3D volume rendering for preoperative planning of neurosurgical interventions

Abstract

Introduction. In Neurosurgery, even with modern diagnostic imaging modalities like CT and MRI, structural information is still usually provided to the neurosurgeon by 2D image stacks, albeit in different planes. The surgeon relies on his spatial-visual imagination of patientspecific anatomy for surgical planning and the surgery itself, which can be challenging. To overcome these limitations, 3D technology has emerged as a technique with the potential to provide to the user detailed information on the three-dimensional orientation of objects within the surgical site before surgery. At present, no special equipment is required to create 3D models, and it is possible by using a personal computer. These models can be used for preoperative planning, such as finding the best cranial approach, avoiding eloquent areas of the brain, measure different structures, or even 3D print the models to simulate the surgery beforehand. By using all these data, the neurosurgeon can achieve the best results with the least complications by choosing the most optimal approach, achieve total removal of a brain lesion with minimal healthy brain involvement. Aim of the study. Our aim is to show the importance of 3d volume segmentation as a teaching and preoperative tool for neurosurgical interventions and to demonstrate our experience in clinical practice. Materials and methods.. There are several 3D segmentation software. Due to the availability of fast and affordable technical support, we chose the “Inobitec DICOM” software. The first stage was a semi-automatic voxel approximation of the object, and then, a polygonal grid was generated around the voxel. Multiple objects were fused to form a final 3D scene of the patientspecific anatomy. The models were exported for subsequent editing in external programs, such as “Meshmixer” and “Blender”. This option was needed to use certain features of these programs when viewing, such as variable transparency of objects, step-by-step navigation through the scene, different functions for vertex/object manipulation, and exporting the models to be displayed on mobile phones or other portable devices. Results. We report a detailed methodology for picture acquisition, 3D reconstruction, and visualization with some surgical examples. We also demonstrate how these navigable modelscan be used to build up composite images derived by the fusion of 3D intraoperative scenarios with neuroimaging-derived 3D models. Conclusions. Our experience, in the Neurosurgical Department, has shown that this is an affordable technology with great opportunities. The models can be used for a variety of purposes (teaching, planning, 3d printing). The creation of individual 3D models for preparation for surgery is already actively used in several areas of neurosurgery.