用深度学习提升磁共振弹性成像剪切模量估计的精度与抗噪能力。
Deep Learning-Driven Inversion Framework for Shear Modulus Estimation in Magnetic Resonance Elastography (DIME)
- 基于有限元仿真生成的位移场-刚度图对，训练小块图像的深度网络以捕捉局部波行为。
- 在仿真和真实肝脏数据上，新方法相关系数达0.99，显著优于传统MMDI方法。
- 适合临床肝病弹性成像，尤其在噪声干扰下仍能保持稳定可靠的刚度分布。
磁共振弹性成像（MRE）中广泛使用的多模态直接反演（MMDI）算法依赖于亥姆霍兹方程，假设波在均匀、无限介质中传播，且使用拉普拉斯算子，对噪声敏感，影响刚度估计的准确性。本文提出深度学习驱动的剪切模量估计反演框架（DIME），通过有限元建模（FEM）生成的位移场-刚度图对进行训练，并在小图像块上学习以捕捉局部波特性并增强对全局图像变化的鲁棒性。在均匀与非均匀的FEM仿真数据上，DIME生成的刚度图像素间变异性低、边界清晰，与真实值相关性高于MMDI。在包含解剖结构的真实肝脏仿真数据中，DIME与真实值相关系数为0.99，决定系数为0.98，而MMDI存在明显低估。在8名健康者和7名纤维化患者的真实体内肝脏MRE数据上，DIME保持生理一致的刚度模式，与MMDI结果接近，但后者呈现方向性偏差。整体表明DIME在临床应用中具有可行性。
原文摘要 · Abstract (English)
The Multimodal Direct Inversion (MMDI) algorithm is widely used in Magnetic Resonance Elastography (MRE) to estimate tissue shear stiffness. However, MMDI relies on the Helmholtz equation, which assumes wave propagation in a uniform, homogeneous, and infinite medium. Furthermore, the use of the Laplacian operator makes MMDI highly sensitive to noise, which compromises the accuracy and reliability of stiffness estimates. In this study, we propose the Deep-Learning driven Inversion Framework for Shear Modulus Estimation in MRE (DIME), aimed at enhancing the robustness of inversion. DIME is trained on the displacement fields-stiffness maps pair generated through Finite Element Modelling (FEM) simulations. To capture local wave behavior and improve robustness to global image variations, DIME is trained on small image patches. We first validated DIME using homogeneous and heterogeneous datasets simulated with FEM, where DIME produced stiffness maps with low inter-pixel variability, accurate boundary delineation, and higher correlation with ground truth (GT) compared to MMDI. Next, DIME was evaluated in a realistic anatomy-informed simulated liver dataset with known GT and compared directly to MMDI. DIME reproduced ground-truth stiffness patterns with high fidelity (r = 0.99, R^2 = 0.98), while MMDI showed greater underestimation. After validating DIME on synthetic data, we tested the model in in vivo liver MRE data from eight healthy and seven fibrotic subjects. DIME preserved physiologically consistent stiffness patterns and closely matched MMDI, which showed directional bias. Overall, DIME showed higher correlation with ground truth and visually similar stiffness patterns, whereas MMDI displayed a larger bias that can potentially be attributed to directional filtering. These preliminary results highlight the feasibility of DIME for clinical applications in MRE.
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