DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models
On the map: Diffusion models → Medical imaging and segmentation
Abstract (arXiv)
Performing magnetic resonance imaging (MRI) reconstruction from under-sampled k-space data can accelerate the procedure to acquire MRI scans and reduce patients' discomfort. The reconstruction problem is usually formulated as a denoising task that removes the noise in under-sampled MRI image slices. Although previous GAN-based methods have achieved good performance in image denoising, they are difficult to train and require careful tuning of hyperparameters. In this paper, we propose a novel MRI denoising framework DiffCMR by leveraging conditional denoising diffusion probabilistic models. Specifically, DiffCMR perceives conditioning signals from the under-sampled MRI image slice and generates its corresponding fully-sampled MRI image slice. During inference, we adopt a multi-round ensembling strategy to stabilize the performance. We validate DiffCMR with cine reconstruction and T1/T2 mapping tasks on MICCAI 2023 Cardiac MRI Reconstruction Challenge (CMRxRecon) dataset. Results show that our method achieves state-of-the-art performance, exceeding previous methods by a significant margin. Code is available at https://github.com/xmed-lab/DiffCMR.
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 18 of its 21 references that are in the map. First posted on arXiv 8 Dec 2023.
Builds on
18 of 21 references.
Closest ancestors · 3
Same direction, newest first
- 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models6 Feb
- 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model1 Nov
- 2021SegDiff: Image Segmentation with Diffusion Probabilistic Models1 Dec
Other references · 15
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
- 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
- 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
- 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
- 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
- 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 335
- 2022SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Modelcited by 276
- 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 231
- 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 201
3 more
- 2023Diffusion Model for Generative Image Denoisingcited by 27
- 2019Training a Neural Network for Gibbs and Noise Removal in Diffusion MRIcited by 7
- 2020MRI Banding Removal via Adversarial Trainingcited by 4
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Citing papers per year: 2024: 1.
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- 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
- 2023CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?
- 2024Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution
- 2025Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models
- 2025Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
- 2025Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching
- 2022Adaptive Diffusion Priors for Accelerated MRI Reconstruction
- 2025Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models