3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation

TL;DR

This paper introduces a novel method for 3D bone surface reconstruction from a single X-ray radiograph by estimating dual-face depth maps, leveraging X-ray geometry for improved accuracy and efficiency in clinical settings.

Contribution

The study presents a new approach that simultaneously learns front- and back-surface depth maps from X-ray images, exploiting fixed geometry to enhance surface reconstruction accuracy.

Findings

Reconstructed surface error reduced from 4.78 mm to 1.96 mm.

Method outperforms traditional approaches in accuracy.

Demonstrated potential for clinical application with 600 CT and 2651 X-ray images.

Abstract

Radiography is widely used in orthopedics for its affordability and low radiation exposure. 3D reconstruction from a single radiograph, so-called 2D-3D reconstruction, offers the possibility of various clinical applications, but achieving clinically viable accuracy and computational efficiency is still an unsolved challenge. Unlike other areas in computer vision, X-ray imaging's unique properties, such as ray penetration and fixed geometry, have not been fully exploited. We propose a novel approach that simultaneously learns multiple depth maps (front- and back-surface of multiple bones) derived from the X-ray image to computed tomography registration. The proposed method not only leverages the fixed geometry characteristic of X-ray imaging but also enhances the precision of the reconstruction of the whole surface. Our study involved 600 CT and 2651 X-ray images (4 to 5 posed X-ray images per patient), demonstrating our method's superiority over traditional approaches with a surface reconstruction error reduction from 4.78 mm to 1.96 mm. This significant accuracy improvement and enhanced computational efficiency suggest our approach's potential for clinical application.