Shou Qinyang, Zhao Chenyang, Shao Xingfeng, Herting Megan M, Wang Danny Jj
Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States.
Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, United States.
Neuroimage. 2025 Mar;308:121070. doi: 10.1016/j.neuroimage.2025.121070. Epub 2025 Jan 30.
Choroid plexus (CP) is an important brain structure that produces cerebrospinal fluid (CSF). CP perfusion has been studied using multi-delay arterial spin labeling (MD-ASL) in adults but not in pediatric populations due to the challenge of small CP size in children. Here we present a high resolution (iso2 mm) MDASL protocol with 10-minute scan time and performed test-retest scans on 21 typically developing children aged 8 to 17 years. We further proposed a Transformer-based deep learning (DL) model with k-space weighted image average (KWIA) denoised images as reference for training the model. The performance of the model was evaluated by the SNR, bias and repeatability of the fitted perfusion parameters of the CP and gray matter. The proposed method was compared to several benchmark methods including KWIA, joint denoising and reconstruction with total generalized variation (TGV) regularization, as well as another self-supervised method termed Noise2Void. The results show that the proposed Transformer model with KWIA reference can effectively denoise multi-delay ASL images, not only improving the SNR for perfusion images of each delay, but also improving the SNR for the fitted perfusion maps for visualizing and quantifying CP perfusion in children. This may facilitate the use of MDASL in neurodevelopmental studies to characterize the development of CP and glymphatic system.
脉络丛（CP）是产生脑脊液（CSF）的重要脑结构。在成人中，已使用多延迟动脉自旋标记（MD - ASL）对CP灌注进行了研究，但由于儿童CP尺寸较小带来的挑战，尚未在儿科人群中进行研究。在此，我们提出了一种扫描时间为10分钟的高分辨率（等体素2毫米）MDASL方案，并对21名8至17岁的典型发育儿童进行了重测扫描。我们还提出了一种基于Transformer的深度学习（DL）模型，该模型以k空间加权图像平均（KWIA）去噪图像作为训练模型的参考。通过CP和灰质拟合灌注参数的信噪比（SNR）、偏差和重复性来评估模型的性能。将所提出的方法与几种基准方法进行了比较，包括KWIA、采用全广义变分（TGV）正则化的联合去噪和重建，以及另一种称为Noise2Void的自监督方法。结果表明，所提出的具有KWIA参考的Transformer模型可以有效地对多延迟ASL图像进行去噪，不仅提高了每个延迟灌注图像的SNR，还提高了用于可视化和量化儿童CP灌注的拟合灌注图的SNR。这可能有助于在神经发育研究中使用MDASL来表征CP和类淋巴系统的发育。
