Optimized post-processing parameters for dry bone computed tomography imaging: Recommendations for diverse research applications

Abstract

Objective

This study aimed to compare the performance of different convolution kernels and reconstruction matrices in enhancing computed tomography (CT) image quality and three-dimensional (3D) reconstruction quality. Based on these findings, recommendations for optimizing CT post-processing parameters are provided to meet various research needs.

Methods

Forty-nine dry human femurs, excavated from archaeological sites, were selected for analysis. Two blinded reviewers scored the CT images from four convolution kernel and reconstruction matrix combinations using a five-point Likert scale for image quality. For 3D reconstruction quality, models generated by optical scanners served as the gold standard and were compared with four CT groups. Measurements included length, angle, cross-sectional area, and volume.

Results

CT images processed with the Br59 + 512 group achieved the highest image quality scores. For 3D reconstruction quality, a significant difference in length measurements was observed between the Br40 + 512 and Br40 + 1024 groups (P < 0.05). Angle measurements showed no significant differences across all groups. In cross-sectional area and volume measurements, the Br40 + 1024 group showed no statistically significant difference from the reference group.

Conclusion

Sharp convolution kernels are associated with higher image quality, whereas soft kernels help reduce noise and enhance 3D reconstruction quality. Although larger reconstruction matrices do not significantly improve image quality based on visual assessment, they contribute to higher-quality 3D models. We recommend the Br59 kernel with a 512 matrix for optimal image observation and the Br40 kernel with a 1024 matrix for 3D reconstruction and morphological analyses.