The demand for high-quality medical imaging in clinical practice and assisted diagnosis has made 3D image reconstruction in radiological imaging a key research focus. Artificial intelligence (AI) has emerged as a promising approach for improving reconstruction accuracy while reducing acquisition and processing time, thereby minimizing patient radiation exposure and discomfort and ultimately benefiting clinical diagnosis. This review surveys state-of-the-art AI-based 3D reconstruction algorithms in radiological imaging and organizes them into four representation families according to how the reconstructed target is parameterized: discrete grid representations, explicit basis expansion representations, explicit primitive representations, and implicit neural representations. In particular, the review clarifies the relationships among these representation forms and highlights radiance field methods as a specialized subtype of implicit neural representation. In addition, we summarize commonly used evaluation metrics and benchmark datasets for radiological image reconstruction. Finally, we discuss the current state of development, major challenges, and future research directions in this rapidly evolving field. Our project is available at: https://github.com/Bean-Young/AI4Radiology.

翻译：临床实践与辅助诊断中对高质量医学成像的需求，使放射影像中的3D图像重建成为关键研究热点。人工智能（AI）已成为一种有前景的方法，能够在提高重建精度的同时减少采集和处理时间，从而降低患者辐射暴露和不适感，最终惠及临床诊断。本综述调查了放射影像中基于AI的最先进3D重建算法，并根据重建目标参数化方式将其组织为四类表示家族：离散网格表示、显式基扩展表示、显式基元表示和隐式神经表示。特别地，本综述阐明了这些表示形式之间的关系，并将辐射场方法作为隐式神经表示的一个专门子类型加以强调。此外，我们还总结了放射影像重建中常用的评估指标和基准数据集。最后，我们讨论了这一快速发展领域的发展现状、主要挑战和未来研究方向。我们的项目代码位于：https://github.com/Bean-Young/AI4Radiology。