Musculoskeletal diseases such as sarcopenia and osteoporosis are major obstacles to health during aging. Although dual-energy X-ray absorptiometry (DXA) and computed tomography (CT) can be used to evaluate musculoskeletal conditions, frequent monitoring is difficult due to the cost and accessibility (as well as high radiation exposure in the case of CT). We propose a method (named MSKdeX) to estimate fine-grained muscle properties from a plain X-ray image, a low-cost, low-radiation, and highly accessible imaging modality, through musculoskeletal decomposition leveraging fine-grained segmentation in CT. We train a multi-channel quantitative image translation model to decompose an X-ray image into projections of CT of individual muscles to infer the lean muscle mass and muscle volume. We propose the object-wise intensity-sum loss, a simple yet surprisingly effective metric invariant to muscle deformation and projection direction, utilizing information in CT and X-ray images collected from the same patient. While our method is basically an unpaired image-to-image translation, we also exploit the nature of the bone's rigidity, which provides the paired data through 2D-3D rigid registration, adding strong pixel-wise supervision in unpaired training. Through the evaluation using a 539-patient dataset, we showed that the proposed method significantly outperformed conventional methods. The average Pearson correlation coefficient between the predicted and CT-derived ground truth metrics was increased from 0.460 to 0.863. We believe our method opened up a new musculoskeletal diagnosis method and has the potential to be extended to broader applications in multi-channel quantitative image translation tasks. Our source code will be released soon.

翻译：骨骼肌肉疾病（如肌少症和骨质疏松症）是老龄化健康面临的主要挑战。尽管双能X射线吸收测定法（DXA）和计算机断层扫描（CT）可用于评估骨骼肌肉状况，但由于成本、可及性（以及CT的高辐射暴露）问题，频繁监测较为困难。我们提出一种名为MSKdeX的方法，利用CT中精细分割的骨骼肌肉分解技术，从普通X光图像（一种低成本、低辐射且高度可及的成像模态）中估计精细化的肌肉属性。我们训练了一个多通道定量图像翻译模型，将X光图像分解为各肌肉CT投影数据，以推断肌肉质量和肌肉体积。我们提出了目标级强度总和损失函数，这是一种简单却出人意料的有效度量，对肌肉变形和投影方向具有不变性，并利用了来自同一患者的CT和X光图像信息。尽管我们的方法本质上是非配对图像到图像的翻译，但我们还利用了骨骼刚性的特性，通过2D-3D刚性配准获得配对数据，从而在非配对训练中增加了强像素级监督。通过使用539名患者数据集的评估，我们证明了所提方法显著优于传统方法：预测指标与CT-derived金标准指标之间的平均皮尔逊相关系数从0.460提升至0.863。我们相信该方法开辟了新的骨骼肌肉诊断途径，并有望扩展至更广泛的多通道定量图像翻译任务。我们的源代码将很快发布。