Bioprinting is an emerging tissue engineering method used to generate cell-laden scaffolds with high spatial resolution. Bioprinted vascularized bone grafts are a potential application of this technology that would meet a critical clinical need, since current approaches to volumetric bone repair have significant limitations. However, generation of vascular networks suitable for bioprinting is challenging. Here, we propose a novel Q-learning approach to quickly generate 3D vascular networks within patient-specific bone geometry that are optimized for bioprinting. First, the inlet and outlet locations are specified and the scenario is modeled using a grid world for initial agent training. Next, the path planned in the grid world environment is converted to a Bezier curve, which is then used to generate the final 3D vascularized bone model. The vessels generated using this procedure have minimal tortuosity, which increases the likelihood of successful bioprinting. Furthermore, the ability to specify inlet and outlet position is necessary for both surgical feasibility as well as generation of more complex vascular networks. In total, this study demonstrates the reliability of our reinforcement learning method for automated generation of 3D vascular networks within patient-specific geometry that can be used for bioprinting vascularized bone grafts.

中文翻译：

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利用Q-Learning生成3D血管网络以进行骨骼生物打印

生物打印是一种新兴的组织工程方法，用于生成具有高空间分辨率的充满细胞的支架。生物打印的血管化骨移植物是该技术的一种潜在应用，它将满足关键的临床需求，因为当前的体积骨修复方法存在重大局限性。然而，适合于生物打印的血管网络的产生是具有挑战性的。在这里，我们提出了一种新颖的Q学习方法，可以在针对生物打印进行了优化的患者特定骨骼几何形状内快速生成3D血管网络。首先，指定入口和出口位置，并使用网格世界对场景进行建模以进行初始代理训练。接下来，将在网格世界环境中规划的路径转换为Bezier曲线，然后将其用于生成最终的3D带血管骨骼模型。使用此程序生成的血管具有最小的曲折度，这增加了成功进行生物打印的可能性。此外，对于外科手术的可行性以及更复杂的血管网络的产生，指定入口和出口位置的能力是必需的。总的来说，这项研究证明了我们的强化学习方法在患者特定几何形状内自动生成3D血管网络的可靠性，该方法可用于生物打印血管化的骨移植物。