X-Field: A Physically Informed Representation for 3D X-ray Reconstruction

Feiran Wang, Jiachen Tao, Junyi Wu, Haoxuan Wang, Bin Duan, Kai Wang, Zongxin Yang, Yan Yan

Keywords: 3D Reconstruction; X-ray

Abstract:

X-ray imaging is indispensable in medical diagnostics, yet its use is tightly regulated due to radiation exposure. Recent research borrows representations from the 3D reconstruction area to complete two tasks with reduced radiation dose: X-ray Novel View Synthesis (NVS) and Computed Tomography (CT) reconstruction.
However, these representations fail to fully capture the penetration and attenuation properties of X-ray imaging as they originate from visible light imaging.
In this paper, we introduce X-Field, a 3D representation informed in the physics of X-ray imaging.
First, we employ homogeneous 3D ellipsoids with distinct attenuation coefficients to accurately model diverse materials within internal structures. Second, we introduce an efficient path-partitioning algorithm that resolves the intricate intersection of ellipsoids to compute cumulative attenuation along an X-ray path.
We further propose a hybrid progressive initialization to refine the geometric accuracy of X-Field and incorporate material-based optimization to enhance model fitting along material boundaries.
Experiments show that X-Field achieves superior visual fidelity on both real-world human organ and synthetic object datasets, outperforming state-of-the-art methods in X-ray NVS and CT Reconstruction.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 18383