Explicit and Implicit Representations in AI-based 3D Reconstruction for Radiology: A Systematic Review
Journal:
arXiv
Published Date:
Apr 15, 2025
Abstract
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.
