mlsanityData to 7 Oct 2026

Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRI

arXiv 2403.08758 ↗First posted 13 Mar 2024eess.IV cs.CVcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Current deep learning reconstruction for accelerated cardiac cine MRI suffers from spatial and temporal blurring. We aim to improve image sharpness and motion delineation for cine MRI under high undersampling rates. A spatiotemporal diffusion enhancement model conditional on an existing deep learning reconstruction along with a novel paired sampling strategy was developed. The diffusion model provided sharper tissue boundaries and clearer motion than the original reconstruction in experts evaluation on clinical data. The innovative paired sampling strategy substantially reduced artificial noises in the generative results.

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 6 of its 7 references that are in the map. First posted on arXiv 13 Mar 2024.

Builds on

6 of 7 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model sampling8 Mar
  2. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov
  3. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 3

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Leveraging Diffusion Model and Image Foundation Model for Improved Correspondence Matching in Coronary Angiographycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Clinically Feasible Diffusion Reconstruction for Highly-Accelerated Cardiac Cine MRI
  2. 2026Piecewise Dynamic Diffusion Regularization for Reconstruction of Cardiac Cine MRI
  3. 2022Accelerated Motion Correction with Deep Generative Diffusion Models
  4. 2026CardioDiT: Latent Diffusion Transformers for 4D Cardiac MRI Synthesis
  5. 2026Patch-Based Diffusion Reconstruction for Accelerated Cardiac Cine ↗
  6. 2025Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model
  7. 2025Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models
  8. 2023Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models