Rezayof Omid, Huang Xinyuan, Kamaraj Meenakshi, John Johnson V, Alambeigi Farshid
Walker Department of Mechanical Engineering and Texas Robotics, University of Texas at Austin, TX, USA.
Terasaki Institute for Biomedical Innovation, Los Angeles, CA, USA.
IEEE Robot Autom Lett. 2024 Nov;9(11):10543-10550. doi: 10.1109/lra.2024.3474483. Epub 2024 Oct 3.
Tissue engineering techniques and particularly bioprinting using handheld devices and robotic systems have recently demonstrated promising outcomes to address volumetric muscle loss injuries. Nevertheless, these approaches suffer from insufficient printing precision and/or lack of quantitative analysis of the thickness and uniformity of bioprinted constructs (BPCs) - which are critical for ensuring cell viability and growth. To address these limitations, in this study, we present a framework for robotic bioprinting and complementary vision-based algorithms to quantitatively analyze thickness and uniformity of BPCs with curved geometries. The performance of the proposed robotic bioprinting and complementary algorithms has been thoroughly evaluated using various simulation and experimental studies on BPCs with constant and variable thicknesses. The results clearly demonstrate the remarkable and accurate performance of the proposed method in calculating the thickness and its variations along the geometry of the BPCs.
组织工程技术,尤其是使用手持设备和机器人系统的生物打印技术,最近已显示出在解决大面积肌肉损失损伤方面的良好前景。然而,这些方法存在打印精度不足和/或缺乏对生物打印构建体(BPC)厚度和均匀性的定量分析的问题,而这对于确保细胞活力和生长至关重要。为了解决这些限制,在本研究中,我们提出了一个用于机器人生物打印的框架以及基于视觉的互补算法,以定量分析具有弯曲几何形状的BPC的厚度和均匀性。通过对具有恒定和可变厚度的BPC进行各种模拟和实验研究,对所提出的机器人生物打印和互补算法的性能进行了全面评估。结果清楚地表明了所提出方法在计算BPC几何形状上的厚度及其变化方面的卓越和准确性能。