Image-based 3D-printing for biomedical applications

用于生物医学应用的基于图像的 3D 打印

• 批准号: RGPIN-2020-06856
• 负责人: Holdsworth, David
• 金额: $ 2.33万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741385
• 关键词: Image; based; 3D; printing; biomedical

Motivation: There has been considerable recent development in the area of additive manufacturing ("3D-printing"), including the development of techniques for printing directly in metal alloys, using powder-bed laser fusion. Additive manufacturing in metal alloys has become routinely available to researchers and manufacturers, with commercial systems widely available. These techniques have increased the potential for precision medicine, including patient-specific components and surgical guides. For biomedical applications, additive manufacturing is driven by advanced imaging (CT and MRI), as the required geometric data is typically derived from 3D volume images. There is an opportunity for significant improvement in the fundamental processes by which images are converted into source data for component design. Similarly, 3D imaging can serve an important role in verifying and validating the shape and mechanical performance of fabricated components for biomedical applications. Objectives: In this program, we will explore the implementation of novel algorithms (including machine learning and deep learning) for image reconstruction and segmentation, with the goal of improving the efficiency and accuracy of biomedical 3D printing. We will also develop techniques for process monitoring, as part of next-generation quality management. The techniques that we develop will be applied to a range of research applications, including veterinary components, porous scaffolds, and "smart" implants. We believe that the development of an integrated "pipeline" for design and testing will significantly expand the impact of image-based 3D printing. Approach: Our research approach for this program is focused on three themes: (1) optimization of image acquisition and segmentation, using dual-energy imaging, iterative reconstruction, and model-based deep-learning; (2) Image-based finite-element design and optimization, validated with dynamic 3D non-destructive testing and embedded sensors; (3) image-based validation and process improvement, using quantitative 3D computed tomography and in-process optical imaging during laser powder-bed fusion. Impact: This program integrates several aspects of recently developed technology and new computing algorithms (such as deep learning using neural networks), creating a new platform of software tools for image-based design and fabrication in biomedical engineering. Anticipated advances from this program include new tools to extract geometry data from 3D image volumes, and new hardware-software techniques to verify FE models (i.e. mechanical properties, drug elution, and thermal transport). This project takes advantage of expertise in several areas of the natural sciences and engineering, including: imaging science, biomechanical engineering, advanced manufacturing, and software engineering. The proposed program will lead to novel software utilities and techniques with broad applications in research and development.

动机：最近在增材制造（“3D打印”）领域有了相当大的发展，包括使用粉末床激光熔合直接在金属合金中打印的技术的发展。金属合金的增材制造已成为研究人员和制造商的常规技术，商业系统广泛使用。这些技术增加了精准医疗的潜力，包括患者特定组件和手术指南。对于生物医学应用，增材制造由先进的成像（CT和MRI）驱动，因为所需的几何数据通常来自3D体积图像。在将图像转换为用于组件设计的源数据的基本过程中，存在显著改进的机会。类似地，3D成像可以在验证和确认用于生物医学应用的制造部件的形状和机械性能方面发挥重要作用。目的：在这个项目中，我们将探索用于图像重建和分割的新算法（包括机器学习和深度学习）的实现，目标是提高生物医学3D打印的效率和准确性。我们还将开发过程监控技术，作为下一代质量管理的一部分。我们开发的技术将应用于一系列研究应用，包括兽医组件，多孔支架和“智能”植入物。我们相信，开发用于设计和测试的集成“管道”将显著扩大基于图像的3D打印的影响。方法：该项目的研究方法主要集中在三个主题上：（1）使用双能量成像、迭代重建和基于模型的深度学习优化图像采集和分割;（2）基于图像的有限元设计和优化，通过动态3D无损检测和嵌入式传感器进行验证;（3）基于图像的验证和工艺改进，在激光粉末床熔合期间使用定量3D计算机断层扫描和过程中光学成像。影响：该计划整合了最近开发的技术和新计算算法（如使用神经网络的深度学习）的几个方面，为生物医学工程中基于图像的设计和制造创建了一个新的软件工具平台。该计划的预期进展包括从3D图像体积中提取几何数据的新工具，以及验证FE模型（即机械性能，药物洗脱和热传输）的新硬件-软件技术。该项目利用了自然科学和工程领域的专业知识，包括：成像科学，生物机械工程，先进制造和软件工程。拟议的计划将导致新的软件实用程序和技术，在研究和开发中具有广泛的应用。