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首页> 外文期刊>Biomedical Engineering, IEEE Transactions on >A Robust Algorithm for Thickness Computation at Low Resolution and Its Application to In Vivo Trabecular Bone CT Imaging
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A Robust Algorithm for Thickness Computation at Low Resolution and Its Application to In Vivo Trabecular Bone CT Imaging

机译:一种低分辨率厚度计算的鲁棒算法及其在体内小梁骨CT成像中的应用

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摘要

Adult bone diseases, especially osteoporosis, lead to increased risk of fracture which in turn is associated with substantial morbidity, mortality, and financial costs. Clinically, osteoporosis is defined by low bone mineral density; however, increasing evidence suggests that the microarchitectural quality of trabecular bone (TB) is an important determinant of bone strength and fracture risk. Accurate measures of TB thickness and marrow spacing is of significant interest for early diagnosis of osteoporosis or treatment effects. Here, we present a new robust algorithm for computing TB thickness and marrow spacing at a low resolution achievable in vivo. The method uses a star-line tracing technique that effectively deals with partial voluming effects of in vivo imaging with voxel size comparable to TB thickness. Also, the method avoids the problem of digitization associated with conventional algorithms based on sampling distance transform along skeletons. Accuracy of the method was examined using computer-generated phantom images, while the robustness of the method was evaluated on human ankle specimens in terms of stability across a wide range of voxel sizes, repeat scan reproducibility under in vivo conditions, and correlation between thickness values computed at ex vivo and in vivo imaging resolutions. Also, the sensitivity of the method was examined by evaluating its ability to predict the bone strength of cadaveric specimens. Finally, the method was evaluated in a human study involving 40 healthy young-adult volunteers (age: 19–21 years; 20 males and 20 females) and ten athletes (age: 19–21 years; six males and four females). Across a wide range of voxel sizes, the new method is significantly more accurate and robust as compared to conventional methods. Both TB thickness and marrow spacing measures computed using the new method demonstrated strong- associations ($R^2in [0.83,0.87]$) with bone strength. Also, the TB thickness and marrow spacing measures allowed discrimination between male and female volunteers ($pin [0.01,0.04]$) as well as between athletes and nonathletes ($pin [0.005,0.03]$).
机译:成人的骨病,尤其是骨质疏松症,导致骨折的风险增加,进而导致发病率,死亡率和财务费用的增加。临床上,骨质疏松症的定义是低骨密度。但是,越来越多的证据表明,小梁骨(TB)的微结构质量是决定骨强度和骨折风险的重要因素。对于骨质疏松症的早期诊断或治疗效果,准确测量结核厚度和骨髓间距非常重要。在这里,我们提出了一种新的健壮算法,可以在体内以较低的分辨率计算结核病厚度和骨髓间距。该方法使用星线跟踪技术,可有效处理体内成像的体体积与TB厚度相当的部分体积效应。而且,该方法避免了与基于沿骨骼的采样距离变换的常规算法相关联的数字化问题。使用计算机生成的幻影图像检查方法的准确性,同时在人的脚踝样本上评估该方法的鲁棒性,包括在各种体素大小范围内的稳定性,在体内条件下重复扫描的可重复性以及厚度值之间的相关性以离体和体内成像分辨率计算。此外,通过评估其预测尸体标本骨强度的能力来检查该方法的敏感性。最后,该方法在一项包含40名健康的年轻志愿者(年龄:19-21岁;男性20位,女性20位)和10名运动员(年龄:19-21岁; 6位男性和4位女性)的人体研究中进行了评估。与常规方法相比,在各种体素大小范围内,该新方法都显着更准确,更可靠。使用新方法计算的结核病厚度和骨髓间距测量值均显示出与骨强度的强相关性([0.83,0.87] $ R ^ 2)。同样,结核病的厚度和骨髓间隔测量值可以区分男性和女性志愿者($ pin [0.01,0.04] $)以及运动员和非运动员($ pin [0.005,0.03] $)。

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