Jacobson Nicholas, Carerra Erik, Smith Lawrence, Browne Lorna, Stence Nicholas, Sheridan Alison, MacCurdy Robert
School of Engineering, Design and Computation-Inworks Innovation Initiative, University of Colorado: Anschutz Medical Campus, Aurora, Colorado, USA.
School of Engineering, University of Colorado: Boulder, Boulder, Colorado, USA.
3D Print Addit Manuf. 2022 Dec 1;9(6):461-472. doi: 10.1089/3dp.2021.0141. Epub 2022 Dec 13.
Nearly all applications of 3D printing for surgical planning have been limited to bony structures and simple morphological descriptions of complex organs due to the fundamental limitations in accuracy, quality, and efficiency of the current modeling paradigms and technologies. Current approaches have largely ignored the constitution of soft tissue critical to most surgical specialties where multiple high-resolution variations transition gradually across the interior of the volume. Differences in the scales of organization related to unique organs require special attention to capture fine features critical to surgical procedures. We present a six-material bitmap printing technique for creating 3D models directly from medical images, which are superior in spatial and contrast resolution to current 3D modeling methods, and contain previously unachievable spatial fidelity for soft tissue differentiation. A retrospective exempt IRB was obtained for all data through the Colorado Multiple Institution Review Board #21-3128.
由于当前建模范式和技术在准确性、质量和效率方面存在根本限制，几乎所有用于手术规划的3D打印应用都仅限于骨骼结构以及对复杂器官的简单形态描述。目前的方法很大程度上忽略了软组织的构成，而软组织对大多数外科专业至关重要，在体积内部，多种高分辨率变化是逐渐过渡的。与独特器官相关的组织尺度差异需要特别关注，以捕捉对手术程序至关重要的精细特征。我们提出了一种六材料位图打印技术，可直接从医学图像创建3D模型，该模型在空间和对比度分辨率方面优于当前的3D建模方法，并且在软组织区分方面具有以前无法实现的空间保真度。通过科罗拉多多机构审查委员会#21-3128，对所有数据获得了回顾性豁免机构审查委员会批准。
