Patient-Specific 3D-Printed Models and Mixed-Reality Head-Mounted Display Visualization for Aneurysm Clip Ligation Planning: A Methodological Workflow Study

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Background: Mixed-reality head-mounted display (MR-HMD) visualization and 3D-printed models may improve three-dimensional visualization in neurosurgical planning. Detailed, reproducible descriptions of the workflows, technical requirements, and practical limitations of these techniques in aneurysm clip ligation planning remain limited. Methods: Between February 2020 and June 2021, patient-specific MR-HMD visualizations and 3D-printed models were generated for 29 patients undergoing elective surgical clip ligation of intracranial aneurysms. CT and 3D rotational angiography were fused to create patient-specific holograms for MR-HMD visualization and 3D-printed models. All cases underwent standardized modality-specific processing. Preparation and manufacturing times as well as recorded workflow-related usability observations, technical advantages, and limitations were assessed. Results: The mean total preparation time (mean ± standard deviation, SD) was 40.6 ± 10.3 min for MR-HMD visualization and 108.8 ± 17.8 min for 3D-printed models, excluding printing, washing, and curing time. The average printing time for 3D-printed models was 894.9 ± 41.6 min. MR-HMD setup in the operating room averaged 7.0 ± 2.8 min. Identified limitations included manual hologram registration and repositioning after table or patient position changes, as well as the inability to track instruments or interact with the hologram. Conclusions: Both modalities could be integrated into a standardized patient-specific workflow and serve complementary functions. MR-HMD visualization supported flexible short-term visualization, whereas 3D-printed models provided a physical model for clip planning and patient education at the cost of longer preparation and production. These findings support a combined workflow in which modality selection is guided by clinical tasks and time constraints.