Comparison of CT attenuation and DEXA-derived bone mineral density in porcine femur specimens: an ex vivo methodological feasibility study

Abstract

Abstract Dual-energy X-ray absorptiometry (DEXA) is the clinical reference standard for bone mineral density assessment, while computed tomography (CT) may enable opportunistic evaluation from routinely acquired scans. This methodological feasibility study evaluated whether AI-assisted CT segmentation can generate regional attenuation measurements corresponding to DEXA-derived measurements. Thirty porcine femur specimens underwent repeated DEXA and CT imaging across 75 observation entries under controlled experimental conditions, including sequential hydrochloric acid exposure cycles. AI-based semantic segmentation implemented in MONAI automatically delineated proximal femoral regions corresponding to DEXA regions of interest. Correlation and reliability analyses were performed at specimen and region levels. AI segmentation achieved high performance across anatomical regions (Dice > 0.84). CT-derived measurements correlated strongly with DEXA at the specimen level (Pearson r  = .78) and showed moderate but consistent region-level correlations ( r  = .64). Reliability was excellent, with ICC values ranging from 0.978 to 0.988. Automated CT-based attenuation analysis provides reproducible regional measurements that correlate with DEXA in a controlled setting. The proposed phantom-independent framework enables scalable and standardized extraction of CT-derived data, supporting potential application in larger or retrospective datasets, while not replacing calibrated quantitative CT approaches.