论文标题
在空间频率和图像域中具有感知损失函数的低剂量CT增强网络
Low-dose CT Enhancement Network with a Perceptual Loss Function in the Spatial Frequency and Image Domains
论文作者
论文摘要
我们提出了在空间频率和图像域中运行的U-NET（即“ W-NET”）的双域级联，以增强低剂量CT（LDCT）图像，而无需专有X射线投影数据。中央切片定理促使使用空间频域代替原始辛图。数据是从AAPM低剂量大挑战中获得的。傅立叶空间（F）和/或图像域（I）U-NET和W-NET的组合通过多尺度结构相似性和平均绝对误差损失函数进行训练，以使DENOISE过滤后的投影（FBP）LDCT图像保持感知功能，以保持对诊断准确性重要的知觉特征。深度学习的增强功能优于FBP LDCT图像，在定量和定性性能中，双域W-NET的表现优于单域U-NET级联。我们的结果表明，与仅图像域的网络相比，与图像域处理结合使用的空间频率学习可以产生优越的LDCT增强。
We propose a dual-domain cascade of U-nets (i.e. a "W-net") operating in both the spatial frequency and image domains to enhance low-dose CT (LDCT) images without the need for proprietary x-ray projection data. The central slice theorem motivated the use of the spatial frequency domain in place of the raw sinogram. Data were obtained from the AAPM Low-dose Grand Challenge. A combination of Fourier space (F) and/or image domain (I) U-nets and W-nets were trained with a multi-scale structural similarity and mean absolute error loss function to denoise filtered back projected (FBP) LDCT images while maintaining perceptual features important for diagnostic accuracy. Deep learning enhancements were superior to FBP LDCT images in quantitative and qualitative performance with the dual-domain W-nets outperforming single-domain U-net cascades. Our results suggest that spatial frequency learning in conjunction with image-domain processing can produce superior LDCT enhancement than image-domain-only networks.
