论文标题
p2t2：一种鲁棒$ t_ {2} $从定量$ t_ {2} $ - 加权MRI的分发估算的物理深神经网络方法
P2T2: a Physically-primed deep-neural-network approach for robust $T_{2}$ distribution estimation from quantitative $T_{2}$-weighted MRI
论文作者
论文摘要
估计来自多回波$ T_2 $加权MRI（$ T_2W $）数据的$ T_2 $放松时间分布数据可以为评估各种病理学的炎症，脱髓鞘，水肿和软骨组成提供宝贵的生物标志物，包括神经退行性疾病，骨质疾病，骨质炎和骨质炎和tumerthlistiation和tumors和Tumors。已经提出了基于深神经网络（DNN）的方法来解决MRI数据中估算$ T_2 $分布的复杂反问题，但对于具有低信噪比（SNR）的临床数据，它们尚未足够强大，并且对诸如Echo-Times（TE）的变量（TE）的分配变化高度敏感。因此，他们的应用在临床实践和具有异质获取方案的大规模多机构试验中受到阻碍。我们提出了一种物理培养的DNN方法，称为$ P_2T_2 $，除了将MRI信号带入DNN体系结构外，还将信号衰变向前模型包含，以提高$ T_2 $分布估计的准确性和鲁棒性。与基于DNN的方法和经典方法相比，我们评估了$ P_2T_2 $模型，使用1D和2D数值模拟以及临床数据，用于$ T_2 $分布估算的经典方法。我们的模型提高了在临床环境中常见的低SNR水平（$ SNR <80 $）的基线模型的准确性。此外，与先前提出的DNN模型相比，我们的模型实现了$ \ sim $ 35 \％的鲁棒性，以提高了采集过程中的分配变化。最后，我们的$ P_2T_2 $型号与基线方法相比，在应用于真实的人类MRI数据时，产生最详细的髓水分数图。我们的$ P_2T_2 $模型提供了一种从MRI数据中估算$ T_2 $发行版的可靠和精确的方法，并显示出有望在大规模的多机构试验中使用，并具有异质性收购协议。
Estimating $T_2$ relaxation time distributions from multi-echo $T_2$-weighted MRI ($T_2W$) data can provide valuable biomarkers for assessing inflammation, demyelination, edema, and cartilage composition in various pathologies, including neurodegenerative disorders, osteoarthritis, and tumors. Deep neural network (DNN) based methods have been proposed to address the complex inverse problem of estimating $T_2$ distributions from MRI data, but they are not yet robust enough for clinical data with low Signal-to-Noise ratio (SNR) and are highly sensitive to distribution shifts such as variations in echo-times (TE) used during acquisition. Consequently, their application is hindered in clinical practice and large-scale multi-institutional trials with heterogeneous acquisition protocols. We propose a physically-primed DNN approach, called $P_2T_2$, that incorporates the signal decay forward model in addition to the MRI signal into the DNN architecture to improve the accuracy and robustness of $T_2$ distribution estimation. We evaluated our $P_2T_2$ model in comparison to both DNN-based methods and classical methods for $T_2$ distribution estimation using 1D and 2D numerical simulations along with clinical data. Our model improved the baseline model's accuracy for low SNR levels ($SNR<80$) which are common in the clinical setting. Further, our model achieved a $\sim$35\% improvement in robustness against distribution shifts in the acquisition process compared to previously proposed DNN models. Finally, Our $P_2T_2$ model produces the most detailed Myelin-Water fraction maps compared to baseline approaches when applied to real human MRI data. Our $P_2T_2$ model offers a reliable and precise means of estimating $T_2$ distributions from MRI data and shows promise for use in large-scale multi-institutional trials with heterogeneous acquisition protocols.
