Osteoporosis and fragility fractures have emerged as major public health
concerns in an aging population. However, measuring age-related changes in bone
density using dual-energy X-ray absorptiometry has limited personalized risk
assessment due to susceptibility to interference from various factors. In this
study, we propose an innovative statistical model of bone pixel distribution in
fine-segmented computed tomography (CT) images, along with a novel approach to
measuring bone density based on CT values of bone pixels. Our findings indicate
that bone density exhibits a linear decline with age during adulthood between
the ages of 39 and 80, with the rate of decline being approximately 1.6 times
faster in women than in men. This contradicts the widely accepted notion that
bone density starts declining in women at menopause and in men at around 50
years of age. The linearity of age-related changes provides further insights
into the dynamics of the aging human body. Consequently, our findings suggest
that the definition of osteoporosis by the World Health Organization should be
revised to the standard deviation of age-based bone density. Furthermore, these
results open up new avenues for research in bone health care and clinical
investigation of osteoporosis.

提出了一种新颖的骨像素分布统计模型和基于 CT 图像中骨像素的密度测量方法，研究发现骨密度在成年期的 39 至 80 岁之间呈线性下降，女性下降速度大约是男性的 1.6 倍，与广泛接受的骨密度女性在更年期开始下降、男性在大约 50 岁开始下降的观点相矛盾，推测世界卫生组织对骨质疏松症的定义应进行修订为与年龄相关的骨密度标准差，为骨健康护理和临床骨质疏松症研究开辟了新研究方向。

基于新的骨密度定义和计算机断层扫描技术的人体骨密度随年龄变化规律

The changing rule of human bone density with aging based on a novel  definition and mensuration of bone density with computed tomography

In this work, we present a method for synthetic CT (sCT) generation from
zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the
image, with a particular focus on the accurate bone density value prediction.
We propose a loss function that favors a spatially sparse region in the image.
We harness the ability of a multi-task network to produce correlated outputs as
a framework to enable localisation of region of interest (RoI) via
classification, emphasize regression of values within RoI and still retain the
overall accuracy via global regression. The network is optimized by a composite
loss function that combines a dedicated loss from each task. We demonstrate how
the multi-task network with RoI focused loss offers an advantage over other
configurations of the network to achieve higher accuracy of performance. This
is relevant to sCT where failure to accurately estimate high Hounsfield Unit
values of bone could lead to impaired accuracy in clinical applications. We
compare the dose calculation maps from the proposed sCT and the real CT in a
radiation therapy treatment planning setup.

本研究提出了一种利用多任务网络从零回波时间 MRI 生成合成 CT（sCT）的方法，重点关注骨密度值的准确预测，并通过分类定位兴趣区域（RoI）和全局回归来优化网络性能。通过与真实 CT 比较，我们证明了该方法可以提高在放射治疗计划中的准确性。