Insights into geometric deviations of medical 3d-printing: a phantom study utilizing error propagation analysis

Area of Science:

• Medical 3D-printing
• Quality Assurance
• Geometric Deviation Analysis

Background:

• Medical 3D-printing necessitates robust quality assurance due to potential errors in segmentation, digital editing, and printing.
• Current approaches lack a unified concept for evaluating these partial errors and their cumulative effect.
• Understanding error propagation is crucial for ensuring patient safety and optimizing the 3D-printing process.

Purpose of the Study:

• To individually evaluate segmentation error (SegE), digital editing error (DEE), and printing error (PrE).
• To examine the cumulative effect of partial errors using error propagation.
• To identify key parameters influencing geometric deviations in medical 3D-printing.

Main Methods:

• Surface deviation analyses were employed to assess partial errors.
• Investigated the impact of parameters like CT slice thickness, reconstruction kernel, threshold, software, and printer type.
• Calculated total error as the sum of SegE, DEE, and PrE.

Main Results:

• Higher threshold values and thicker CT slices increased segmentation deviations.
• Bone kernel segmentations showed artifact formation; soft kernels were advantageous.
• Printing errors were primarily influenced by build plate adhesion; total error ranged from 0.0093 mm to 0.3494 mm.

Conclusions:

• Identified critical parameters influencing geometric deviations in medical 3D-printing.
• Error propagation provides a framework for understanding cumulative errors and enabling analytical approaches.
• Soft reconstruction kernels are beneficial for segmentation quality.