  Research in vertebral bone micro-structure generally requires costly
procedures to obtain physical scans of real bone with a specific pathology
under study, since no methods are available yet to generate realistic bone
structures in-silico. Here we propose to apply recent advances in generative
adversarial networks (GANs) to develop such a method. We adapted style-transfer
techniques, which have been largely used in other contexts, in order to
transfer style between image pairs while preserving its informational content.
In a first step, we trained a volumetric generative model in a progressive
manner using a Wasserstein objective and gradient penalty (PWGAN-GP) to create
patches of realistic bone structure in-silico. The training set contained 7660
purely spongeous bone samples from twelve human vertebrae (T12 or L1) with
isotropic resolution of 164um and scanned with a high resolution peripheral
quantitative CT (Scanco XCT). After training, we generated new samples with
tailored micro-structure properties by optimizing a vector z in the learned
latent space. To solve this optimization problem, we formulated a
differentiable goal function that leads to valid samples while compromising the
appearance (content) with target 3D properties (style). Properties of the
learned latent space effectively matched the data distribution. Furthermore, we
were able to simulate the resulting bone structure after deterioration or
treatment effects of osteoporosis therapies based only on expected changes of
micro-structural parameters. Our method allows to generate a virtually infinite
number of patches of realistic bone micro-structure, and thereby likely serves
for the development of bone-biomarkers and to simulate bone therapies in
advance.
