Abstract
OBJECTIVES: This study aims to use the three-dimensional (3D) mixed-reality model of liver, entailing complex intrahepatic systems and to deeply study the anatomical structures and to promote the training, diagnosis and treatment of liver diseases. METHODS: Vascular perfusion human specimens were used for thin-layer frozen milling to obtain liver cross-sections. The 104-megapixel-high-definition cross sectional data set was established and registered to achieve structure identification and manual segmentation. The digital model was reconstructed and data was used to print a 3D hepatic model. The model was combined with HoloLens mixed reality technology to reflect the complex relationships of intrahepatic systems. We simulated 3D patient specific anatomy for identification and preoperative planning, conducted a questionnaire survey, and evaluated the results. RESULTS: The 3D digital model and 1:1 transparent and colored model of liver established truly reflected intrahepatic vessels and their complex relationships. The reconstructed model imported into HoloLens could be accurately matched with the 3D model. Only 7.7% participants could identify accessory hepatic veins. The depth and spatial-relationship of intrahepatic structures were better understandable for 92%. The 100%, 84.6%, 69% and 84% believed the 3D models were useful in planning, safer surgical paths, reducing intraoperative complications and training of young surgeons respectively. CONCLUSIONS: A detailed 3D model can be reconstructed using the higher quality cross-sectional anatomical data set. When combined with 3D printing and HoloLens technology, a novel hybrid-reality navigation-training system for liver surgery is created. Mixed Reality training is a worthy alternative to provide 3D information to clinicians and its possible application in surgery. This conclusion was obtained based on a questionnaire and evaluation. Surgeons with extensive experience in surgical operations perceived in the questionnaire that this technology might be useful in liver surgery, would help in precise preoperative planning, accurate intraoperative identification, and reduction of hepatic injury.