Development of learning-based online C-arm CT imaging technique for image-guided adaptive brachytherapy

Existing protocols for image-guided adaptive brachytherapy (BT) pose distinct limitations, which include: (1) the possible misalignment of the application position as a consequence of patient translocation between the treatment room and the computed tomography (CT)/magnetic resonance imaging (MRI) suite, (2) an inherent inability to continually monitor physiological and anatomical changes during the course of 3-10 fraction treatment regimens, and (3) insufficient tracing of the radioactive source movement within the patient's body. In response to these challenges, this study proposes an innovative deep-learning-based C-arm CT/SPECT imaging system tailored specifically for image-guided adaptive BT. This technological advancement aims to enable the execution of high-precision online adaptive BT, thereby amplifying the adaptability and effectiveness of oncological care. An image dataset of 66 non-contrast pelvic CT studies was acquired and limited-angle cone-beam CT (CBCT) images were generated by mathematically calculating sinograms for voxel phantoms based on these CT images. Using a Generative Adversarial Network (GAN), low-quality CBCT images obtained using a 110 degrees limitedangle rotating C-arm system were transformed into high-quality diagnostic CT images. The performances of these networks were evaluated by comparing them with CBCT images reconstructed using an iterative reconstruction method and ground-truth images. Synthetic CT images were successfully transformed from low-quality CBCT images, considerably reducing streaking artifacts and preserving the anatomical structures. Our deep-learningbased image reconstruction technique could successfully improve the image quality of limited-angle CBCT, similar to the ground-truth image quality, with a faster speed than the latest iterative reconstruction algorithm. We expect that this will be effectively implemented in online image-guided adaptive BT and patient dose verification.