Department of Neurosurgery, 6572University of Pennsylvania Health System Penn Presbyterian Medical Center, Philadelphia, PA, USA.
Department of Radiology, 6572University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Surg Innov. 2021 Aug;28(4):427-437. doi: 10.1177/1553350620984339. Epub 2020 Dec 31.
. Holographic mixed reality (HMR) allows for the superimposition of computer-generated virtual objects onto the operator's view of the world. Innovative solutions can be developed to enable the use of this technology during surgery. The authors developed and iteratively optimized a pipeline to construct, visualize, and register intraoperative holographic models of patient landmarks during spinal fusion surgery. The study was carried out in two phases. In phase 1, the custom intraoperative pipeline to generate patient-specific holographic models was developed over 7 patients. In phase 2, registration accuracy was optimized iteratively for 6 patients in a real-time operative setting. In phase 1, an intraoperative pipeline was successfully employed to generate and deploy patient-specific holographic models. In phase 2, the registration error with the native hand-gesture registration was 20.2 ± 10.8 mm (n = 7 test points). Custom controller-based registration significantly reduced the mean registration error to 4.18 ± 2.83 mm (n = 24 test points, < .01). Accuracy improved over time (B = -.69, < .0001) with the final patient achieving a registration error of 2.30 ± .58 mm. Across both phases, the average model generation time was 18.0 ± 6.1 minutes (n = 6) for isolated spinal hardware and 33.8 ± 8.6 minutes (n = 6) for spinal anatomy. A custom pipeline is described for the generation of intraoperative 3D holographic models during spine surgery. Registration accuracy dramatically improved with iterative optimization of the pipeline and technique. While significant improvements and advancements need to be made to enable clinical utility, HMR demonstrates significant potential as the next frontier of intraoperative visualization.
. 全息混合现实 (HMR) 允许将计算机生成的虚拟物体叠加到操作员对世界的视图上。可以开发创新的解决方案,以在手术中使用这项技术。作者开发并迭代优化了一个管道,以构建、可视化和注册脊柱融合手术中患者标志点的术中全息模型。该研究分两个阶段进行。在第 1 阶段,针对 7 名患者开发了用于生成患者特定全息模型的定制术中管道。在第 2 阶段,在实时手术环境中针对 6 名患者迭代优化了注册准确性。在第 1 阶段,成功地采用了术中管道来生成和部署患者特定的全息模型。在第 2 阶段,与原生手势注册的配准误差为 20.2 ± 10.8mm(n = 7 个测试点)。基于定制控制器的注册显著将平均配准误差降低至 4.18 ± 2.83mm(n = 24 个测试点,<.01)。准确性随着时间的推移而提高(B = -.69,<.0001),最后一名患者的配准误差为 2.30 ±.58mm。在两个阶段中,对于孤立的脊柱硬件,平均模型生成时间为 18.0 ± 6.1 分钟(n = 6),对于脊柱解剖结构为 33.8 ± 8.6 分钟(n = 6)。描述了一种用于在脊柱手术中生成术中 3D 全息模型的定制管道。通过对管道和技术的迭代优化,配准准确性得到了显著提高。虽然需要进行重大改进和进步才能实现临床实用性,但 HMR 作为术中可视化的下一个前沿技术具有重要的潜力。
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