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Osteoporosis is a prevalent bone disease that causes fractures in fragilebones, leading to a decline in daily living activities. Dual-energy X-rayabsorptiometry (DXA) and quantitative computed tomography (QCT) are highlyaccurate for diagnosing osteoporosis; however, these modalities require specialequipment 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 aplain X-ray image for opportunistic screening, which is potentially useful forearly diagnosis. Existing methods have used multi-stage approaches consistingof extraction of the region of interest and simple regression to estimate BMD,which require a large amount of training data. Therefore, we propose anefficient method that learns decomposition into projections of bone-segmentedQCT for BMD estimation under limited datasets. The proposed method achievedhigh accuracy in BMD estimation, where Pearson correlation coefficients of0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMDestimation tasks, respectively, and the root mean square of the coefficient ofvariation values were 3.27 to 3.79% for four measurements with different poses.Furthermore, we conducted extensive validation experiments, includingmulti-pose, uncalibrated-CT, and compression experiments toward actualapplication in routine clinical practice.

Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography