This study proposes a framework for online adaptive three-dimensional (3D) brachytherapy, a type of radiation therapy used to treat gynecological cancers such as cervical and endometrial cancers. Intracavitary brachytherapy is used to deliver a high dose of radiation directly to the tumor, while minimizing exposure to the surrounding normal tissue. Maintaining accurate reproducibility of radiation dose delivery during each treatment session is important. The proposed framework uses a C-arm-based integrated online imaging system for computed tomography (CT) and single-photon emission computed tomography (SPECT) to detect changes in the treatment conditions and improve treatment quality. The C-arm CT system acquires 3D images by obtaining two-dimensional image data at limited rotation angles, which are less than the complete 360° range of rotation. However, images obtained with limited angular rotation have poor image quality and many artifacts. To overcome this limitation, a deep learning technique was applied to eliminate these artifacts and improve image quality. This study evaluated the auto-segmentation performance of ground truth (GT) images, limited-angle C-arm cone-beam CT images, and C-arm CT images corrected using deep learning. We used the Dice similarity coefficient and Hausdorff distance to quantitatively evaluate the auto-segmentation performance of major organs in the pelvic region. Although slight differences were observed in certain areas, the auto-segmentation results demonstrated an overall high performance, similar to that of the GT image. The results showed that the proposed framework can establish an optimal treatment plan by quickly and accurately calculating the patient's internal radiation dose distribution. This study demonstrates the potential of deep learning-based high-quality CT imaging techniques and auto-contouring techniques for major organs within images to improve the treatment quality of intracavitary brachytherapy.
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