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

Kai Wang (China Unicom) · Feiran Wang (Illinois Institute of Technology) · Jiachen Tao (University of Illinois at Chicago) · Junyi Wu (University of Illinois Chicago) · Haoxuan Wang (University of Illinois at Chicago) · Bin Duan (University of Michigan Ann Arbor) · Zongxin Yang (Zhejiang University) · Yan Yan (Xiamen University)

3d reconstructionattenuation coefficientsattenuation propertiescomputed tomographycumulative attenuationgeometric accuracyhomogeneous 3d ellipsoidshybrid progressive initializationmaterial-based optimizationnovel view synthesispath-partitioning algorithmpenetration propertiesradiation exposurevisual fidelityx-ray imaging

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.