Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore.
Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany.
Arch Osteoporos. 2020 Feb 22;15(1):17. doi: 10.1007/s11657-020-0708-9.
This study aims to evaluate the impact of dose reduction through tube current and sparse sampling on multi-detector computed tomography (MDCT)-based femoral bone strength prediction using finite element (FE) analysis. FE-predicted femoral failure load obtained from MDCT scan data was not significantly affected by 50% dose reductions through sparse sampling. Further decrease in dose through sparse sampling (25% of original projections) and virtually reduced tube current (50% and 25% of the original dose) showed significant effects on the FE-predicted failure load results.
To investigate the effect of virtually reduced tube current and sparse sampling on multi-detector computed tomography (MDCT)-based femoral bone strength prediction using finite element (FE) analysis.
Routine MDCT data covering the proximal femur of 21 subjects (17 males; 4 females; mean age, 71.0 ± 8.8 years) without any bone diseases aside from osteoporosis were included in this study. Fifty percent and 75% dose reductions were achieved by virtually reducing tube current and by applying a sparse sampling strategy from the raw image data. Images were then reconstructed with a statistically iterative reconstruction algorithm. FE analysis was performed on all reconstructed images and the failure load was calculated. The root mean square coefficient of variation (RMSCV) and coefficient of correlation (R) were calculated to determine the variation in the FE-predicted failure load data for dose reductions, using original-dose MDCT scan as the standard of reference.
Fifty percent dose reduction through sparse sampling showed lower RMSCV and higher correlations when compared with virtually reduced tube current method (RMSCV = 5.70%, R = 0.96 vs. RMSCV = 20.78%, R = 0.79). Seventy-five percent dose reduction achieved through both methods (RMSCV = 22.38%, R = 0.80 for sparse sampling; RMSCV = 24.58%, R = 0.73 for reduced tube current) could not predict the failure load accurately.
Our simulations indicate that up to 50% reduction in radiation dose through sparse sampling can be used for FE-based prediction of femoral failure load. Sparse-sampled MDCT may allow fracture risk prediction and treatment monitoring in osteoporosis with less radiation exposure in the future.
研究虚拟降低管电流和稀疏采样对基于多排螺旋 CT(MDCT)的有限元(FE)分析预测股骨骨强度的影响。
本研究纳入了 21 名受试者(17 名男性;4 名女性;平均年龄 71.0±8.8 岁)的常规 MDCT 数据,这些受试者除了骨质疏松症外,均无任何骨骼疾病。通过虚拟降低管电流和从原始图像数据中应用稀疏采样策略,分别实现了 50%和 75%的剂量降低。然后使用统计迭代重建算法对所有重建图像进行 FE 分析,并计算失效负荷。使用原始剂量 MDCT 扫描作为参考,计算 FE 预测失效负荷数据的均方根变异系数(RMSCV)和相关系数(R),以确定剂量降低时的变化。
与虚拟降低管电流方法相比,稀疏采样的 50%剂量降低显示出更低的 RMSCV 和更高的相关性(RMSCV=5.70%,R=0.96 与 RMSCV=20.78%,R=0.79)。两种方法都实现了 75%的剂量降低(稀疏采样的 RMSCV=22.38%,R=0.80;管电流降低的 RMSCV=24.58%,R=0.73),无法准确预测失效负荷。
我们的模拟表明,通过稀疏采样降低 50%的辐射剂量可用于基于 FE 的股骨失效负荷预测。稀疏采样的 MDCT 将来可能允许在更少的辐射暴露下进行骨质疏松症的骨折风险预测和治疗监测。