MULTI-TASK DEEP LEARNING AND UNCERTAINTY ESTIMATION FOR PET HEAD MOTION CORRECTION.
MULTI-TASK DEEP LEARNING AND UNCERTAINTY ESTIMATION FOR PET HEAD MOTION CORRECTION.
复制标题
宠物头部运动校正的多任务深度学习和不确定性估计。
DOI：
10.1109/isbi53787.2023.10230791
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Onofrey,JohnA
中科院分区：
文献类型：
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作者：
Lieffrig,EléonoreV;Zeng,Tianyi;Zhang,Jiazhen;Fontaine,Kathryn;Fang,Xi;Revilla,Enette;Lu,Yihuan;Onofrey,JohnA
在脑正电子发射断层扫描图像采集过程中发生的头部运动导致图像质量下降并引起量化误差。我们之前介绍了一种基于金标准Polaris Vicra运动跟踪设备的监督学习的深度学习头部运动校正（DL-HMC）方法，并展示了这种方法的潜力。在这项研究中，我们将我们的网络升级为多任务架构，以便在学习过程中包括图像外观预测。这种多任务深度学习头部运动校正（mtDL-HMC）模型在21个受试者上进行了训练，与我们之前的DL-HMC方法相比，在5个测试受试者的定量和定性结果上显示出增强的运动预测性能。我们还通过对测试对象进行蒙特卡洛丢弃推断来评估网络预测的可信度。我们丢弃了与运动预测不确定性相关的数据，并表明这不会损害重建图像的质量，甚至可以改善它。
Head motion occurring during brain positron emission tomography images acquisition leads to a decrease in image quality and induces quantification errors. We have previously introduced a Deep Learning Head Motion Correction (DL-HMC) method based on supervised learning of gold-standard Polaris Vicra motion tracking device and showed the potential of this method. In this study, we upgrade our network to a multi-task architecture in order to include image appearance prediction in the learning process. This multi-task Deep Learning Head Motion Correction (mtDL-HMC) model was trained on 21 subjects and showed enhanced motion prediction performance compared to our previous DL-HMC method on both quantitative and qualitative results for 5 testing subjects. We also evaluate the trustworthiness of network predictions by performing Monte Carlo Dropout at inference on testing subjects. We discard the data associated with a great motion prediction uncertainty and show that this does not harm the quality of reconstructed images, and can even improve it.