Quantitative Analysis of Age-Associated Bone Mineral Density Variations via Automated Segmentation: Using CT Scans and Radon Transform to Accurately Examine and Assess the Vertebrae

Abstract

The proposed research study investigates the relationship between Bone Mineral Density (BMD) values extracted from computed tomography (CT) scans, with a primary focus on their significance in vertebral segmentation. The study includes 25 subjects spanning five age groups; the study reveals a continuous reduction in mean Hounsfield Unit (HU) values with aging, validated by statistical analysis that emphasizes the importance of these findings across different vertebral levels. The data exposes a steady decline in mean BMD values with age, ranging from 175.05 HU in the 40s to 51.8 HU in the over-80s age group, exhibiting statistically significant differences ( $\text{p} < 0.05$ ). Subgroup analysis provides granular insights into age-related variations at specific lumbar vertebrae levels by contributing valuable information to the complexities of bone health assessment. The research connects mathematics and medical imaging to improve our understanding of vertebral characteristics by introducing the Radon Transform as a fundamental tool in CT image reconstruction. These findings include both p and mean values which have the potential to revolutionize the field of bone health assessment by affecting public health policy and opening the way for more personalized and successful treatment options. This includes the earlier detection of conditions like osteoporosis and improvements in patient care.

Keywords

• Bone mineral density
• computed tomography
• radon transform
• automated BMD calculations
• bone health assessment
• vertebral segmentation