CT scan merging to enhance navigation in interventional radiology simulation

Area of Science:

• Medical Imaging
• Computer-Aided Surgery
• Radiology

Background:

• Patient CT scans are often localized to minimize radiation exposure and increase throughput.
• Localized scans may lack essential surrounding anatomy required for certain simulations.
• This limitation impacts the completeness of anatomical data for applications like surgical planning.

Purpose of the Study:

• To present a novel method for merging two distinct CT scans from different patients.
• To create a comprehensive anatomical dataset for enhanced vascular intervention simulations.
• To provide a solution for supplementing missing anatomical regions in patient CT data.

Main Methods:

• A technique to merge two separate CT scans from different individuals.
• Integration of supporting anatomy from a second scan into a primary scan.
• Development of a process to create a complete anatomical reference from partial scans.

Main Results:

• Successfully merged distinct CT scans to supplement missing anatomical structures.
• Generated a comprehensive anatomical dataset suitable for full-body simulations.
• Demonstrated the utility of the merged scan for enhancing X-ray rendering in navigation simulations.

Conclusions:

• The CT scan merging method effectively creates complete anatomical models from partial scans.
• This technique significantly enhances the utility of CT data for vascular intervention simulations and training.
• The approach provides a valuable tool for improving anatomical reference in medical simulations and interventional radiology.