论文标题
通过深度学习从多个标签后延迟动脉自旋标记的MRI加速大脑血流和动脉传输时间地图估计
Acceleration of cerebral blood flow and arterial transit time maps estimation from multiple post-labeling delay arterial spin-labeled MRI via deep learning
论文作者
论文摘要
目的：动脉自旋标记（ASL）灌注成像指示大脑血流（CBF）的直接和绝对测量。动脉转运时间（ATT）是一个相关的生理参数，反映了标记的旋转到达感兴趣的大脑区域的持续时间。多个标签后延迟（PLD）可以提供CBF和ATT的强大度量，从而可以根据ATT优化区域CBF建模。延长的获取时间可以潜在地降低CBF和ATT估计的质量和准确性。我们提出了一个新型网络，以显着减少具有较高信噪比（SNR）的PLD数量。方法：对一个PLD和两个PLDS-SEPA-列表进行了CBF和ATT估计。对每个模型进行独立训练，以学习从灌注加权图像（PWI）到CBF和ATT图像的非线性转化。结果：One-PLD和两个PLD模型在CBF上的视觉上优于常规方法，而两-PLD模型在ATT估计上显示出更准确的结构。所提出的方法将PLD的数量从ATT上的6个降低到2个，甚至在CBF上的单个PLD中，而无需牺牲SNR。结论：使用高质量的深度学习来生成CBF和ATT图以减少PLD是可行的。
Purpose: Arterial spin labeling (ASL) perfusion imaging indicates direct and absolute measurement of cerebral blood flow (CBF). Arterial transit time (ATT) is a related physiological parameter reflecting the duration for the labeled spins to reach the brain region of interest. Multiple post-labeling delay (PLDs) can provide robust measures of both CBF and ATT, allowing for optimization of regional CBF modeling based on ATT. The prolonged acquisition time can potentially reduce the quality and accuracy of the CBF and ATT estimation. We proposed a novel network to significantly reduce the number of PLDs with higher signal-to-noise ratio (SNR). Method: CBF and ATT estimations were performed for one PLD and two PLDs sepa-rately. Each model was trained independently to learn the nonlinear transformation from perfusion weighted image (PWI) to CBF and ATT images. Results: Both one-PLD and two-PLD models outperformed the conventional method visually on CBF and two-PLD model showed more accurate structure on ATT estima-tion. The proposed method significantly reduces the number of PLDs from 6 to 2 on ATT and even to single PLD on CBF without sacrificing the SNR. Conclusion: It is feasible to generate CBF and ATT maps with reduced PLDs using deep learning with high quality.
