T1 mapping is a quantitative magnetic resonance imaging (qMRI) technique that has emerged as a valuable tool in the diagnosis of diffuse myocardial diseases. However, prevailing approaches have relied heavily on breath-hold sequences to eliminate respiratory motion artifacts. This limitation hinders accessibility and effectiveness for patients who cannot tolerate breath-holding. Image registration can be used to enable free-breathing T1 mapping. Yet, inherent intensity differences between the different time points make the registration task challenging. We introduce PCMC-T1, a physically-constrained deep-learning model for motion correction in free-breathing T1 mapping. We incorporate the signal decay model into the network architecture to encourage physically-plausible deformations along the longitudinal relaxation axis. We compared PCMC-T1 to baseline deep-learning-based image registration approaches using a 5-fold experimental setup on a publicly available dataset of 210 patients. PCMC-T1 demonstrated superior model fitting quality (R2: 0.955) and achieved the highest clinical impact (clinical score: 3.93) compared to baseline methods (0.941, 0.946 and 3.34, 3.62 respectively). Anatomical alignment results were comparable (Dice score: 0.9835 vs. 0.984, 0.988). Our code and trained models are available at https://github.com/eyalhana/PCMC-T1.

翻译：T1 mapping是一种定量磁共振成像（qMRI）技术，已成为诊断弥漫性心肌疾病的重要工具。然而，现有方法严重依赖屏气序列来消除呼吸运动伪影。这一局限性限制了无法耐受屏气患者的可及性和有效性。图像配准可用于实现自由呼吸T1 mapping，但不同时间点间固有的强度差异使配准任务具有挑战性。我们提出PCMC-T1，一种用于自由呼吸T1 mapping运动校正的物理约束深度学习模型。通过将信号衰减模型融入网络架构，该方法能沿纵向弛豫轴生成符合物理规律的形变。采用210名患者的公开数据集，通过五折交叉实验设置，我们将PCMC-T1与基于深度学习的基线图像配准方法进行比较。PCMC-T1展现出更优的模型拟合质量（R²：0.955），并取得最高临床影响评分（临床评分：3.93），优于基线方法（分别对应0.941、0.946和3.34、3.62）。解剖对齐结果具有可比性（Dice系数：0.9835对比0.984、0.988）。我们的代码与训练模型已在https://github.com/eyalhana/PCMC-T1 公开。