Attention-Aware Discrimination for MR-to-CT Image Translation Using Cycle-Consistent Generative Adversarial Networks
Vasant Kearney, Benjamin Ziemer, Alan Perry, Tianqi Wang, Jason W. Chan, Lijun Ma, Olivier Morin, Sue S. Yom, Timothy D. Solberg
Abstract
Purpose To suggest an attention-aware, cycle-consistent generative adversarial network (A-CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to current state-of-the-art MR-to-CT image translation methods. Materials and Methods An attention-gating mechanism is incorporated into a discriminator network to encourage a more parsimonious use of network parameters, whereas VAE enhancement enables deeper discrimination architectures without inhibiting model convergence. Findings from 60 patients with head, neck, and brain cancer were used to train and validate A-CycleGAN, and findings from 30 patients were used for the holdout test set and were used to report final evaluation metric results using mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). Results A-CycleGAN achieved superior results compared with U-Net, a generative adversarial network (GAN), and a cycle-consistent GAN. The A-CycleGAN averages, 95% confidence intervals (CIs), and Wilcoxon signed-rank two-sided test statistics are shown for MAE (19.61 [95% CI: 18.83, 20.39], P = .0104), structure similarity index metric (0.778 [95% CI: 0.758, 0.798], P = .0495), and PSNR (62.35 [95% CI: 61.80, 62.90], P = .0571). Conclusion A-CycleGANs were a superior alternative to state-of-the-art MR-to-CT image translation methods. Keywords: Brain/Brain Stem, CT, Convolutional Neural Network (CNN), Head/Neck, MR-Imaging, Neural Networks, Physics, Radiation Therapy, Radiation Therapy/Oncology, Supervised learning, Unsupervised learning © RSNA, 2020