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.

翻译：同步多层（SMS）成像结合平面内欠采样可实现高度加速的磁共振成像，但会形成一个强耦合的逆问题，其中存在确定性的层间干扰以及缺失的k空间数据。大多数基于扩散模型的重建方法围绕高斯噪声退化构建，并依赖额外的保真度约束步骤来融入SMS物理机制，这可能与SMS采集中由算子主导的退化过程不匹配。我们提出了一种算子引导的框架，该框架利用已知的采集算子对退化轨迹进行建模，并通过确定性更新来逆转此过程。在此框架内，我们引入了一种算子条件双流交互网络（OCDI-Net），它显式地将目标层内容与层间干扰解耦，并预测结构化退化以实现算子对齐的反演；同时，我们将重建实例化为一个两阶段链式推理过程，依次执行SMS层分离与平面内补全。在fastMRI脑部数据及前瞻性采集的体内扩散MRI数据上的实验表明，相较于传统及基于学习的SMS重建方法，本方法在保真度上有所提升，并减少了层间泄漏。