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A newly developed image fusion algorithm between CECT image and CT image: A feasibility study.

Zi Wang¹, Yinzhang Lv¹, Shaowen He²

¹Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

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Summary

This study introduces a new algorithm to map blood vessels onto CT scans, improving cancer diagnosis accuracy. This enhances safety during percutaneous needle biopsies by providing crucial vascular information to radiologists.

Keywords:

CT image Contrast-enhanced CT image finite element model vascular fusion

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Area of Science:

Medical Imaging
Biomedical Engineering
Radiology

Background:

Accurate cancer diagnosis is crucial for effective treatment, with percutaneous needle biopsy being a common diagnostic method.
CT imaging lacks vascular information, posing risks during biopsies, especially for tumors near blood vessels.

Purpose of the Study:

To develop and validate a method for mapping vascular structures from contrast-enhanced CT to standard CT images.
To improve the safety and accuracy of percutaneous needle biopsies by visualizing vessels during the procedure.

Main Methods:

A biomechanical model and surface elastic registration algorithm were employed.
Vessel information was mapped from contrast-enhanced CT to standard CT images for liver and lung data.

Main Results:

The fusion algorithm demonstrated low mean fusion errors for major vessels: portal vein (2.35±0.85 mm), hepatic vein (2.08±0.41 mm), pulmonary artery (2.31±0.49 mm), and pulmonary vein (2.37±0.62 mm).
The developed method achieved satisfactory accuracy for clinical application.

Conclusions:

The developed algorithm effectively maps vascular information onto CT images, aiding radiologists during biopsies.
This technique significantly reduces the risks associated with percutaneous needle biopsies in cancer diagnosis.