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：肌肉骨骼疾病（如肌少症和骨质疏松症）是老龄化过程中健康的主要障碍。尽管双能X线吸收测定法（DXA）和计算机断层扫描（CT）可用于评估肌肉骨骼状况，但由于成本、可及性（以及CT的高辐射暴露）等因素，频繁监测较为困难。我们提出了一种名为MSKdeX的方法，通过利用CT中的精细分割进行肌肉骨骼分解，从普通X线图像（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线图像分解为单个肌肉的CT投影，以推断瘦肌肉质量和肌肉体积。我们提出了目标级强度总和损失，这是一种简单但出乎意料有效的度量，对肌肉变形和投影方向具有不变性，利用了来自同一患者的CT和X线图像中的信息。尽管我们的方法本质上是无配对的图像到图像翻译，但我们也利用了骨骼刚性的特性，通过2D-3D刚性配准提供配对数据，在无配对训练中增加了强像素级监督。通过使用包含539名患者的数据集进行评估，我们证明了所提出的方法显著优于传统方法。预测指标与CT导出的真实指标之间的平均皮尔逊相关系数从0.460提高到0.863。我们相信，我们的方法开辟了一种新的肌肉骨骼诊断方法，并有可能扩展到多通道定量图像翻译任务的更广泛应用中。我们的源代码即将发布。