Explicit and Implicit Representations in AI-based 3D Reconstruction for Radiology: A Systematic Review
It provides a structured overview for researchers and practitioners in radiology, but is incremental as it synthesizes existing work without introducing new methods.
This systematic review categorizes AI-based 3D reconstruction algorithms in radiology into explicit and implicit approaches, analyzing their methods, evaluation metrics, and datasets to address the need for improved medical imaging accuracy and efficiency.
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/AI4Radiology.