Luca Zanuttini | Searcharxiv

arXiv2025

Towards the target and not beyond: 2D vs 3D visual aids in MR-based neurosurgical simulation

Neurosurgery increasingly uses Mixed Reality (MR) technologies for intraoperative assistance. The greatest challenge in this area is mentally reconstructing complex 3D anatomical structures from 2D slices with millimetric precision, which is required in procedures like External Ventricular Drain (EVD) placement. MR technologies have shown great potential in improving surgical performance, however, their limited availability in clinical settings underscores the need for training systems that foster skill retention in unaided conditions. In this paper, we introduce NeuroMix, an MR-based simulator for EVD placement. We conduct a study with 48 participants to assess the impact of 2D and 3D visual aids on usability, cognitive load, technology acceptance, and procedure precision and execution time. Three training modalities are compared: one without visual aids, one with 2D aids only, and one combining both 2D and 3D aids. The training phase takes place entirely on digital objects, followed by a freehand EVD placement testing phase performed with a physical catherer and a physical phantom without MR aids. We then compare the participants performance with that of a control group that does not undergo training. Our findings show that participants trained with both 2D and 3D aids achieve a 44\% improvement in precision during unaided testing compared to the control group, substantially higher than the improvement observed in the other groups. All three training modalities receive high usability and technology acceptance ratings, with significant equivalence across groups. The combination of 2D and 3D visual aids does not significantly increase cognitive workload, though it leads to longer operation times during freehand testing compared to the control group.

cs.GR↗

arXiv2024

Desynchronization Index: a New Connectivity Approach for Exploring Epileptogenic Networks

In drug-resistant epilepsy, Stereo-Electroencephalography (SEEG) monitoring is one of the most effective techniques to identify the Epileptogenic Zone (EZ), the fundamental prerequisite for epilepsy surgery. Despite recent technological advances, SEEG recordings remain difficult to interpret, and SEEG-guided surgery still achieves success rates below 70%. In this work, we develop a novel computational framework for SEEG analysis, with the ultimate aim of improving the accuracy in EZ definition. Specifically, we investigate the hypothesis that epileptogenic regions exhibit a tendency to behave independently and thus desynchronize from neighboring brain structures before seizure onset. To this end, we design the Desynchronization Index (DI), an algorithm that identifies the Epileptogenic Zone (EZ) as the subset of channels that disconnect from the SEEG network during the ictal transition. We evaluate the DI algorithm against Epileptogenicity Index (EI), one of the most common tools for EZ definition, on a clinical dataset of 20 patients, considering the channels that were thermocoagulated at the end of SEEG monitoring as the detection target. Our results show that DI overcomes EI in terms of area under the ROC curve (AUC=0.86 vs. AUC=0.83), while combining the two algorithms into a single framework leads to the best performance (AUC=0.88). Overall, the DI algorithm underscores anomalous connectivity patterns that are difficult to detect through visual inspection, improving the accuracy in the EZ definition and providing new insights into the dynamics of seizure generation.

eess.SP↗