LAUSR

PURPOSE Dynamic contrast enhanced computed tomography (CT) is widely used to provide dynamic tissue contrast for diagnostic investigation and vascular identification. However, the phase information of contrast injection is typically recorded manually by technicians, which introduces missing or mislabeling. Hence, imaging-based contrast phase identification is appealing, but challenging, due to large variations among different contrast protocols, vascular dynamics, and metabolism, especially for clinically acquired CT scans. The purpose of this study is to perform imaging-based phase identification for dynamic abdominal CT using a proposed adversarial learning framework across five representative contrast phases. METHODS A generative adversarial network (GAN) is proposed as a disentangled representation learning model. To explicitly model different contrast phases, a low dimensional common representation and a class specific code are fused in the hidden layer. Then, the low dimensional features are reconstructed following a discriminator and classifier. 36350 slices of CT scans from 400 subjects are used to evaluate the proposed method with five-fold cross validation with splits on subjects. Then, 2216 slices images from 20 independent subjects are employed as independent testing data, which are evaluated using multi-class normalized confusion matrix. RESULTS The proposed network significantly improved correspondence (0.93) over VGG, ResNet50, StarGAN and 3DSE with accuracy scores 0.59. 0.62, 0.72 and 0.90, respectively (p-value<0.001 Stuart-Maxwell test for normalized multi-class confusion matrix). CONCLUSION We show that adversarial learning for discriminator can be benefit for capturing contrast information among phases. The proposed discriminator from the disentangled network achieves promising results.