用于PET头部运动校正的多任务深度学习与不确定性估计
MULTI-TASK DEEP LEARNING AND UNCERTAINTY ESTIMATION FOR PET HEAD MOTION CORRECTION.
作者信息
Lieffrig Eléonore V, Zeng Tianyi, Zhang Jiazhen, Fontaine Kathryn, Fang Xi, Revilla Enette, Lu Yihuan, Onofrey John A
机构信息
Departments of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
出版信息
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023. doi: 10.1109/isbi53787.2023.10230791. Epub 2023 Sep 1.
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.
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，会导致图像质量下降并引发量化误差。我们之前基于金标准Polaris Vicra运动跟踪设备的监督学习，引入了一种深度学习头部运动校正（DL-HMC）方法，并展示了该方法的潜力。在本研究中，我们将网络升级为多任务架构，以便在学习过程中纳入图像外观预测。这种多任务深度学习头部运动校正（mtDL-HMC）模型在21名受试者上进行了训练，并且在对5名测试受试者的定量和定性结果方面，与我们之前的DL-HMC方法相比，展示出了增强的运动预测性能。我们还通过在测试受试者推理时执行蒙特卡洛随机失活来评估网络预测的可信度。我们舍弃与较大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，甚至还能提高图像质量。
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