Accuracy of bone mineral density quantification using dual-layer spectral detector CT: a phantom study

European radiology(2017)

引用 61|浏览24
暂无评分
摘要
Objectives To investigate the accuracy of bone mineral density (BMD) quantification using dual-layer spectral detector CT (SDCT) at various scan protocols. Methods Two validated anthropomorphic phantoms containing inserts of 50–200 mg/cm 3 calcium hydroxyapatite (HA) were scanned using a 64-slice SDCT scanner at various acquisition protocols (120 and 140 kVp, and 50, 100 and 200 mAs). Regions of interest (ROIs) were placed in each insert and mean attenuation profiles at monochromatic energy levels (90–200 keV) were constructed. These profiles were fitted to attenuation profiles of pure HA and water to calculate HA concentrations. For comparison, one phantom was scanned using dual energy X-ray absorptiometry (DXA). Results At both 120 and 140 kVp, excellent correlations (R = 0.97, P < 0.001) were found between true and measured HA concentrations. Mean error for all measurements at 120 kVp was -5.6 ± 5.7 mg/cm 3 (-3.6 ± 3.2%) and at 140 kVp -2.4 ± 3.7 mg/cm 3 (-0.8 ± 2.8%). Mean measurement errors were smaller than 6% for all acquisition protocols. Strong linear correlations (R 2 ≥ 0.970, P < 0.001) with DXA were found. Conclusions SDCT allows for accurate BMD quantification and potentially opens up the possibility for osteoporosis evaluation and opportunistic screening in patients undergoing SDCT for other clinical indications. However, patient studies are needed to extend and translate our findings. Key points • Dual - layer spectral detector CT allows for accurate bone mineral density quantification . • BMD measurements on SDCT are strongly linearly correlated to DXA . • SDCT , acquired for several indications , may allow for evaluation of osteoporosis . • This potentially opens up the possibility for opportunistic osteoporosis screening .
更多
查看译文
关键词
Dual-energy CT,Dual-layer spectral detector CT,Dual energy X-Ray absorptiometry,Bone mineral density,Material decomposition
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要