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.

中文翻译：

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具有可控微结构参数的 3D 硅海绵的生成建模

椎骨微结构的研究通常需要昂贵的程序来获得具有研究中的特定病理的真实骨骼的物理扫描，因为目前还没有可用的方法来生成真实的计算机骨骼结构。在这里，我们建议应用生成对抗网络 (GAN) 的最新进展来开发这种方法。我们采用了已在其他上下文中大量使用的风格迁移技术，以便在图像对之间迁移风格，同时保留其信息内容。第一步，我们使用 Wasserstein 目标和梯度惩罚 (PWGAN-GP) 以渐进方式训练体积生成模型，以在计算机上创建逼真的骨骼结构补丁。训练集包含来自十二个人类椎骨（T12 或 L1）的 7660 个纯海绵骨样本，各向同性分辨率为 164um，并使用高分辨率外周定量 CT (Scanco XCT) 进行扫描。训练后，我们通过优化学习到的潜在空间中的向量 z 来生成具有定制微结构特性的新样本。为了解决这个优化问题，我们制定了一个可微的目标函数，该函数可以在用目标 3D 属性（样式）妥协外观（内容）的同时产生有效样本。学习到的潜在空间的属性有效地匹配了数据分布。此外，我们能够仅根据微观结构参数的预期变化来模拟骨质疏松症治疗恶化或治疗效果后产生的骨骼结构。