同时多层（SMS）成像与面内欠采样叠加时，重建需从高度折叠且欠采样的K空间中同时完成解折叠与去混叠．针对现有扩散模型重建中数据一致性以固定强度施加、且预训练先验难以自适应当次扫描的问题，提出一种结合几何门控数据一致性与测试时低秩自适应的SMS重建方法．该方法在每一反向采样步中，将数据一致性修正量相对去噪器局部切空间分解为切向与法向分量，切向分量完整保留、法向分量经自适应门控注入，以抑制离流形伪影；并在得分网络中并联低秩旁支，冻结主干权重，仅以测量一致性损失在推理阶段做少步微调．基于fastMRI脑部T₂加权数据的仿真实验表明，该方法在全部条件下均优于5种对比方法，峰值信噪比相对于次优方法提升2.27~5.45 dB，且随加速倍数增大的性能退化更为平缓．

When simultaneous multi-slice (SMS) imaging is combined with in-plane undersampling, reconstruction must unfold superimposed slices and remove aliasing simultaneously from a highly folded, undersampled K-space. To address the fixed-strength data consistency and the prior-scan mismatch in existing diffusion-based reconstructions, an SMS reconstruction method combining geometry-gated data consistency with test-time low-rank adaptation is proposed. At each reverse sampling step, the data-consistency correction is decomposed with respect to the local tangent space of the denoiser: the tangential component is retained in full, whereas the normal component is admitted through an adaptive gate, suppressing off-manifold artefacts. Low-rank branches are further attached to the score network and updated at inference using a measurement-consistency loss alone, with the backbone frozen. Experiments on simulated fastMRI brain T₂-weighted data show that the proposed method outperforms five comparison methods under all conditions, with PSNR gains of 2.27~5.45 dB over the second-best method and a more graceful degradation as acceleration increases.