论文标题
低剂量CT重建的插件方法可以在多大程度上超过神经网络
To what extent can Plug-and-Play methods outperform neural networks alone in low-dose CT reconstruction
论文作者
论文摘要
最近引入了用于低剂量CT重建的插件（PNP）框架，以利用基于模型的方法的解释性和灵活性来合并各种插件，例如训练有素的深度学习（DL）神经网络。但是，尚未明确证明PNP与最先进的DL方法的好处。在这项工作中，我们提出了一个改进的PNP框架，以解决先前的局限性并开发与临床相关的分割指标进行定量结果评估。与单独使用DL方法相比，我们提出的PNP框架在MSE和PSNR中略微较低。但是，所得图像的功率谱比全剂量图像的功率谱比DL DeNo的图像更好地匹配。与DL DeNo的图像相比，测试组中的所有十名患者的呼吸道分割的精度更高，在横截面上更大的横截面小于0.61厘米$^2 $，在Lobar分割中的50个肺叶中的45张横截面小于0.61cm $^2 $。事实证明，我们的PNP方法在保留图像纹理方面表现出色，这转化为自动结构分割和检测中特定于任务的好处。
The Plug-and-Play (PnP) framework was recently introduced for low-dose CT reconstruction to leverage the interpretability and the flexibility of model-based methods to incorporate various plugins, such as trained deep learning (DL) neural networks. However, the benefits of PnP vs. state-of-the-art DL methods have not been clearly demonstrated. In this work, we proposed an improved PnP framework to address the previous limitations and develop clinical-relevant segmentation metrics for quantitative result assessment. Compared with the DL alone methods, our proposed PnP framework was slightly inferior in MSE and PSNR. However, the power spectrum of the resulting images better matched that of full-dose images than that of DL denoised images. The resulting images supported higher accuracy in airway segmentation than DL denoised images for all the ten patients in the test set, more substantially on the airways with a cross-section smaller than 0.61cm$^2$, and outperformed the DL denoised images for 45 out of 50 lung lobes in lobar segmentation. Our PnP method proved to be significantly better at preserving the image texture, which translated to task-specific benefits in automated structure segmentation and detection.
