  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.
