Bone mineral density estimation from a plain X-ray image by learning
  decomposition into projections of bone-segmented computed tomography
Yi Gu Yoshito Otake Keisuke Uemura Mazen Soufi Masaki Takao Hugues Talbot Seiji Okada Nobuhiko Sugano Yoshinobu Sato
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.