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Updated: May 5, 2026

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Automatic multi-view X-ray/CT registration using bone substructure contours.

Roman Flepp^1,2, Leon Nissen³, Bastian Sigrist³

¹University Children's Hospital Zürich, Zurich, Switzerland. roman.flepp@kispi.uzh.ch.

International Journal of Computer Assisted Radiology and Surgery

|May 20, 2025

Summary

This summary is machine-generated.

This study introduces a new multi-view X-ray/CT registration method for orthopedic surgery. The technique achieves sub-millimeter accuracy automatically, improving surgical navigation and outcomes.

Keywords:

Contour segmentation Contour-based ICP Multi-view X-ray/CT registration Orthopedic surgery Semantic contour Skeleton loss Surgical navigation nnU-Net

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Area of Science:

Medical Imaging
Computer-Assisted Surgery
Orthopedic Surgery

Background:

Accurate intraoperative X-ray/CT registration is crucial for orthopedic surgical navigation.
Existing methods often lack consistent sub-millimeter accuracy, robustness, or require manual annotations.

Purpose of the Study:

To develop a novel multi-view X-ray/CT registration method for intraoperative bone registration.
To overcome limitations of existing methods in accuracy, robustness, and automation.

Main Methods:

A multi-view, contour-based iterative closest point (ICP) optimization approach.
Matching specific bone substructure contours, reducing ambiguity for robust ICP.
Fully automatic registration using only two X-ray images.

Main Results:

Achieved sub-millimeter accuracy with a mean reprojection error (mRPD) of 0.67 mm.
Outperformed a commercial solution (5.35 mm mRPD) that required manual intervention.
Demonstrated improved practical applicability through full automation.

Conclusions:

The proposed method provides a practical, accurate, and efficient solution for multi-view X-ray/CT registration in orthopedic surgery.
Enhances intraoperative navigation accuracy and reduces manual intervention for better surgical outcomes.
Publicly available code and dataset facilitate further research and application in computer-assisted surgery (CAS).