NeAS: 3D Reconstruction from X-ray Images using Neural Attenuation Surface
This work addresses a domain-specific problem in medical imaging by improving 3D reconstruction accuracy from X-ray images, which could reduce radiation exposure compared to CT scans, but it appears incremental as it builds on existing implicit neural representation approaches.
The paper tackles the problem of insufficient surface shape estimation in 3D reconstruction from 2D X-ray images by proposing NeAS, a method that simultaneously captures surface geometry and attenuation coefficient fields, and demonstrates accurate 3D surface extraction using only 2D X-ray images in experiments with simulated and authentic data.
Reconstructing three-dimensional (3D) structures from two-dimensional (2D) X-ray images is a valuable and efficient technique in medical applications that requires less radiation exposure than computed tomography scans. Recent approaches that use implicit neural representations have enabled the synthesis of novel views from sparse X-ray images. However, although image synthesis has improved the accuracy, the accuracy of surface shape estimation remains insufficient. Therefore, we propose a novel approach for reconstructing 3D scenes using a Neural Attenuation Surface (NeAS) that simultaneously captures the surface geometry and attenuation coefficient fields. NeAS incorporates a signed distance function (SDF), which defines the attenuation field and aids in extracting the 3D surface within the scene. We conducted experiments using simulated and authentic X-ray images, and the results demonstrated that NeAS could accurately extract 3D surfaces within a scene using only 2D X-ray images.