mlsanityData to 7 Oct 2026

Real-time cardiac cine MRI -- A comparison of a diffusion probabilistic model with alternative state-of-the-art image reconstruction techniques for undersampled spiral acquisitions

arXiv 2410.04843 ↗First posted 7 Oct 2024physics.med-ph eess.IVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

ECG-gated cine imaging in breath-hold enables high-quality diagnostics in most patients, arrhythmia and inability to hold breath, however, can severely corrupt outcomes. Real-time cardiac MRI in free-breathing leverages robust and faster investigations regardless of these confounding factors. With the need for sufficient acceleration, adequate reconstruction methods, which transfer data into high quality images, are required. Undersampled spiral real-time acquisitions in free-breathing were conducted in a study with 16 healthy volunteers and 5 patients. Image reconstructions were performed using a novel score-based diffusion model, as well as a variational network and different compressed sensing approaches. The techniques were compared by means of an expert reader study, by calculating scalar metrics and difference images with respect to a segmented reference, and by a Bland-Altman analysis of cardiac functional parameters. In participants with irregular RR-cycles, spiral real-time acquisitions showed superior image quality with respect to the clinical reference standard. Reconstructions using the diffusion model, the variational network and l1-wavelets offered an overall comparable image quality, however sharpness was slightly increased by the diffusion approach. While slightly larger ejection fractions for the real-time acquisitions were exhibited with a bias of 1.6% for healthy subjects, differences in the data acquisition procedure resulted in uncertainties of 7.4%. The proposed real-time technique enables free-breathing acquisitions of spatio-temporal images with high-quality, covering the entire heart in less than one minute. Evaluation of the ejection fractions using the segmented reference can be significantly corrupted due to arrhythmias and averaging effects. Prolonged inference times of the diffusion model represent the main obstacle to overcome for clinical translation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 33 of its 61 references that are in the map. First posted on arXiv 7 Oct 2024.

Builds on

33 of 61 references.

Closest ancestors · 8

Same direction, newest first

  1. 2023Fast Controllable Diffusion Models for Undersampled MRI Reconstruction20 Nov
  2. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI19 Nov
  3. 2023Self-Supervised MRI Reconstruction with Unrolled Diffusion Models29 Jun
  4. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey14 Nov
  5. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstruction12 Jul
  6. 2022Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models3 Feb
  7. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov
  8. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 25

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  6. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  7. 2022Video Diffusion Modelscited by 2,280
  8. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  9. 2023One-step Diffusion with Distribution Matching Distillationcited by 978
  10. 2022On Distillation of Guided Diffusion Modelscited by 721
  11. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  12. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 340
13 more
  1. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contractioncited by 306
  2. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  3. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 201
  4. 2022Improving Sample Quality of Diffusion Models Using Self-Attention Guidancecited by 151
  5. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problemscited by 129
  6. 2019Deep Learning Methods for Parallel Magnetic Resonance Image Reconstructioncited by 81
  7. 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imagingcited by 49
  8. 2021Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstructioncited by 34
  9. 2020Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space datacited by 24
  10. 2024Plug-and-Play Diffusion Distillationcited by 16
  11. 2022Off-resonance artifact correction for magnetic resonance imaging: a reviewcited by 4
  12. 2023HyperSLICE: HyperBand optimized Spiral for Low-latency Interactive Cardiac Examinationcited by 3
  13. 2023Attention Hybrid Variational Net for Accelerated MRI Reconstructioncited by 3

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  4. 2026One Frame, Full Heartbeat: ECG-Free 4D Cardiac Cine MRI Synthesis via Radial-Decomposed Flow Matching
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  7. 2024Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRI
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