Geometric plane alignment as a methodological requirement for accurate angular measurement in three-dimensional...

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Radiology

Background:

• Accurate angular measurements from 3D radiological imaging are vital for morphometric analysis, surgical planning, and implant design.
• Standard sectional planes (sagittal, coronal, transverse) can introduce projection distortions, compromising true spatial geometry.
• Existing methods may lack the precision required for critical clinical applications.

Purpose of the Study:

• To develop and validate a reoriented plane protocol for enhancing angular measurement accuracy and reproducibility in 3D imaging.
• To compare the precision of the novel reoriented plane method against standard sectional plane analysis.
• To establish a reliable framework for angular measurements in craniofacial analysis.

Main Methods:

• Utilized five plastic skull models to define four key craniofacial angles.
• Performed direct goniometric measurements as the reference standard.
• Analyzed CT scans using both standard sectional planes and a novel reoriented plane approach, aligning with true landmark geometry.
• Assessed intraobserver and interobserver reliability using intraclass correlation coefficients (ICC) and Bland-Altman analyses.

Main Results:

• Reoriented plane measurements closely matched goniometric reference values with minimal bias (e.g., 0.002° for ∠Co-Go-Gn).
• Standard sectional measurements showed significant deviations, with mean biases up to -36.47° in coronal views.
• The reoriented plane method demonstrated excellent intraobserver and interobserver reliability (ICC up to 0.950, p < 0.001).

Conclusions:

• Aligning the measurement plane with the true landmark-defined plane significantly enhances angular measurement accuracy and reproducibility in 3D radiological datasets.
• The reoriented plane protocol offers a robust methodological improvement for clinical and research applications in craniofacial analysis.
• This validated approach addresses limitations of standard sectional analysis, improving diagnostic and planning capabilities.