mlsanityData to 14 Oct 2026

Robust Simultaneous Multislice MRI Reconstruction Using Slice-Wise Learned Generative Diffusion Priors

arXiv 2407.21600 ↗First posted 31 Jul 2024eess.IV cs.AI cs.CVcited by 2

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Simultaneous multislice (SMS) imaging is a powerful technique for accelerating magnetic resonance imaging (MRI) acquisitions. However, SMS reconstruction remains challenging due to complex signal interactions between and within the excited slices. In this study, we introduce ROGER, a robust SMS MRI reconstruction method based on deep generative priors. Utilizing denoising diffusion probabilistic models (DDPM), ROGER begins with Gaussian noise and gradually recovers individual slices through reverse diffusion iterations while enforcing data consistency from measured k-space data within the readout concatenation framework. The posterior sampling procedure is designed such that the DDPM training can be performed on single-slice images without requiring modifications for SMS tasks. Additionally, our method incorporates a low-frequency enhancement (LFE) module to address the practical issue that SMS-accelerated fast spin echo (FSE) and echo planar imaging (EPI) sequences cannot easily embed fully-sampled autocalibration signals. Extensive experiments on both retrospectively and prospectively accelerated datasets demonstrate that ROGER consistently outperforms existing methods, enhancing both anatomical and functional imaging with strong out-of-distribution generalization. The source code and sample data for ROGER are available at https://github.com/Solor-pikachu/ROGER.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 29 of its 87 references that are in the map. First posted on arXiv 31 Jul 2024.

Builds on

29 of 87 references.

Closest ancestors · 9

Same direction, newest first

  1. 2024Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI8 Mar
  2. 2023Self-Supervised MRI Reconstruction with Unrolled Diffusion Models29 Jun
  3. 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization4 Apr
  4. 2022Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models19 Nov
  5. 2022High-Frequency Space Diffusion Models for Accelerated MRI10 Aug
  6. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstruction12 Jul
  7. 2022Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models3 Feb
  8. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov
  9. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 20

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  4. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  5. 2022Denoising Diffusion Restoration Modelscited by 963
  6. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Modelcited by 583
  7. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
  8. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 387
  9. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 342
  10. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 286
  11. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 202
  12. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problemscited by 132
8 more
  1. 2019GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstructioncited by 45
  2. 2023Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidancecited by 43
  3. 2018A Machine-learning framework for automatic reference-free quality assessment in MRIcited by 10
  4. 202120-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstructioncited by 9
  5. 2024Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRIcited by 4
  6. 2024Non-Reference Quality Assessment for Medical Imaging: Application to Synthetic Brain MRIscited by 4
  7. 2020Optimization of hyperparameters for SMS reconstructioncited by 1
  8. 2022Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Mapscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024LDPM: Towards undersampled MRI reconstruction with MR-VAE and Latent Diffusion Priorcited by 0
  2. 2026Back to Physics: Operator-Guided Generative Paths for SMS MRI Reconstructioncited by 0

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  1. 2023Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
  2. 2022Universal Generative Modeling in Dual-domain for Dynamic MR Imaging
  3. 2025K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging
  4. 2024Learning the Domain Specific Inverse NUFFT for Accelerated Spiral MRI using Diffusion Models
  5. 2025ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning
  6. 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
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  8. 2026Consistency Models for Fast MRI Reconstruction Using Regularization by Denoising