论文标题
低剂量CT DeNoing的级联卷积神经网络具有感知损失
Cascaded Convolutional Neural Networks with Perceptual Loss for Low Dose CT Denoising
论文作者
论文摘要
低剂量CT Denoising Research旨在减少对患者的辐射暴露的风险。最近，研究人员使用深度学习来降低低剂量的CT图像，并具有令人鼓舞的结果。但是，使用均方体误差（MSE）的方法倾向于过度平滑图像，从而导致图像低对比度区域的细节丢失。这些区域通常对于诊断至关重要，必须保留以使低剂量CT在实践中有效使用。在这项工作中，我们使用了两个神经网络的级联，其中第一个旨在通过最大程度地减少感知损失来重建低剂量CT的正常剂量CT，第二个预测了感知损失网络的地面真相和预测之间的差异。我们表明，我们的方法优于相关的作品，并且在图像的低对比度区域中更有效地重建了精细的结构细节。
Low Dose CT Denoising research aims to reduce the risks of radiation exposure to patients. Recently researchers have used deep learning to denoise low dose CT images with promising results. However, approaches that use mean-squared-error (MSE) tend to over smooth the image resulting in loss of fine structural details in low contrast regions of the image. These regions are often crucial for diagnosis and must be preserved in order for Low dose CT to be used effectively in practice. In this work we use a cascade of two neural networks, the first of which aims to reconstruct normal dose CT from low dose CT by minimizing perceptual loss, and the second which predicts the difference between the ground truth and prediction from the perceptual loss network. We show that our method outperforms related works and more effectively reconstructs fine structural details in low contrast regions of the image.
