标题： 基于神经控制微分方程的定量MRI参数估计的独立采集深度学习 显示英文标题

标题： Acquisition-Independent Deep Learning for Quantitative MRI Parameter Estimation using Neural Controlled Differential Equations

摘要： 深度学习已被证明是用于各种定量磁共振成像（QMRI）模型中参数估计的一种合适替代方法，而不是最小二乘（LSQ）拟合。 然而，当前的深度学习实现对磁共振采集协议的变化不够稳健。 在实践中，不同研究和临床环境中的QMRI采集协议存在显著差异。 当前深度学习方法在QMRI参数估计中的泛化能力和可采用性不足，阻碍了这些算法在临床试验和临床实践中的应用。 神经控制微分方程（NCDEs）允许对长度可变的不完整和不规则采样数据进行采样，使其非常适合用于QMRI参数估计。 在本研究中，我们表明NCDEs可以作为通用工具，无论QMRI序列长度、自变量配置和QMRI前向模型（可变翻转角T1映射、体素内不相干运动MRI、动态对比增强MRI）如何，都能准确预测QMRI参数。 在低信噪比（SNR）模拟和腹部和腿部等具有挑战性的解剖区域的体内实验中，NCDEs的均方误差低于LSQ拟合，但在高SNR下这种改进不再明显。 NCDEs在不增加偏差的情况下减少了估计误差的四分位距范围，尤其是在高不确定性条件下。 这些发现表明，NCDEs为可靠的QMRI参数估计提供了一种稳健的方法，特别是在高不确定性和低图像质量的场景中。 我们认为，通过NCDEs，我们已经解决了在更广泛的临床和研究环境中使用深度学习进行QMRI参数估计的主要挑战之一。 显示英文摘要

摘要： Deep learning has proven to be a suitable alternative to least-squares (LSQ) fitting for parameter estimation in various quantitative MRI (QMRI) models. However, current deep learning implementations are not robust to changes in MR acquisition protocols. In practice, QMRI acquisition protocols differ substantially between different studies and clinical settings. The lack of generalizability and adoptability of current deep learning approaches for QMRI parameter estimation impedes the implementation of these algorithms in clinical trials and clinical practice. Neural Controlled Differential Equations (NCDEs) allow for the sampling of incomplete and irregularly sampled data with variable length, making them ideal for use in QMRI parameter estimation. In this study, we show that NCDEs can function as a generic tool for the accurate prediction of QMRI parameters, regardless of QMRI sequence length, configuration of independent variables and QMRI forward model (variable flip angle T1-mapping, intravoxel incoherent motion MRI, dynamic contrast-enhanced MRI). NCDEs achieved lower mean squared error than LSQ fitting in low-SNR simulations and in vivo in challenging anatomical regions like the abdomen and leg, but this improvement was no longer evident at high SNR. NCDEs reduce estimation error interquartile range without increasing bias, particularly under conditions of high uncertainty. These findings suggest that NCDEs offer a robust approach for reliable QMRI parameter estimation, especially in scenarios with high uncertainty or low image quality. We believe that with NCDEs, we have solved one of the main challenges for using deep learning for QMRI parameter estimation in a broader clinical and research setting.