用于定量磁共振成像的基于自监督深度学习的Rician似然损失
Rician likelihood loss for quantitative MRI using self-supervised deep learning
AI总结：
针对自监督深度学习定量MRI在低信噪比下参数估计偏差的问题，提出数值稳定的负对数Rician似然损失，替代MSE损失，在5-30信噪比范围内提升ADC和IVIM扩散系数估计准确性。
摘要：
目的：先前使用自监督深度学习进行定量磁共振成像的研究报告了在低信噪比（SNR）下参数估计存在偏差。这种系统性误差源于网络训练中使用的均方误差（MSE）损失函数的选择，该函数与Rician分布的磁共振幅度信号不兼容。为解决此问题，我们引入了负对数Rician似然（NLR）损失。方法：我们开发了一种数值稳定且精确的NLR损失实现，用于估计表观扩散系数（ADC）模型和体素内不相干运动（IVIM）模型的定量参数。参数估计的准确性、精确性和总体误差通过偏差、方差和均方根误差进行评估，并在信噪比范围（5-30）内与MSE损失进行比较。结果：随着信噪比降低，使用NLR损失训练的网络在ADC和IVIM扩散系数的估计准确性上均高于使用MSE损失的网络，且精度或总体误差的损失极小。在高有效信噪比（高信噪比和小扩散系数）下，两种损失在两种模型的所有参数上均表现出相当的准确性和精确性。结论：所提出的NLR损失在测试的整个信噪比范围内数值稳定且准确，并提高了使用自监督深度学习进行扩散系数参数估计的准确性。我们预期这一发展将广泛惠及定量磁共振成像技术，使得从噪声数据中实现更准确的参数估计成为可能。
英文摘要：
Purpose: Previous quantitative MR imaging studies using self-supervised deep learning have reported biased parameter estimates at low SNR. Such systematic errors arise from the choice of Mean Squared Error (MSE) loss function for network training, which is incompatible with Rician-distributed MR magnitude signals. To address this issue, we introduce the negative log Rician likelihood (NLR) loss. Methods: A numerically stable and accurate implementation of the NLR loss was developed to estimate quantitative parameters of the apparent diffusion coefficient (ADC) model and intra-voxel incoherent motion (IVIM) model. Parameter estimation accuracy, precision and overall error were evaluated in terms of bias, variance and root mean squared error and compared against the MSE loss over a range of SNRs (5 - 30). Results: Networks trained with NLR loss show higher estimation accuracy than MSE for the ADC and IVIM diffusion coefficients as SNR decreases, with minimal loss of precision or total error. At high effective SNR (high SNR and small diffusion coefficients), both losses show comparable accuracy and precision for all parameters of both models. Conclusion: The proposed NLR loss is numerically stable and accurate across the full range of tested SNRs and improves parameter estimation accuracy of diffusion coefficients using self-supervised deep learning. We expect the development to benefit quantitative MR imaging techniques broadly, enabling more accurate parameter estimation from noisy data.
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