用自监督深度学习从普通MRI生成定量参数图，克服设备差异影响。
Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: applications to a large-scale, clinically heterogeneous dataset
- 基于磁共振物理模型的自监督学习，直接从常规MRI推导T1/T2/PD图
- 在4121次扫描上验证，各组织参数与文献值一致，跨设备变异小于1.1%
- 生成图对设备和序列变化不敏感，适合大规模临床研究使用
磁共振成像（MRI）是临床神经影像的核心，但常规MRI提供的是依赖设备和扫描参数的定性信息。虽然定量MRI（qMRI）能获取组织本征参数，但其需专用采集协议和重建算法，限制了应用并阻碍大规模生物标志物研究。本研究提出一种自监督物理引导的深度学习框架，可直接从常见的临床常规T1加权、T2加权及FLAIR MRI中推导出定量T1、T2和质子密度（PD）图。该框架在包含4,121次扫描会话的大规模、临床异质数据集上训练与评估，覆盖六年期间在四种3T MRI设备上采集的数据，真实反映临床变异性。框架将基于Bloch方程的信号模型嵌入训练目标。在超过600次测试会话中，生成图的白质和灰质参数值与文献范围一致。生成图对设备硬件和采集协议组具有不变性，组间变异系数≤1.1%。个体层面分析显示，跨设备和序列参数的体素级重现性极佳，T1和T2的皮尔逊相关系数与一致性相关系数均超过0.82，平均相对体素差异低，尤其T2低于6%。结果表明，该框架能鲁棒地将多样化的临床常规MRI数据转化为定量图，有望推动大规模定量生物标志物研究。
原文摘要 · Abstract (English)
Magnetic resonance imaging (MRI) is a cornerstone of clinical neuroimaging, yet conventional MRIs provide qualitative information heavily dependent on scanner hardware and acquisition settings. While quantitative MRI (qMRI) offers intrinsic tissue parameters, the requirement for specialized acquisition protocols and reconstruction algorithms restricts its availability and impedes large-scale biomarker research. This study presents a self-supervised physics-guided deep learning framework to infer quantitative T1, T2, and proton-density (PD) maps directly from widely available clinical conventional T1-weighted, T2-weighted, and FLAIR MRIs. The framework was trained and evaluated on a large-scale, clinically heterogeneous dataset comprising 4,121 scan sessions acquired at our institution over six years on four different 3 T MRI scanner systems, capturing real-world clinical variability. The framework integrates Bloch-based signal models directly into the training objective. Across more than 600 test sessions, the generated maps exhibited white matter and gray matter values consistent with literature ranges. Additionally, the generated maps showed invariance to scanner hardware and acquisition protocol groups, with inter-group coefficients of variation $\leq$ 1.1%. Subject-specific analyses demonstrated excellent voxel-wise reproducibility across scanner systems and sequence parameters, with Pearson $r$ and concordance correlation coefficients exceeding 0.82 for T1 and T2. Mean relative voxel-wise differences were low across all quantitative parameters, especially for T2 ($<$ 6%). These results indicate that the proposed framework can robustly transform diverse clinical conventional MRI data into quantitative maps, potentially paving the way for large-scale quantitative biomarker research.
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