mlsanityData to 7 Oct 2026

Clinically Feasible Diffusion Reconstruction for Highly-Accelerated Cardiac Cine MRI

arXiv 2403.08749 ↗First posted 13 Mar 2024eess.IV cs.CVcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

The currently limited quality of accelerated cardiac cine reconstruction may potentially be improved by the emerging diffusion models, but the clinically unacceptable long processing time poses a challenge. We aim to develop a clinically feasible diffusion-model-based reconstruction pipeline to improve the image quality of cine MRI. A multi-in multi-out diffusion enhancement model together with fast inference strategies were developed to be used in conjunction with a reconstruction model. The diffusion reconstruction reduced spatial and temporal blurring in prospectively undersampled clinical data, as validated by experts inspection. The 1.5s per video processing time enabled the approach to be applied in clinical scenarios.

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 7 of its 8 references that are in the map. First posted on arXiv 13 Mar 2024.

Builds on

7 of 8 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstruction12 Jul
  2. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model sampling8 Mar
  3. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 4

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  3. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contractioncited by 306
  4. 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. 2024Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRI
  2. 2026Patch-Based Diffusion Reconstruction for Accelerated Cardiac Cine ↗
  3. 2026Piecewise Dynamic Diffusion Regularization for Reconstruction of Cardiac Cine MRI
  4. 2024Real-time cardiac cine MRI -- A comparison of a diffusion probabilistic model with alternative state-of-the-art image reconstruction techniques for undersampled spiral acquisitions
  5. 2025Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model
  6. 2025Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
  7. 2026CardioDiT: Latent Diffusion Transformers for 4D Cardiac MRI Synthesis
  8. 2023DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models