Development of a CT image-based virtual atelectasis simulation model and noninvasive lung nodule localization system

Area of Science:

• Medical Imaging
• Thoracic Surgery
• Computational Modeling

Background:

• Lung nodule resection via VATS often faces localization challenges due to discrepancies between preoperative CT scans (inspiration phase) and intraoperative lung status (atelectasis).
• This difference complicates the precise identification of small or subsolid nodules during surgery.

Purpose of the Study:

• To develop a CT-based virtual atelectasis simulation system for noninvasive lung nodule localization.
• To enhance the precision of presurgical planning for lung nodule resections.

Main Methods:

• Retrospective analysis of 20 patients undergoing VATS for lung nodules.
• Conversion of preoperative CT images into 3D lung models.
• Simulation of lung atelectasis by manipulating mesh points to mimic gravity and surgical posture, aligning nodule locations with intraoperative views.
• Comparison of simulated lung shapes and nodule positions against surgical video recordings.

Main Results:

• The simulation achieved an average lung shrinkage rate of 48.6%.
• High conformity between simulation images and surgical videos was observed, with average Dice (90.27%) and Jaccard (88.25%) similarity coefficients.
• Accurate nodule localization was confirmed, with an average Hausdorff distance of 6.39 mm.

Conclusions:

• A CT-based virtual atelectasis simulation system was successfully developed, closely matching intraoperative surgical video findings.
• This simulation system is expected to improve the accuracy of nodule localization.
• The integration of this tool promises more efficient and precise surgical planning for lung nodule resections.