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.

翻译：磁共振成像（MRI）是临床神经影像学的基石，然而传统MRI提供的定性信息高度依赖于扫描仪硬件和采集设置。尽管定量MRI（qMRI）能够提供本征组织参数，但其对专用采集协议和重建算法的要求限制了其可用性，并阻碍了大规模生物标志物研究。本研究提出了一种自监督物理引导深度学习框架，可直接从广泛可用的临床传统T1加权、T2加权和FLAIR MRI图像中推断定量T1、T2和质子密度（PD）图。该框架在一个大规模、临床异质性数据集上进行了训练和评估，该数据集包含我院六年间在四台不同3 T MRI扫描仪系统上采集的4,121次扫描会话，捕捉了真实世界的临床变异性。该框架将基于Bloch方程的信号模型直接整合到训练目标中。在超过600次测试会话中，生成的图谱显示白质和灰质数值与文献报道范围一致。此外，生成的图谱对扫描仪硬件和采集协议组表现出不变性，组间变异系数 $\leq$ 1.1%。个体特异性分析显示，在不同扫描仪系统和序列参数下，图谱具有优异的体素级可重复性，T1和T2的Pearson $r$ 和一致性相关系数均超过0.82。所有定量参数的平均相对体素级差异均较低，尤其是T2（$<$ 6%）。这些结果表明，所提出的框架能够稳健地将多样化的临床传统MRI数据转化为定量图谱，可能为大规模定量生物标志物研究铺平道路。