Conventional 2D neural networks can only process one single image at a time, which may affect the image continuity and lead to missing structural details around lesions. In contrast, 3D neural networks support volumetric data input, maintaining data continuity while improving reconstruction speed, accuracy, and reliability. 

uAI Vision, based on powerful deep learning algorithms, can identify 19 anatomical landmarks on the human body. It is able to match all scanning protocols, which covers multiple clinical scenarios. Furthermore, it can even provides real-time tracking of patient movements, avoiding the resulted artificial errors, which could potentially increase the radiation dose and impact on image quality.

At lower tube voltages, the mass attenuation coefficient increases, leading to higher contrast between vessels and surrounding tissues with the same concentration of iodinated contrast agent. Therefore, scanning at 70 kV not only enhances vascular contrast but also reduces the amount of contrast medium required and lowers radiation exposure. Combined with Auto ALARA kVp, which automatically selects tube voltage based on patient size and scanning protocol, providing tailored imaging with high-quality results.