VR display of human brain vessel network extracted from time-of-flight MRI

Hao Chen, Xinpei Wang, Jichang Zhang, Pengfei Xu, Zhen Nan, Chengbo Wang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

According to the report from the World Health Organization (WHO), vascular diseases became the most life-threatening diseases by 2015. Time of flight angiography (TOF) is a noncontrast MRI technique to visualize blood vessel in the human vascular system. However, diagnosis of the complex vascular network structures was difficult based on traditional (2D) display method owing to the complexity and variability of vascular network structures. In this study, we investigated the feasibility of displaying three-dimensional (3D) vessel network in virtual reality (VR) environment, using 3D TOF data. Also, the potential value of its clinical application was evaluated. The experiment results, 3D VR videos, showed that the intervention of VR technology is feasible for the 3D image display of cerebrovascular network.

Original languageEnglish
Title of host publicationIET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence 2018, BRAIN 2018
PublisherInstitution of Engineering and Technology
EditionCP754
ISBN (Print)9781785617911, 9781839530838
DOIs
Publication statusPublished - 2018
EventIET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence 2018, BRAIN 2018 - Ningbo, China
Duration: 4 Nov 2018 → …

Publication series

NameIET Conference Publications
NumberCP754
Volume2018

Conference

ConferenceIET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence 2018, BRAIN 2018
Country/TerritoryChina
CityNingbo
Period4/11/18 → …

Keywords

  • 3D RECONSTRUCTION
  • BRAIN
  • TOF MRI
  • VESSEL NETWORK
  • VIRTUAL REALITY DISPLAY

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'VR display of human brain vessel network extracted from time-of-flight MRI'. Together they form a unique fingerprint.

Cite this