The interpretation of three-dimensional (3D) medical images largely depends on the expertise of healthcare professionals, making the training and interpretation processes challenging. With the expanding use of 3D printing in medicine, particularly in orthopedics, oncology, and cardiovascular diseases, its application has become increasingly widespread. This study focuses on the cardiovascular, developing a model-building process to provide accurate 3D models tailored to individual patient conditions. We utilized computed tomography angiography (CTA) images to construct the models. Initially, median filtering was employed to eliminate noise while preserving edge details, thus enhancing image quality. This was followed by threshold segmentation, which extracted relevant anatomical structures by isolating pixel values within a specific range. Subsequent erosion and dilation operations were applied to remove minor vessels and irrelevant regions. The largest connected component analysis was then conducted to ensure retention of only primary vascular structures. The processed data were converted into Stereolithography (STL) format for 3D printing. We reconstructed digital image slices to align with the 3D printed model’s angles and perspectives, allowing for direct comparison within software. Healthcare professionals can use measurement tools in the software to verify the consistency between the 3D model and the original images. The resulting 3D models facilitate a better understanding of complex anatomical structures and pathological features, significantly benefiting surgical planning, personalized treatment, and medical education. These models serve as valuable tools not only for clinical decision-making but also for enhancing healthcare professionals’ training.