Abstract：
BACKGROUND: Forty to fifty percent of women and 13%-22% of men experience an osteoporosis-related fragility fracture in their lifetimes. After the age of 50 years, the risk of hip fracture doubles in every 10 years. x-Ray based DXA is currently clinically used to diagnose osteoporosis and predict fracture risk. However, it provides only 2-D representation of bone and is associated with other technical limitations. Thus, alternative methods are needed.
OBJECTIVE: To develop and evaluate an ultra-low dose (ULD) hip CT-based automated method for assessment of volumetric bone mineral density (vBMD) at proximal femoral subregions.
METHODS: An automated method was developed to segment the proximal femur in ULD hip CT images and delineate femoral subregions. The computational pipeline consists of deep learning (DL)-based computation of femur likelihood map followed by shape model-based femur segmentation and finite element analysis-based warping of a reference subregion labeling onto individual femur shapes. Finally, vBMD is computed over each subregion in the target image using a calibration phantom scan. A total of 100 participants (50 females) were recruited from the Genetic Epidemiology of COPD (COPDGene) study, and ULD hip CT imaging, equivalent to 18 days of background radiation received by U.S. residents, was performed on each participant. Additional hip CT imaging using a clinical protocol was performed on 12 participants and repeat ULD hip CT was acquired on another five participants. ULD CT images from 80 participants were used to train the DL network; ULD CT images of the remaining 20 participants as well as clinical and repeat ULD CT images were used to evaluate the accuracy, generalizability, and reproducibility of segmentation of femoral subregions. Finally, clinical CT and repeat ULD CT images were used to evaluate accuracy and reproducibility of ULD CT-based automated measurements of femoral vBMD.
RESULTS: Dice scores of accuracy (n = 20), reproducibility (n = 5), and generalizability (n = 12) of ULD CT-based automated subregion segmentation were 0.990, 0.982, and 0.977, respectively, for the femoral head and 0.941, 0.970, and 0.960, respectively, for the femoral neck. ULD CT-based regional vBMD showed Pearson and concordance correlation coefficients of 0.994 and 0.977, respectively, and a root-mean-square coefficient of variation (RMSCV) (%) of 1.39% with the clinical CT-derived reference measure. After 3-digit approximation, each of Pearson and concordance correlation coefficients as well as intraclass correlation coefficient (ICC) between baseline and repeat scans were 0.996 with RMSCV of 0.72%. Results of ULD CT-based bone analysis on 100 participants (age (mean ± SD) 73.6 ± 6.6 years) show that males have significantly greater (p < 0.01) vBMD at the femoral head and trochanteric regions than females, while females have moderately greater vBMD (p = 0.05) at the medial half of the femoral neck than males.
CONCLUSIONS: Deep learning, combined with shape model and finite element analysis, offers an accurate, reproducible, and generalizable algorithm for automated segmentation of the proximal femur and anatomic femoral subregions using ULD hip CT images. ULD CT-based regional measures of femoral vBMD are accurate and reproducible and demonstrate regional differences between males and females.
OBJECTIVE: To develop and evaluate an ultra-low dose (ULD) hip CT-based automated method for assessment of volumetric bone mineral density (vBMD) at proximal femoral subregions.
METHODS: An automated method was developed to segment the proximal femur in ULD hip CT images and delineate femoral subregions. The computational pipeline consists of deep learning (DL)-based computation of femur likelihood map followed by shape model-based femur segmentation and finite element analysis-based warping of a reference subregion labeling onto individual femur shapes. Finally, vBMD is computed over each subregion in the target image using a calibration phantom scan. A total of 100 participants (50 females) were recruited from the Genetic Epidemiology of COPD (COPDGene) study, and ULD hip CT imaging, equivalent to 18 days of background radiation received by U.S. residents, was performed on each participant. Additional hip CT imaging using a clinical protocol was performed on 12 participants and repeat ULD hip CT was acquired on another five participants. ULD CT images from 80 participants were used to train the DL network; ULD CT images of the remaining 20 participants as well as clinical and repeat ULD CT images were used to evaluate the accuracy, generalizability, and reproducibility of segmentation of femoral subregions. Finally, clinical CT and repeat ULD CT images were used to evaluate accuracy and reproducibility of ULD CT-based automated measurements of femoral vBMD.
RESULTS: Dice scores of accuracy (n = 20), reproducibility (n = 5), and generalizability (n = 12) of ULD CT-based automated subregion segmentation were 0.990, 0.982, and 0.977, respectively, for the femoral head and 0.941, 0.970, and 0.960, respectively, for the femoral neck. ULD CT-based regional vBMD showed Pearson and concordance correlation coefficients of 0.994 and 0.977, respectively, and a root-mean-square coefficient of variation (RMSCV) (%) of 1.39% with the clinical CT-derived reference measure. After 3-digit approximation, each of Pearson and concordance correlation coefficients as well as intraclass correlation coefficient (ICC) between baseline and repeat scans were 0.996 with RMSCV of 0.72%. Results of ULD CT-based bone analysis on 100 participants (age (mean ± SD) 73.6 ± 6.6 years) show that males have significantly greater (p < 0.01) vBMD at the femoral head and trochanteric regions than females, while females have moderately greater vBMD (p = 0.05) at the medial half of the femoral neck than males.
CONCLUSIONS: Deep learning, combined with shape model and finite element analysis, offers an accurate, reproducible, and generalizable algorithm for automated segmentation of the proximal femur and anatomic femoral subregions using ULD hip CT images. ULD CT-based regional measures of femoral vBMD are accurate and reproducible and demonstrate regional differences between males and females.
摘要：
背景：40％至50％的女性和13％-22％的男性在其一生中经历骨质疏松症相关的脆性骨折。50岁以后,髋部骨折的风险每10年增加一倍。基于X射线的DXA目前在临床上用于诊断骨质疏松症和预测骨折风险。然而,它仅提供骨骼的二维表示，并且与其他技术限制有关。因此，需要替代方法。
目的：开发并评估一种基于超低剂量（ULD）髋关节CT的自动化方法，用于评估股骨近端亚区的体积骨矿物质密度（vBMD）。
方法：开发了一种自动方法，用于在ULD髋关节CT图像中分割股骨近端并描绘股骨亚区域。计算管道包括基于深度学习（DL）的股骨似然图计算，然后是基于形状模型的股骨分割和基于有限元分析的参考子区域的变形，标记到各个股骨形状上。最后,使用校准体模扫描在目标图像中的每个子区域上计算vBMD。共有100名参与者（50名女性）从COPD遗传流行病学（COPDGene）研究中招募，和ULD髋关节CT成像，相当于美国居民接受的18天背景辐射，对每个参与者进行。使用临床方案对12名参与者进行了额外的髋关节CT成像，并对另外5名参与者进行了重复的ULD髋关节CT成像。80名参与者的ULDCT图像用于训练DL网络；其余20名参与者的ULDCT图像以及临床和重复ULDCT图像用于评估准确性。概括性，和股骨亚区域分割的可重复性。最后,使用临床CT和重复ULDCT图像评估基于ULDCT的股骨vBMD自动测量的准确性和可重复性.
结果：骰子的准确性得分（n=20），再现性(n=5)，基于ULDCT的自动子区域分割的可泛化性（n=12）分别为0.990、0.982和0.977，股骨头分别为0.941、0.970和0.960，股骨颈.基于ULDCT的区域vBMD显示皮尔逊和一致性相关系数分别为0.994和0.977，和均方根变异系数（RMSCV）（％）1.39％与临床CT衍生的参考措施。经过3位数的近似,基线和重复扫描之间的Pearson和一致性相关系数以及组内相关系数(ICC)均为0.996,RMSCV为0.72%.对100名参与者（年龄（平均值±SD）73.6±6.6岁）的ULDCT骨分析结果表明，男性在股骨头和转子区域的vBMD明显大于（p<0.01）女性，而女性在股骨颈内侧半部的vBMD稍大于男性（p=0.05）。
结论：深度学习，结合形状模型和有限元分析，提供了一个准确的，可重复,使用ULD髋关节CT图像自动分割股骨近端和解剖股骨亚区域的通用算法。基于ULDCT的股骨vBMD区域测量是准确且可重复的，并显示了男性和女性之间的区域差异。
目的：开发并评估一种基于超低剂量（ULD）髋关节CT的自动化方法，用于评估股骨近端亚区的体积骨矿物质密度（vBMD）。
方法：开发了一种自动方法，用于在ULD髋关节CT图像中分割股骨近端并描绘股骨亚区域。计算管道包括基于深度学习（DL）的股骨似然图计算，然后是基于形状模型的股骨分割和基于有限元分析的参考子区域的变形，标记到各个股骨形状上。最后,使用校准体模扫描在目标图像中的每个子区域上计算vBMD。共有100名参与者（50名女性）从COPD遗传流行病学（COPDGene）研究中招募，和ULD髋关节CT成像，相当于美国居民接受的18天背景辐射，对每个参与者进行。使用临床方案对12名参与者进行了额外的髋关节CT成像，并对另外5名参与者进行了重复的ULD髋关节CT成像。80名参与者的ULDCT图像用于训练DL网络；其余20名参与者的ULDCT图像以及临床和重复ULDCT图像用于评估准确性。概括性，和股骨亚区域分割的可重复性。最后,使用临床CT和重复ULDCT图像评估基于ULDCT的股骨vBMD自动测量的准确性和可重复性.
结果：骰子的准确性得分（n=20），再现性(n=5)，基于ULDCT的自动子区域分割的可泛化性（n=12）分别为0.990、0.982和0.977，股骨头分别为0.941、0.970和0.960，股骨颈.基于ULDCT的区域vBMD显示皮尔逊和一致性相关系数分别为0.994和0.977，和均方根变异系数（RMSCV）（％）1.39％与临床CT衍生的参考措施。经过3位数的近似,基线和重复扫描之间的Pearson和一致性相关系数以及组内相关系数(ICC)均为0.996,RMSCV为0.72%.对100名参与者（年龄（平均值±SD）73.6±6.6岁）的ULDCT骨分析结果表明，男性在股骨头和转子区域的vBMD明显大于（p<0.01）女性，而女性在股骨颈内侧半部的vBMD稍大于男性（p=0.05）。
结论：深度学习，结合形状模型和有限元分析，提供了一个准确的，可重复,使用ULD髋关节CT图像自动分割股骨近端和解剖股骨亚区域的通用算法。基于ULDCT的股骨vBMD区域测量是准确且可重复的，并显示了男性和女性之间的区域差异。
