Computed tomography image generation from magnetic resonance imaging using Wasserstein metric for MR‐only radiation therapy

Abstract

AbstractMagnetic resonance imaging (MRI) and computed tomography (CT) are the prevalent imaging techniques used in treatment planning in radiation therapy. Since MR‐only radiation therapy planning (RTP) is needed in the future for new technologies like MR‐LINAC (medical linear accelerator), MR to CT synthesis model benefits in CT synthesis from MR images generated via MR‐LINAC. A Wasserstein generative adversarial network (WGAN) architecture with a residual UNet based generator and a patch‐based discriminator is proposed in this paper. The WGAN uses the Wasserstein metric with gradient penalty along with mean absolute error (MAE) as the loss function. The WGAN is trained and tested on an NVIDIA Tesla V100 GPU server using mutually aligned MR‐CT brain images of 26 patients. The proposed model generates synthetic CTs with an average PSNR of 31.09 dB, SSIM of 0.9265, MAE of 48.39 HU, and RMSE of 172.75 HU. The treatment planning was performed on real and synthetic CTs. The dose distributions of the synthetic CTs obtained were similar to that of the corresponding real CTs. The synthetic CTs exhibit good visual quality as well. The experimental outcomes and image quality of the model surpass many of the state‐of‐the‐art methods. The proposed CT synthesizer supports MR‐only radiation therapy while minimizing treatment costs and radiation exposure. A significant increase in the training set can further improve the CT prediction accuracy of the proposed model.