Paper Info
Title
Synthesizing Contrast-enhanced Computed Tomography Images with an Improved Conditional Generative Adversarial Network.
Abstract
Contrast-enhanced computed tomography (CE-CT) images are used extensively for the diagnosis of liver cancer in clinical practice. Compared with the non-contrast CT (NC-CT) images (CT scans without injection), the CE-CT images are obtained after injecting the contrast, which will increase physical burden of patients. To handle the limitation, we proposed an improved conditional generative adversarial network (improved cGAN) to generate CE-CT images from non-contrast CT images. In the improved cGAN, we incorporate a pyramid pooling module and an elaborate feature fusion module to the generator to improve the capability of encoder in capturing multi-scale semantic features and prevent the dilution of information in the process of decoding. We evaluate the performance of our proposed method on a contrast-enhanced CT dataset including three phases of CT images, (i.e., non-contrast image, CE-CT images in arterial and portal venous phases). Experimental results suggest that the proposed method is superior to existing GAN-based models in quantitative and qualitative results.
Year
DOI
Venue
2022
10.1109/EMBC48229.2022.9871672
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DocType
Volume
ISSN
Conference
2022
2694-0604
Citations 
PageRank 
References 
0
0.34
0
Authors
9
Name
Order
Citations
PageRank
Yulin Yang121.81
Yutaro Iwamoto202.03
Yen-Wei Chen3720155.73
Caie Xu401.35
Qingqing Chen500.68
Hongjie Hu6119.50
Xian-Hua Han71410.19
Ruofeng Tong846649.69
Lanfen Lin97824.70