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

• URL: http://arxiv.org/abs/2602.07820v1
• Date: Sun, 08 Feb 2026 04:57:39 GMT
• Title: Back to Physics: Operator-Guided Generative Paths for SMS MRI Reconstruction
• Authors: Zhibo Chen, Yu Guan, Yajuan Huang, Chaoqi Chen, XiangJi, Qiuyun Fan, Dong Liang, Qiegen Liu,
• Abstract summary: Simultaneous multi-slice (SMS) imaging with in-plane undersampling enables highly accelerated MRI.<n>We propose an operator-guided framework that models the degradation trajectory using known acquisition operators.<n>Within this framework, we introduce an operator-conditional dual-stream interaction network (OCDI-Net) that explicitly disentangles target-slice content from inter-slice interference.
• Score: 30.36786801853506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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