MR-based synthetic CT generation using dual-attention enhanced 3D Conditional GAN for head and neck radiotherapy

<i>Purpose</i>. This study aims to synthesize CT from MR images for radiotherapy planning of head and neck tumor using an improved three-dimensional conditional generative adversarial network (3D cGAN) based on dual-attention modules.<i>Methods</i>. A total of 212 paired CT and T1-weighted MRI datasets are utilized, including 180 publicly available cases and 32 clinical cases from our hospital. Building upon the 3D cGAN framework, we implement structural modifications to the generator, discriminator, and loss functions. In particular, a lightweight dual-attention mechanism module is introduced to the generator based on 3D residual network. The model is trained on 186 datasets and evaluated on 26 test cases. Quantitative metrics including normalized cross-correlation (NCC), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE) are calculated to assess the similarity between synthetic CT (sCT) and ground-truth CT images. A comparative analysis with U-Net, CycleGAN and basic 3D cGAN is conducted to validate performance improvements.<i>Results</i>. The proposed dual-attention enhanced 3D cGAN generates clinically acceptable sCT images across all 26 test cases. Quantitative evaluations demonstrate high accuracy with NCC of 97.06%, SSIM of 90.24%, PSNR of 28.23 ± 0.42, and MAE of 32.53 ± 2.49 HU. In quantitative comparison, the proposed dual-attention enhanced 3D cGAN approach outperforms U-Net, CycleGAN and the basic 3D cGAN across all metrics.<i>Conclusion</i>. This study proposes an improved dual-attention enhanced 3D cGAN algorithm. The method can rapidly and automatically generate sCT images from MR images for patients of head and neck tumor, which holds significant importance for implementing MR-only radiotherapy planning.