Generating synthetic CTs from magnetic resonance images using generative adversarial networks. Issue 8 (10th July 2018)

• Record Type: Journal Article
• Authors: Emami, Hajar; Dong, Ming; Nejad‐Davarani, Siamak P.; Glide‐Hurst, Carri K.
• Is Part Of: Medical physics. Volume 45:Issue 8(2018)
• Page Start: 3627
• Page End: 3636
• Publication Date: 2018-07-10
• Subjects: generative adversarial network -- radiation therapy -- synthetic CTs; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13047
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: While MR‐only treatment planning using synthetic CTs (synCTs) offers potential for streamlining clinical workflow, a need exists for an efficient and automated synCT generation in the brain to facilitate near real‐time MR‐only planning. This work describes a novel method for generating brain synCTs based on generative adversarial networks (GANs), a deep learning model that trains two competing networks simultaneously, and compares it to a deep convolutional neural network (CNN). Methods: Post‐Gadolinium T1‐Weighted and CT‐SIM images from fifteen brain cancer patients were retrospectively analyzed. The GAN model was developed to generate synCTs using T1‐weighted MRI images as the input using a residual network (ResNet) as the generator. The discriminator is a CNN with five convolutional layers that classified the input image as real or synthetic. Fivefold cross‐validation was performed to validate our model. GAN performance was compared to CNN based on mean absolute error (MAE), structural similarity index (SSIM), and peak signal‐to‐noise ratio (PSNR) metrics between the synCT and CT images. Results: GAN training took ~11 h with a new case testing time of 5.7 ± 0.6 s. For GAN, MAEs between synCT and CT‐SIM were 89.3 ± 10.3 Hounsfield units (HU) and 41.9 ± 8.6 HU across the entire FOV and tissues, respectively. However, MAE in the bone and air was, on average, ~240–255 HU. By comparison, the CNN model had an average full FOV MAE of 102.4 ± 11.1 HU. For GAN, the mean PSNR was 26.6 ± 1.2 and SSIM was 0.83 ± 0.03. GAN synCTs preserved details better than CNN, and regions of abnormal anatomy were well represented on GAN synCTs. Conclusions: We developed and validated a GAN model using a single T1‐weighted MR image as the input that generates robust, high quality synCTs in seconds. Our method offers strong potential for supporting near real‐time MR‐only treatment planning in the brain.