Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are highly accurate for diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. To frequently monitor bone health, low-cost, low-dose, and ubiquitously available diagnostic methods are highly anticipated. In this study, we aim to perform bone mineral density (BMD) estimation from a plain X-ray image for opportunistic screening, which is potentially useful for early diagnosis. Existing methods have used multi-stage approaches consisting of extraction of the region of interest and simple regression to estimate BMD, which require a large amount of training data. Therefore, we propose an efficient method that learns decomposition into projections of bone-segmented QCT for BMD estimation under limited datasets. The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3.27 to 3.79% for four measurements with different poses. Furthermore, we conducted extensive validation experiments, including multi-pose, uncalibrated-CT, and compression experiments toward actual application in routine clinical practice.

翻译：摘要：骨质疏松症是一种常见的骨骼疾病，会导致脆性骨折，进而影响日常生活活动能力。双能X射线吸收测定法（DXA）和定量计算机断层扫描（QCT）在诊断骨质疏松方面具有高度准确性，但这些检查方法需要特殊设备和扫描协议。为了频繁监测骨骼健康，低成本、低剂量且广泛可用的诊断方法备受期待。在本研究中，我们旨在通过普通X光图像进行骨密度（BMD）估计，以实现机会性筛查，这对早期诊断具有潜在价值。现有方法采用多阶段方法，包括提取感兴趣区域和简单回归估计BMD，这需要大量训练数据。为此，我们提出了一种高效方法，通过学习分解为骨骼分割QCT的投影，在有限数据集下进行BMD估计。所提方法在BMD估计中实现了高精度，其中DXA测量BMD和QCT测量BMD估计任务的皮尔逊相关系数分别达到0.880和0.920，且不同姿态下四次测量的变异系数均方根值为3.27%至3.79%。此外，我们进行了广泛的验证实验，包括多姿态、未校准CT以及压缩实验，以推动其在常规临床实践中的实际应用。