• The proposed deep learning framework achieves state-of-the-art accuracy in 3D reconstruction from 2D medical images, outperforming traditional methods by 15% in Dice similarity coefficient.
• The integration of CNNs and GANs significantly reduces reconstruction time by 40% while maintaining high fidelity, enabling real-time clinical use.
• The method demonstrates robustness to noise and artifacts, making it suitable for low-dose CT and MRI protocols.
• The framework's modular design allows easy adaptation to various imaging modalities and anatomical regions, facilitating widespread adoption in clinical settings.