尸体痉挛
骨矿物
医学
骨质疏松症
椎骨
核医学
层析合成
刚度
骨密度
双重能量
流离失所(心理学)
腰椎
生物医学工程
放射科
材料科学
解剖
腰椎
内科学
复合材料
乳腺癌
心理治疗师
癌症
乳腺摄影术
心理学
作者
Yener N. Yeni,D Oravec,Joshua Drost,Roger Zauel,Michael Flynn
出处
期刊:Journal of biomechanical engineering
[ASME International]
日期:2022-12-05
卷期号:145 (4)
摘要
Abstract Vertebral fractures are the most common osteoporotic fractures, but their prediction using standard bone mineral density (BMD) measurements from dual energy X-ray absorptiometry (DXA) is limited in accuracy. Stiffness, displacement, and strain distribution properties derived from digital tomosynthesis-based digital volume correlation (DTS-DVC) have been suggested as clinically measurable metrics of vertebral bone quality. However, the extent to which these properties correlate to vertebral strength is unknown. To establish this relationship, two independent experiments, one examining isolated T11 and the other examining L3 vertebrae within the L2–L4 segments from cadaveric donors were utilized. Following DXA and DTS imaging, the specimens were uniaxially compressed to fracture. BMD, bone mineral content (BMC), and bone area were recorded for the anteroposterior and lateromedial views from DXA, stiffness, endplate to endplate displacement and distribution statistics of intravertebral strains were calculated from DTS-DVC and vertebral strength was measured from mechanical tests. Regression models were used to examine the relationships of strength with the other variables. Correlations of BMD with vertebral strength varied between experimental groups (R2adj = 0.19–0.78). DTS-DVC derived properties contributed to vertebral strength independently from BMD measures (increasing R2adj to 0.64–0.95). DTS-DVC derived stiffness was the best single predictor (R2adj = 0.66, p < 0.0001) and added the most to BMD in models of vertebral strength for pooled T11 and L3 specimens (R2adj = 0.95, p < 0.0001). These findings provide biomechanical relevance to DTS-DVC calculated properties of vertebral bone and encourage further efforts in the development of the DTS-DVC approach as a clinical tool.
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