用物理模型指导生成路径，提升加速MRI重建精度。
Back to Physics: Operator-Guided Generative Paths for SMS MRI Reconstruction
- 基于已知成像算子建模退化过程，实现确定性反演。
- 在fastMRI和体内扩散MRI数据上，减少切片串扰并提升图像保真度。
- 适合需要高精度切片分离的快速MRI研究者。
平面内欠采样的同时多层（SMS）成像可实现高度加速的MRI，但导致强耦合的逆问题，包含确定性的层间干扰和缺失的k空间数据。现有基于扩散的方法通常以高斯噪声污染为前提，需额外一致性步骤引入SMS物理特性，易与实际操作算子造成的退化不匹配。本文提出一种算子引导框架，利用已知采集算子建模退化轨迹，并通过确定性更新进行反演。在此框架中，设计算子条件双流交互网络（OCDI-Net），显式解耦目标层内容与层间干扰，预测结构化退化以实现算子对齐反演；并将重建定义为两阶段链式推理：先完成层间分离，再进行平面内补全。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，相比传统与学习型方法，本方法显著提升图像保真度并降低切片泄漏。
原文摘要 · Abstract (English)
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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