possibility of changing reconstruction parameters such as the strength of the prior on anatomically guided PET reconstruction in the absence of MRI.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 12 of its 37 references that are in the map. First posted on arXiv 26 Mar 2024.

Builds on

12 of 37 references.

Closest ancestors · 7

Same direction, newest first

  1. 2023DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model29 Nov
  2. 2023DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging7 Nov
  3. 2023Score-Based Generative Models for PET Image Reconstruction27 Aug
  4. 2023Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis28 Apr
  5. 2023CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis24 Mar
  6. 2022Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models19 Nov
  7. 2022PET image denoising based on denoising diffusion probabilistic models13 Sep

Other references · 5

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  3. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  4. 2022Diffusion Models in Vision: A Surveycited by 1,008
  5. 2023Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Modelcited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026FlowPET: Physics-Informed Symplectic Flow Matching for Low-Count PET Reconstructioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Supervised Diffusion-Model-Based PET Image Reconstruction
  2. 2025Personalized MR-Informed Diffusion Models for 3D PET Image Reconstruction
  3. 2025PET Image Reconstruction Using Deep Diffusion Image Prior
  4. 2025Physics-Constrained Diffusion Reconstruction with Posterior Correction for Quantitative and Fast PET Imaging
  5. 2025End-to-End PET Image Reconstruction via a Posterior-Mean Diffusion Model
  6. 2024Diffusion Transformer Model With Compact Prior for Low-dose PET Reconstruction
  7. 2025PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts
  8. 2024Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet