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Objective. In this study, we aimed to develop deep learning framework to improve cone-beam computed tomography (CBCT) image quality for adaptive radiation therapy (ART) applications. Approach. Paired CBCT and planning CT images of 2 pelvic phantoms and 91 patients (15 patients for testing) diagnosed with prostate cancer were included in this study. First, well-matched images of rigid phantoms were used to train a U-net, which is the supervised learning strategy to reduce serious artifacts. Second, the phantom-trained U-net generated intermediate CT images from the patient CBCT images. Finally, a cycle-consistent generative adversarial network (CycleGAN) was trained with intermediate CT images and deformed planning CT images, which is the unsupervised learning strategy to learn the style of the patient images for further improvement. When testing or applying the trained model on patient CBCT images, the intermediate CT images were generated from the original CBCT image by U-net, and then the synthetic CT images were generated by the generator of CycleGAN with intermediate CT images as input. The performance was compared with conventional methods (U-net/CycleGAN alone trained with patient images) on the test set. Results. The proposed two-step method effectively improved the CBCT image quality to the level of CT scans. It outperformed conventional methods for region-of-interest contouring and HU calibration, which are important to ART applications. Compared with the U-net alone, it maintained the structure of CBCT. Compared with CycleGAN alone, our method improved the accuracy of CT number and effectively reduced the artifacts, making it more helpful for identifying the clinical target volume. Significance. This novel two-step method improves CBCT image quality by combining phantom-based supervised and patient-based unsupervised learning strategies. It has immense potential to be integrated into the ART workflow to improve radiotherapy accuracy., (© 2022 Institute of Physics and Engineering in Medicine.)

Objective: Megavoltage computed tomography (MV-CT) is used for setup verification and adaptive radiotherapy in tomotherapy. However, its low contrast and high noise lead to poor image quality. This study aimed to develop a deep-learning-based method to generate synthetic kilovoltage CT (skV-CT) and then evaluate its ability to improve image quality and tumor segmentation. Approach: The planning kV-CT and MV-CT images of 270 patients with nasopharyngeal carcinoma (NPC) treated on an Accuray TomoHD system were used. An improved cycle-consistent adversarial network which used residual blocks as its generator was adopted to learn the mapping between MV-CT and kV-CT and then generate skV-CT from MV-CT. A Catphan 700 phantom and 30 patients with NPC were used to evaluate image quality. The quantitative indices included contrast-to-noise ratio (CNR), uniformity and signal-to-noise ratio (SNR) for the phantom and the structural similarity index measure (SSIM), mean absolute error (MAE), and peak signal-to-noise ratio (PSNR) for patients. Next, we trained three models for segmentation of the clinical target volume (CTV): MV-CT, skV-CT, and MV-CT combined with skV-CT. The segmentation accuracy was compared with indices of the dice similarity coefficient (DSC) and mean distance agreement (MDA). Main results: Compared with MV-CT, skV-CT showed significant improvement in CNR (184.0%), image uniformity (34.7%), and SNR (199.0%) in the phantom study and improved SSIM (1.7%), MAE (24.7%), and PSNR (7.5%) in the patient study. For CTV segmentation with only MV-CT, only skV-CT, and MV-CT combined with skV-CT, the DSCs were 0.75 ± 0.04, 0.78 ± 0.04, and 0.79 ± 0.03, respectively, and the MDAs (in mm) were 3.69 ± 0.81, 3.14 ± 0.80, and 2.90 ± 0.62, respectively. Significance: The proposed method improved the image quality of MV-CT and thus tumor segmentation in helical tomotherapy. The method potentially can benefit adaptive radiotherapy., (Creative Commons Attribution license.)