mlsanityData to 14 Oct 2026

MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)

arXiv 2412.18723 ↗First posted 25 Dec 2024eess.IV cs.CV cs.LGcited by 4

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Magnetic Resonance Imaging (MRI) is a powerful imaging technique widely used for visualizing structures within the human body and in other fields such as plant sciences. However, there is a demand to develop fast 3D-MRI reconstruction algorithms to show the fine structure of objects from under-sampled acquisition data, i.e., k-space data. This emphasizes the need for efficient solutions that can handle limited input while maintaining high-quality imaging. In contrast to previous methods only using 2D, we propose a 3D MRI reconstruction method that leverages a regularized 3D diffusion model combined with optimization method. By incorporating diffusion based priors, our method improves image quality, reduces noise, and enhances the overall fidelity of 3D MRI reconstructions. We conduct comprehensive experiments analysis on clinical and plant science MRI datasets. To evaluate the algorithm effectiveness for under-sampled k-space data, we also demonstrate its reconstruction performance with several undersampling patterns, as well as with in- and out-of-distribution pre-trained data. In experiments, we show that our method improves upon tested competitors.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 21 of its 56 references that are in the map. First posted on arXiv 25 Dec 2024.

Builds on

21 of 56 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models19 Nov
  2. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov
  3. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 18

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  3. 2022Video Diffusion Modelscited by 2,305
  4. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  5. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 508
  6. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
  7. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 433
  8. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 387
  9. 2022MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolationcited by 309
  10. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contractioncited by 307
  11. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 286
  12. 2017Image reconstruction by domain transform manifold learningcited by 232
6 more
  1. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 202
  2. 2017Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Losscited by 82
  3. 2019Deep Learning Methods for Parallel Magnetic Resonance Image Reconstructioncited by 81
  4. 2017A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networkscited by 43
  5. 2023A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imagingcited by 10
  6. 2020Fast Variable Density Poisson-Disc Sample Generation with Directional Variationcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025From 2D to 3D, Deep Learning-based Shape Reconstruction in Magnetic Resonance Imaging: A Reviewcited by 2
  2. 2025Active MRI Acquisition with Diffusion Guided Bayesian Experimental Designcited by 0
  3. 2025Physics-Guided Diffusion Priors for Multi-Slice Reconstruction in Scientific Imagingcited by 0
  4. 2026MK-ResRecon: Multi-Kernel Residual Framework for Texture-Aware 3D MRI Refinement from Sparse 2D Slicescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation
  2. 2023Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
  3. 2024X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models
  4. 2026Improving 2D Diffusion Models for 3D Medical Imaging with Inter-Slice Consistent Stochasticity
  5. 2023Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction
  6. 2026Consistency Models for Fast MRI Reconstruction Using Regularization by Denoising
  7. 2026High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
  8. 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data