The demand for high-quality medical imaging in clinical practice and assisted diagnosis has made 3D reconstruction in radiological imaging a key research focus. Artificial intelligence (AI) has emerged as a promising approach to enhancing reconstruction accuracy while reducing acquisition and processing time, thereby minimizing patient radiation exposure and discomfort and ultimately benefiting clinical diagnosis. This review explores state-of-the-art AI-based 3D reconstruction algorithms in radiological imaging, categorizing them into explicit and implicit approaches based on their underlying principles. Explicit methods include point-based, volume-based, and Gaussian representations, while implicit methods encompass implicit prior embedding and neural radiance fields. Additionally, we examine commonly used evaluation metrics and benchmark datasets. Finally, we discuss the current state of development, key challenges, and future research directions in this evolving field. Our project available on: https://github.com/Bean-Young/AI4Med.

本研究针对放射学图像中三维重建的现有技术与方法的不足，提供了一种系统的文献综述。论文通过对显式和隐式重建算法的分类，揭示了人工智能在提升重建精度、缩短处理时间及减小患者辐射暴露方面的关键作用，指出未来研究的挑战与方向。

基于AI的放射学三维重建中的显式和隐式表示：系统文献综述