Synthetic Projection Generation with Angle Conditioning

Computed Tomography (CT) plays a vital role in medical imaging, offering cross-sectional views of internal structures. Yet, radiation exposure during CT scans poses health risks. This study explores the application of existing deep learning models to synthesize CT projections at unknown arbitrary angles. Multiple input images from varying angles, along with their corresponding ground truth data, train different network architectures to reproduce target images from different view angles.  This approach potentially reduces radiation exposure and addresses challenges in obtaining specific missing angular views. Experimental results confirm the effectiveness and feasibility of the methodology, establishing it as a valuable tool in CT imaging.