Deformable resection process map for intraoperative cutting guides.

Area of Science:

• Medical Imaging
• Surgical Oncology
• Computational Geometry

Background:

• Soft tissue tumor resection requires precise guidance to avoid critical structures like blood vessels.
• Current methods for surgical planning and intraoperative guidance can be time-consuming and lack real-time adaptability.

Purpose of the Study:

• To introduce deformable resection process mapping (RPM) as a novel, time-varying geometric guide for soft tissue tumor resection.
• To develop a user-friendly software tool for generating RPMs from patient-specific medical images, enabling surgical preview and intraoperative workflow management.

Main Methods:

• Deformable resection process mapping (RPM) was developed, estimating local vascular structure appearance post-incision.
• RPMs are generated from patient-specific medical images using volumetric resampling techniques.
• The method requires minimal user input (cutting points) for RPM generation.

Main Results:

• The RPM provides a novel visual guide by estimating vascular structures after simulated cuts.
• The developed software allows for direct clinical use, previewing surgical procedures and managing intraoperative workflow.
• Experiments using CT images of hepatic cancer patients demonstrated the method's performance in representing curved cut surfaces, comparable to conventional methods.

Conclusions:

• Deformable resection process mapping offers a clinically applicable, time-varying geometric guide for soft tissue tumor resection.
• The RPM system enhances surgical planning and intraoperative workflow by providing real-time visual feedback on vascular structures.
• This approach minimizes setup time and additional workload for clinicians, facilitating integration into surgical practice.