Purpose: To develop biophysics-based method for estimating perfusion Q from arterial spin labeling (ASL) images using deep learning. Methods: A 3D U-Net (QTMnet) was trained to estimate perfusion from 4D tracer propagation images. The network was trained and tested on simulated 4D tracer concentration data based on artificial vasculature structure generated by constrained constructive optimization (CCO) method. The trained network was further tested in a synthetic brain ASL image based on vasculature network extracted from magnetic resonance (MR) angiography. The estimations from both trained network and a conventional kinetic model were compared in ASL images acquired from eight healthy volunteers. Results: QTMnet accurately reconstructed perfusion Q from concentration data. Relative error of the synthetic brain ASL image was 7.04% for perfusion Q, lower than the error using single-delay ASL model: 25.15% for Q, and multi-delay ASL model: 12.62% for perfusion Q. Conclusion: QTMnet provides accurate estimation on perfusion parameters and is a promising approach as a clinical ASL MRI image processing pipeline.

翻译：目的：开发基于生物物理的方法，利用深度学习从动脉自旋标记(ASL)图像中估计灌注参数Q。方法：训练三维U-Net网络(称为QTMnet)从四维示踪剂传播图像中估计灌注参数。该网络基于受约束构造优化(CCO)方法生成的人工血管结构模拟的四维示踪剂浓度数据进行训练和测试。进一步在基于磁共振血管成像(MR angiography)提取的血管网络的合成脑ASL图像上测试训练好的网络。在八名健康志愿者采集的ASL图像中，比较了训练网络与传统动力学模型的估计结果。结果：QTMnet能从浓度数据中准确重建灌注参数Q。合成脑ASL图像中灌注参数Q的相对误差为7.04%，低于单延迟ASL模型(Q的误差为25.15%)和多延迟ASL模型(灌注参数Q的误差为12.62%)。结论：QTMnet能精确估计灌注参数，作为临床ASL MRI图像处理流程具有良好应用前景。