  Purpose: To assess the feasibility of deep learning-based high resolution
synthetic CT generation from MRI scans of the lower arm for orthopedic
applications.
  Methods: A conditional Generative Adversarial Network was trained to
synthesize CT images from multi-echo MR images. A training set of MRI and CT
scans of 9 ex vivo lower arms was acquired and the CT images were registered to
the MRI images. Three-fold cross-validation was applied to generate independent
results for the entire dataset. The synthetic CT images were quantitatively
evaluated with the mean absolute error metric, and Dice similarity and surface
to surface distance on cortical bone segmentations.
  Results: The mean absolute error was 63.5 HU on the overall tissue volume and
144.2 HU on the cortical bone. The mean Dice similarity of the cortical bone
segmentations was 0.86. The average surface to surface distance between bone on
real and synthetic CT was 0.48 mm. Qualitatively, the synthetic CT images
corresponded well with the real CT scans and partially maintained high
resolution structures in the trabecular bone. The bone segmentations on
synthetic CT images showed some false positives on tendons, but the general
shape of the bone was accurately reconstructed.
  Conclusions: This study demonstrates that high quality synthetic CT can be
generated from MRI scans of the lower arm. The good correspondence of the bone
segmentations demonstrates that synthetic CT could be competitive with real CT
in applications that depend on such segmentations, such as planning of
orthopedic surgery and 3D printing.
