体素级监督模型在IVIM MRI中不确定性量化的综合框架
A Comprehensive Framework for Uncertainty Quantification of Voxel-wise Supervised Models in IVIM MRI
AI总结：
提出基于深度集成混合密度网络的概率框架，用于IVIM参数估计的不确定性量化，分解偶然与认知不确定性，经模拟和体内数据验证，提升校准性并识别不可靠估计。
摘要：
从弥散加权MRI中准确估计体素内不相干运动（IVIM）参数仍然具有挑战性，这归因于逆问题的不适定性以及对噪声的高敏感性，尤其是在灌注分量中。在本工作中，我们提出了一种基于混合密度网络（MDNs）的深度集成（DE）的概率深度学习框架，能够估计总预测不确定性并将其分解为偶然不确定性（AU）和认知不确定性（EU）分量。该方法与非概率神经网络、贝叶斯拟合方法以及具有单高斯参数化的概率网络进行了基准比较。监督训练在合成数据上进行，评估在模拟数据集和体内数据集上开展。量化不确定性的可靠性通过校准曲线、输出分布锐度以及连续排名概率分数（CRPS）进行评估。MDN为扩散系数D和分数f参数生成了更校准且更锐利的预测分布，尽管在伪扩散系数D*上观察到轻微过度自信。稳健变异系数（RCV）表明，与高斯模型相比，MDN在体内D*估计上更平滑。尽管训练数据覆盖了预期的生理范围，但体内较高的EU表明与实际采集条件不匹配，这凸显了纳入EU的重要性，而DE允许这一点。总体而言，我们提出了一个用于IVIM拟合且带有不确定性量化的综合框架，该框架能够识别和解释不可靠的估计。所提出的方法也可通过适当的架构和模拟调整用于拟合其他物理模型。
英文摘要：
Accurate estimation of intravoxel incoherent motion (IVIM) parameters from diffusion-weighted MRI remains challenging due to the ill-posed nature of the inverse problem and high sensitivity to noise, particularly in the perfusion compartment. In this work, we propose a probabilistic deep learning framework based on Deep Ensembles (DE) of Mixture Density Networks (MDNs), enabling estimation of total predictive uncertainty and decomposition into aleatoric (AU) and epistemic (EU) components. The method was benchmarked against non probabilistic neural networks, a Bayesian fitting approach and a probabilistic network with single Gaussian parametrization. Supervised training was performed on synthetic data, and evaluation was conducted on both simulated and an in vivo dataset. The reliability of the quantified uncertainties was assessed using calibration curves, output distribution sharpness, and the Continuous Ranked Probability Score (CRPS). MDNs produced more calibrated and sharper predictive distributions for the diffusion coefficient D and fraction f parameters, although slight overconfidence was observed in pseudo-diffusion coefficient D*. The Robust Coefficient of Variation (RCV) indicated smoother in vivo estimates for D* with MDNs compared to Gaussian model. Despite the training data covering the expected physiological range, elevated EU in vivo suggests a mismatch with real acquisition conditions, highlighting the importance of incorporating EU, which was allowed by DE. Overall, we present a comprehensive framework for IVIM fitting with uncertainty quantification, which enables the identification and interpretation of unreliable estimates. The proposed approach can also be adopted for fitting other physical models through appropriate architectural and simulation adjustments.
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