Computed tomography imaging of Medpor: Graph-cut algorithm and its accuracy

Area of Science:

• Biomaterials Science
• Medical Imaging
• Computer-Aided Surgery

Background:

• Porous polyethylene is a common alloplastic material for maxillofacial reconstruction.
• Its radiolucent nature poses challenges for conventional radiography and accurate volumetric assessment.
• Previous segmentation methods based on gray levels lack detailed accuracy analysis.

Purpose of the Study:

• To evaluate the accuracy of computed tomography (CT) segmentation for porous polyethylene using the Graph-cut algorithm.
• To compare the accuracy of the Graph-cut algorithm with the traditional gray-level segmentation method.

Main Methods:

• Fifteen patients undergoing maxillofacial reconstruction with porous polyethylene between 2005 and 2010 were included.
• Computed tomography scans were acquired for image analysis.
• Volumes were reconstructed using OsiriX software for gray-level segmentation and a self-developed module for Graph-cut segmentation.
• Accuracy was assessed by comparing reconstructed volumes with actual intraoperative measurements via linear regression.

Main Results:

• Both Graph-cut and gray-level segmentation algorithms could define porous polyethylene on CT scans.
• Linear regression analysis showed the Graph-cut algorithm's intercept was not significantly different from zero (P = 0.4172).
• The gray-level algorithm's intercept was significantly different from zero (P = 0.0055), indicating lower accuracy.

Conclusions:

• Porous polyethylene used in maxillofacial reconstruction can be effectively imaged using CT segmentation.
• The Graph-cut algorithm offers superior accuracy for volumetric assessment of porous polyethylene compared to the gray-level method.