| Popis: |
Purpose/Objective(s) To achieve high-precision stereotactic body radiotherapy, visualization of moving tumor/target estimation is performed using 4-dimensional cone-beam computed tomography (4D-CBCT) before each treatment fraction. Currently, respiration-correlated 4D-CBCT is achieved with subgroups of cone-beam projections into different respiratory phases. However, insufficient projections inside each phase bin cause severe imaging artifacts. Convolutional neural networks are widely used to correct CBCT specific artifacts. Previous research has shown that mean absolute error in Hounsfield units improved up to 93% over the original CBCT image. However, slight voxel-wise misalignment between the training images may lead to blurred synthesis, and hence, due to respiratory motion, it is impossible to perform 4D-CBCT imaging without organ misalignment with the 4D-computed tomography (4D-CT). Cycle-generative adversarial network (cycle-GAN) framework enforces an inverse transformation and achieves high accurate consistency even in mapping nonlinear domains. The aim of this study was to improve 4D-CBCT image quality using cycle-GAN and evaluate its use in adaptive radiation therapy (ART) planning for lung cancer. Materials/Methods Unpaired 4D-CT and 4D-CBCT taken from publicly available datasets in 5 patients who underwent lung cancer treatment were used for training using cycle-GAN model. Then, Synthesis of 4D-CBCT (Syn-CBCT) images with improved quality conferred by the cycle-GAN model was tested in another 5 cases in the lung region. To assess pixel value deviation and image similarity, mean absolute errors (MAE) and structural similarity index measure (SSIM) were calculated for Syn-CBCT and paired 4D-CT images and compared with those of original 4D-CBCT images. Moreover, volumetric modulation arc therapy plans with a dose of 48 Gy in 4 fractions were recalculated based on Syn-CBCT images, and 3-dimensional (3D)-gamma (3%/3 mm) analysis was conducted to compare the former with ideal dose distributions observed in 4D-CT images. Results 4D-CBCT imaging quality was improved and lung tumor regions were clearly observed after noise and artifact removal by applying the trained cycle-GAN model. MAEs were improved by 21% and SSIMs by 46% for whole images. Hence, pixel value and structural visualization in Syn-CBCT images could achieve near-ideal quality of 4D-CT images. Large structures were generally consistent with those in paired 4D-CT images, whereas fine structures (pulmonary vessels and fine bones) were not completely duplicated. However, a mean 3D gamma pass rate of > 95% was achieved in all cases. Conclusion The proposed cycle-GAN method enhances 4D-CBCT image quality, making it comparable to that of 4D-CT images. Thus, clinical implementation of Syn-CBCT-based ART is feasible. |
