论文标题
自我监督的动态CT CT灌注图像Denoising vith Deep Neural Networks
Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks
论文作者
论文摘要
动态计算机断层摄影灌注（CTP）成像是急性缺血性中风诊断和评估的有前途的方法。大脑实质的血液动力学参数图是根据碘对比的第一次通过的重复CT扫描来计算的。由于重复扫描的高辐射暴露，因此有必要减少常规应用的CTP剂量，在这种扫描中，必须进行图像降级以实现可靠的诊断。在本文中，我们提出了一种用于CTP DeNoising的自制深度学习方法，该方法不需要任何高剂量参考图像进行培训。通过将CTP的每个帧映射到其相邻帧的估计中，对网络进行了训练。由于源和目标中的噪声是独立的，因此该方法可以有效地消除噪声。没有高剂量训练图像授予了提出的方法更容易适应不同的扫描协议。该方法在仿真和公共实际数据集上均已验证。与传统的降解方法相比，提出的方法提高了图像质量。在实际数据上，与监督学习相比，所提出的方法还改善了空间分辨率和对比度与噪声比率。
Dynamic computed tomography perfusion (CTP) imaging is a promising approach for acute ischemic stroke diagnosis and evaluation. Hemodynamic parametric maps of cerebral parenchyma are calculated from repeated CT scans of the first pass of iodinated contrast through the brain. It is necessary to reduce the dose of CTP for routine applications due to the high radiation exposure from the repeated scans, where image denoising is necessary to achieve a reliable diagnosis. In this paper, we proposed a self-supervised deep learning method for CTP denoising, which did not require any high-dose reference images for training. The network was trained by mapping each frame of CTP to an estimation from its adjacent frames. Because the noise in the source and target was independent, this approach could effectively remove the noise. Being free from high-dose training images granted the proposed method easier adaptation to different scanning protocols. The method was validated on both simulation and a public real dataset. The proposed method achieved improved image quality compared to conventional denoising methods. On the real data, the proposed method also had improved spatial resolution and contrast-to-noise ratio compared to supervised learning which was trained on the simulation data
