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Back to Physics: Operator-Guided Generative Paths for SMS MRI Reconstruction

arXiv 2602.07820 ↗First posted 8 Feb 2026cs.CVcited by 0

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Abstract (arXiv)

Simultaneous multi-slice (SMS) imaging with in-plane undersampling enables highly accelerated MRI but yields a strongly coupled inverse problem with deterministic inter-slice interference and missing k-space data. Most diffusion-based reconstructions are formulated around Gaussian-noise corruption and rely on additional consistency steps to incorporate SMS physics, which can be mismatched to the operator-governed degradations in SMS acquisition. We propose an operator-guided framework that models the degradation trajectory using known acquisition operators and inverts this process via deterministic updates. Within this framework, we introduce an operator-conditional dual-stream interaction network (OCDI-Net) that explicitly disentangles target-slice content from inter-slice interference and predicts structured degradations for operator-aligned inversion, and we instantiate reconstruction as a two-stage chained inference procedure that performs SMS slice separation followed by in-plane completion. Experiments on fastMRI brain data and prospectively acquired in vivo diffusion MRI data demonstrate improved fidelity and reduced slice leakage over conventional and learning-based SMS reconstructions.

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References · 17

  1. 2017Attention Is All You Needcited by 60,361
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  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2020Denoising Diffusion Implicit Modelscited by 9,781
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  6. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
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  10. 2021Score-based diffusion models for accelerated MRIcited by 311
  11. 2022Cold Diffusion: Inverting Arbitrary Image Transforms Without Noisecited by 276
  12. 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 232
5 more
  1. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 202
  2. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 118
  3. 2024Robust Simultaneous Multislice MRI Reconstruction Using Slice-Wise Learned Generative Diffusion Priorscited by 2
  4. 2023Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstructioncited by 1
  5. 2025K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imagingcited by 1

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