Cone-Beam CT Segmentation for Intraoperative Electron Radiotherapy Based on U-Net Variants with Transformer and Extended LSTM Approaches.

Artificial Intelligence (AI) applications are increasingly prevalent in radiotherapy, including commercial software solutions for automatic segmentation of anatomical structures for 3D Computed Tomography (CT). However, their use in intraoperative electron radiotherapy (IOERT) remains limited. In particular, no AI solution is available for contouring cone beam CT (CBCT) images acquired with a mobile CBCT device.

Investigating focal spot position drift in a mobile imaging system equipped with a monobloc-based x-ray generator.

Background: Misalignment or double-contouring artifacts can appear in high-resolution 3D cone beam computed tomography (CBCT) images, potentially indicating geometric accuracy issues in the projection data. Such artifacts may go unnoticed in low-resolution images and could be associated with changes in the focal spot (FS) position.

Purpose: High-resolution 3D-CBCT imaging by a mobile imaging device with a large gantry clearance offers more versatility for clinical workflows in image-guided brachytherapy (IGBT), intraoperative radiation therapy (IORT), and spinal, as well as maxillofacial surgery.