Generating Synthetic 4DCT From 4DCBCT for Lung Tumor Adaptive Photon and Proton Therapy Using a Unet Attention-Guided CycleGAN With Structure-Consistency Loss

This study aims to develop a deep learning-based synthetic 4DCT (s4DCT) generation method from 4DCBCT to enhance the accuracy of dose calculation and respiratory motion management in adaptive radiotherapy for lung tumors. A Unet-based attention mechanism integrated with CycleGAN, incorporating structure-consistency loss called UGGAN-GC, was developed to generate s4DCT images and was compared with several commonly used models. 4DCT and 4DCBCT images of 17 lung tumor patients were included and randomly divided into training set, validation set and test set. Elastix was used to deformably register 4DCT to 4DCBCT to generate the ground truth for training and evaluation of image-quality and dose calculation. Quantitative and qualitative methods were used to assess the quality of regions of interest (ROIs) and images of s4DCT. 4DCT was deformably registered to 4DCBCT and s4DCT using Elastix to evaluate the Dice similarity coefficient (DSC) of ROIs and gross tumor volume (GTV) motion. The average intensity projections (AIP) of the ground truth were used to design photon and proton therapy plans. Dose distributions were compared between s4DCT-AIP and ground truth-AIP using gamma analysis and dose-volume histograms. The experimental results showed that UGGAN-GC eliminated streak artifacts, generated the clearest anatomical structures, and achieved the best HU correction for soft tissues. The MAEs of 4DCBCT, Unet, Pix2pix, Cut, Fastcut, CycleGAN, UGGAN, and UGGAN-GC were 117.65, 71.87, 64.73, 62.92, 62.14, 63.01, 59.97, and 59.66 HU, respectively. The gamma passing rate (GPR) (2%/2 mm) of photon plans exceeded 99.8% for all models. The ranking of proton plan GPR (2%/2 mm) was: UGGAN-GC (97.7%), CycleGAN (95.4%), UGGAN (95.2%), Fastcut (93.1%), Pix2pix (90.8%), Unet (89.9%), and Cut (87.7%). The s4DCT generated by UGGAN-GC demonstrated excellent image quality, characterized by high HU accuracy, structural similarity, and edge detail fidelity, and had the potential to achieve accurate dose calculation and respiratory motion management for online photon and proton therapy plans.