Smoothing the Undersampled Carpal Bone Model with Small Volume and Large Curvature: A Feasibility Study

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Anatomy

Background:

• Carpal bones present complex 3D geometry with high surface curvature.
• Magnetic Resonance Imaging (MRI) reconstructions often result in undersampled carpal bone models with mesh defects.
• Accurate 3D carpal bone models are crucial for clinical applications like wrist kinematic analysis.

Purpose of the Study:

• To evaluate the effectiveness of various smoothing algorithms for improving 3D carpal bone models derived from in vivo MRI data.
• To identify the optimal smoothing technique for enhancing mesh quality while preserving anatomical integrity.

Main Methods:

• Five smoothing algorithms were assessed: Laplacian, Laplacian with pre-dilation, scale-dependent Laplacian, curvature flow, and inverse distance.
• Evaluation metrics included Relative Volume Difference, Hausdorff Distance, surface quality assessment, and preservation of morphological/morphometric properties.
• All eight carpal bones were analyzed using these methods.

Main Results:

• The scale-dependent Laplacian algorithm demonstrated superior performance across all evaluated metrics.
• This method significantly improved surface quality and maintained morphological and morphometric characteristics.
• Relative Volume Difference and Hausdorff Distance analyses supported the superiority of the scale-dependent Laplacian approach.

Conclusions:

• The scale-dependent Laplacian algorithm is highly suitable for refining undersampled 3D carpal bone models generated from MRI.
• This technique effectively addresses mesh irregularities and preserves critical anatomical features in complex carpal bone geometry.