Denoising arterial spin labeling perfusion MRI with deep machine learning.
Denoising arterial spin labeling perfusion MRI with deep machine learning.
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DOI：
10.1016/j.mri.2020.01.005
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发表时间：
2020-05
影响因子：
2.5
通讯作者：
Wang Z
中科院分区：
文献类型：
--
作者：
Xie D;Li Y;Yang H;Bai L;Wang T;Zhou F;Zhang L;Wang Z
动脉自旋标记（ASL）灌注MRI是一种无创的定量测量脑血流量（CBF）的技术。由于固有的低信噪比（SNR），ASL MRI的技术挑战是数据处理。深度学习（DL）是一种新兴的机器学习技术，它可以在不使用任何显式假设的情况下从获取的数据中学习非线性变换。这样的高灵活性对于ASL去噪可以是特别有益的。本文提出并验证了一种基于DL的ASL MRI去噪算法（DL-ASL）。DL-ASL网络是使用卷积神经网络（CNN）构建的，具有扩张卷积和宽激活残差块，以显式地考虑体素间的相关性，并在模型学习期间保持输入图像的空间分辨率。DL-ASL在SNR方面显著改善了ASL CBF的质量。基于回顾性分析，DL-ASL显示出在不牺牲CBF测量质量的情况下减少75%原始采集时间的高潜力。与目前的常规方法相比，DL-ASL在更高的PSNR、SSIM和放射学评分方面实现了ASL MRI的去噪性能改善。在DL-ASL的帮助下，可以在ASL MRI中规定更少的重复，从而大大减少总采集时间。
Arterial spin labeling (ASL) perfusion MRI is a noninvasive technique for measuring cerebral blood flow (CBF) in a quantitative manner. A technical challenge in ASL MRI is data processing because of the inherently low signal-to-noise-ratio (SNR). Deep learning (DL) is an emerging machine learning technique that can learn a nonlinear transform from acquired data without using any explicit hypothesis. Such a high flexibility may be particularly beneficial for ASL denoising. In this paper, we proposed and validated a DL-based ASL MRI denoising algorithm (DL-ASL). The DL-ASL network was constructed using convolutional neural networks (CNNs) with dilated convolution and wide activation residual blocks to explicitly take the inter-voxel correlations into account, and preserve spatial resolution of input image during model learning. DL-ASL substantially improved the quality of ASL CBF in terms of SNR. Based on retrospective analyses, DL-ASL showed a high potential of reducing 75% of the original acquisition time without sacrificing CBF measurement quality. DL-ASL achieved improved denoising performance for ASL MRI as compared with current routine methods in terms of higher PSNR, SSIM and Radiologic scores. With the help of DL-ASL, much fewer repetitions may be prescribed in ASL MRI, resulting in a great reduction of the total acquisition time.