Next-Generation Tools in Neurosurgery: Robotics, Imaging and Beyond

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurosurgery and Neuroanatomy".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 992

Special Issue Editors


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Guest Editor
1. Department of Neurosurgery, Azienda Ospedaliera Papardo, University of Messina, 98158 Messina, Italy
2. Division of Neurosurgery, BIOMORF Department, University of Messina, 98124 Messina, Italy
Interests: neurosurgery; radiosurgery; brain tumor
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Department of Neurosurgery, Azienda Ospedaliera Papardo, University of Messina, 98158 Messina, Italy
Interests: robotics; spine surgery; neuroncology; intraoperative imaging

Special Issue Information

Dear Colleagues,

In recent years, neurosurgery has embraced a technological revolution that is rapidly transforming the way in which brain and spine disorders are diagnosed and treated. With the advent of next-generation tools, including advanced robotic systems, intraoperative imaging, neuronavigation, augmented reality (AR), and artificial intelligence (AI), surgeons now have unprecedented support in achieving greater precision, safety, and efficiency in the operating room.

Robotic-assisted neurosurgery, once considered futuristic, is now an emerging reality in both cranial and spinal procedures. These platforms improve instrument stability, reduce surgeon fatigue, and enable minimally invasive approaches, resulting in reduced complication rates and faster patient recovery. Similarly, the evolution of intraoperative imaging—ranging from high-definition exoscopes to real-time intraoperative, CT, MRI and 3D ultrasound—enhances the visualization of complex anatomy, improving the extent of resection while preserving neurological function.

We look forward to receiving contributions that could expand our knowledge of modern neurosurgery from diagnosis to treatment.

Prof. Dr. Salvatore M. Cardali
Guest Editor

Dr. Giada Garufi
Guest Editor Assistant

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Keywords

  • neurosurgery
  • robotics
  • artificial intelligence
  • AI
  • neuronavigation
  • augmented reality

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Published Papers (1 paper)

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Research

21 pages, 7110 KB  
Article
An Augmented Reality-Based Navigation System for Stereotactic Brain Biopsy with Multi-Objective Path Planning and Hybrid Registration
by Tao Zhang, Shuyi Wang, Yueyang Zhong, Haoliang Li, Jingyi Hu and Haokun Wang
Brain Sci. 2026, 16(3), 296; https://doi.org/10.3390/brainsci16030296 - 6 Mar 2026
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Abstract
Background: Stereotactic brain biopsy is the gold standard for the pathological diagnosis of malignant brain tumors. However, conventional procedures rely heavily on manual path planning and unintuitive navigation, which significantly increase the risk of severe complications and impose an additional cognitive burden on [...] Read more.
Background: Stereotactic brain biopsy is the gold standard for the pathological diagnosis of malignant brain tumors. However, conventional procedures rely heavily on manual path planning and unintuitive navigation, which significantly increase the risk of severe complications and impose an additional cognitive burden on surgeons. Methods: We propose an augmented reality-based navigation system that synergizes multi-objective path planning with hybrid registration. Preoperatively, the system utilizes a constrained multi-objective optimization (MOO) model derived from clinical criteria to automatically calculate and visualize optimal biopsy paths within a three-dimensional anatomical environment. Intraoperatively, the system performs rapid initial alignment using quick response (QR) codes, followed by precise refinement through anatomical landmarks. This process ultimately enables the highly accurate, real-time overlay of the surgical path and anatomical models onto the patient’s operative field. Results: An expert study across four common brain tumor locations demonstrated that the MOO model significantly outperformed manual methods in satisfying safety criteria. The hybrid registration reduced the mean fiducial registration error (FRE) from 4.19 ± 1.11 mm to 2.37 ± 0.91 mm (p < 0.001), with a mean target registration error (TRE) of 2.34 ± 0.71 mm and a mean clinical setup time of 2.63 ± 0.36 min. Conclusions: This system assists stereotactic brain biopsy through automated path planning and immersive augmented reality-based guidance, highlighting its potential to support surgical workflow consistency and procedural safety. Full article
(This article belongs to the Special Issue Next-Generation Tools in Neurosurgery: Robotics, Imaging and Beyond)
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