Feasibility and workflow analysis of IV-DSA-based augmented reality-guided brain arteriovenous malformation resection in a hybrid operating room: i-Flow tailored method

Augmented reality (AR) has emerged as a promising technology in various medical fields.1 2 In the context of brain arteriovenous malformation (bAVM) surgery, AR offers the potential to enhance surgical visualization and improve procedural accuracy.3 4 5 6 This report aims to explore the application...

Full description

Saved in:
Bibliographic Details
Published inJournal of neurointerventional surgery p. jnis-2023-020797
Main Authors Huang, Chih-Wei, Lee, Chung-Hsin, Chung, Kai-Chen, Tsuei, Yuang-Seng
Format Journal Article
LanguageEnglish
Published London BMJ Publishing Group LTD 28.09.2023
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:Augmented reality (AR) has emerged as a promising technology in various medical fields.1 2 In the context of brain arteriovenous malformation (bAVM) surgery, AR offers the potential to enhance surgical visualization and improve procedural accuracy.3 4 5 6 This report aims to explore the application of digital subtraction angiography (DSA) from an IV contrast injection (IV-DSA) in AR-guided resection of bAVMs in a neurosurgical hybrid operating room. The workflow of IV-DSA-based AR-guided surgery for the resection of bAVMs consists of four main components: (1) acquiring source images through i-Flow tailored or multiphase scans (Siemens, Germany); (2) labelling targets in the workstation using Smartbrush software (Brainlab, Westchester, Illinois, USA); (3) using the Brainlab Curve navigation system; and (4) merging microscopic AR fusion using Zeiss Kinevo (AG, Germany). In video 1 we show the entire workflow and introduce i-Flow tailored IV-DSA data acquisition in the hybrid operating room. In summary, IV-DSA-based augmented reality is an innovative technique for bAVM surgery. Video 1 -i-flow tailored iv-DSA
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1759-8478
1759-8486
DOI:10.1136/jnis-2023-020797