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Image-guided surgery has shown great utility in neurosurgery, especially in allowing for more accurate surgical planning and navigation. The current gold standard for image-guided neurosurgery is neuronavigation, which provides millimetric accuracy on such tasks. However, these approaches often require a complicated setup and have high cost, hindering their potential in low- and middle-income countries. The aim of this study was to develop and evaluate the performance of a mobile-based augmented reality neuronavigation solution under different conditions in a preclinical environment. The application was developed using the Swift programming language and was tested on a replica of a human scalp under variable lighting, with different numbers of registration points and target point position conditions. For each condition, reference points were input into the application, and the target points were computed for 10 iterations. The mean registration error and target error were used to assess the performance of the application. In the best-case scenario, the proposed solution had a mean target error of 2.6 ± 1.6 mm. Our approach provides a viable, low-cost, easy-to-use, portable method for locating points on the scalp surface with an accuracy of 2.6 ± 1.6 mm in the best-case scenario. [ABSTRACT FROM AUTHOR]

The precise location in the scalp of specifically planned points can help to achieve less invasive approaches. This study aims to develop a smartphone app, evaluate the precision and accuracy of the developed tool, and describe a series of cases using the referred technique. The application was developed with the React Native framework for Android and iOS. A phantom was printed based on the patient's CT scan, which was used for the calculation of accuracy and precision of the method. The points of interest were marked with an "x" on the patient's head, with the aid of the app and a compass attached to a skin marker pen. Then, two experienced neurosurgeons checked the plausibility of the demarcations based on the anatomical references. Both evaluators marked the frontal, temporal and parietal targets with a difference of less than 5 mm from the corresponding intended point, in all cases. The overall average accuracy observed was 1.6 ± 1.0 mm. The app was used in the surgical planning of trepanations for ventriculoperitoneal (VP) shunts and for drainage of abscesses, and in the definition of craniotomies for meningiomas, gliomas, brain metastases, intracranial hematomas, cavernomas, and arteriovenous malformation. The sample consisted of 88 volunteers who exhibited the following pathologies: 41 (46.6%) had brain tumors, 17 (19.3%) had traumatic brain injuries, 16 (18.2%) had spontaneous intracerebral hemorrhages, 2 (2.3%) had cavernomas, 1 (1.1%) had arteriovenous malformation (AVM), 4 (4.5%) had brain abscesses, and 7 (7.9%) had a VP shunt placement. In cases approached by craniotomy, with the exception of AVM, straight incisions and minicraniotomy were performed. Surgical planning with the aid of the NeuroKeypoint app is feasible and reliable. It has enabled neurological surgeries by craniotomy and trepanation in an accurate, precise, and less invasive manner. [ABSTRACT FROM AUTHOR]

Background Preoperative recognition of the anatomic individualities of each patient can help to achieve more precise and less invasive approaches. It also may help to anticipate potential complications and intraoperative difficulties. Here we describe the use, accuracy, and precision of a free tool for planning microsurgical approaches using 3-dimensional (3D) reconstructions from magnetic resonance imaging (MRI). Methods We used the 3D volume rendering tool of a free open-source software program for 3D reconstruction of images of surgical sites obtained by MRI volumetric acquisition. We recorded anatomic reference points, such as the sulcus and gyrus, and vascularization patterns for intraoperative localization of lesions. Lesion locations were confirmed during surgery by intraoperative ultrasound and/or electrocorticography and later by postoperative MRI. Results Between August 2015 and September 2016, a total of 23 surgeries were performed using this technique for 9 low-grade gliomas, 7 high-grade gliomas, 4 cortical dysplasias, and 3 arteriovenous malformations. The technique helped delineate lesions with an overall accuracy of 2.6 ± 1.0 mm. 3D reconstructions were successfully performed in all patients, and images showed sulcus, gyrus, and venous patterns corresponding to the intraoperative images. All lesion areas were confirmed both intraoperatively and at the postoperative evaluation. Conclusions With the technique described herein, it was possible to successfully perform 3D reconstruction of the cortical surface. This reconstruction tool may serve as an adjunct to neuronavigation systems or may be used alone when such a system is unavailable. [ABSTRACT FROM AUTHOR]