Diffusion MRI enables non-invasive probing of tissue microstructure, but accurate parameter estimation is challenged by noise-related effects. In supervised machine learning frameworks trained on simulated data, discrepancies between the noise characteristics of simulated and acquired signals introduce a form of covariate shift, whereby the input signal distribution differs between training and inference. We investigated the impact of this mismatch on microstructure parameter estimation and propose a realistic noise synthesis (RNS) framework to mitigate it. RNS incorporates both the Rician expectation and the effective post-processing noise variance into simulated training signals. The Rician expectation was modelled using a noise standard deviation estimated with MPPCA, while the effective standard deviation was derived from spherical harmonic residuals of preprocessed data. The method was evaluated using the cylinder-zeppelin and the SANDI models on simulated datasets across multiple SNR levels and on in vivo diffusion data with repeated acquisitions. Sensitivity to noise misestimation was also assessed. Ignoring magnitude-induced noise effects during training produced systematic, SNR-dependent parameter bias, particularly at low SNR. Incorporating the Rician expectation substantially reduced bias to the level of noise-aware nonlinear least-squares fitting. Modelling the effective standard deviation further improved precision. Performance was largely independent of regression architecture but sensitive to accurate noise estimation. These findings demonstrate that realistic noise modelling in simulated training data mitigates signal-domain covariate shift and is essential for unbiased supervised microstructure estimation, particularly in low-SNR regimes associated with high b-values or high spatial resolution.

翻译：扩散磁共振成像能够无创探测组织微结构，但准确参数估计面临噪声相关效应的挑战。在基于模拟数据训练的监督学习框架中，模拟信号与采集信号之间噪声特性的差异会引入协变量偏移，导致训练与推理阶段输入信号分布不一致。我们研究了这种失配对微结构参数估计的影响，并提出一种真实噪声合成框架以缓解该问题。RNS将莱斯期望与有效后处理噪声方差同时纳入模拟训练信号：利用MPPCA估计的噪声标准差建模莱斯期望，基于预处理数据的球谐残差推导有效标准差。通过圆柱-zeppelin模型和SANDI模型，在多个信噪比水平的模拟数据集及重复采集的活体扩散数据上评估该方法，并检验对噪声估计误差的敏感性。训练阶段忽略幅度诱导的噪声效应会导致系统性、信噪比依赖的参数偏差，尤其在低SNR条件下。引入莱斯期望可显著将偏差降低至噪声感知非线性最小二乘拟合的水平；进一步建模有效标准差则能提升参数估计精度。模型性能对回归架构基本不敏感，但对噪声估计的精确性高度依赖。这些结果表明，在模拟训练数据中实现真实噪声建模可缓解信号域的协变量偏移，这对于实现无偏监督微结构估计至关重要，尤其适用于高b值或高空间分辨率对应的低SNR场景。