Back to Search Start Over

Enabling Real-Time Volume Rendering of Functional Magnetic Resonance Imaging on an iOS Device.

Authors :
Holub, Joseph
Winer, Eliot
Source :
Journal of Digital Imaging; Dec2017, Vol. 30 Issue 6, p738-750, 13p, 1 Color Photograph, 1 Black and White Photograph, 4 Diagrams, 4 Charts
Publication Year :
2017

Abstract

Powerful non-invasive imaging technologies like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI) are used daily by medical professionals to diagnose and treat patients. While 2D slice viewers have long been the standard, many tools allowing 3D representations of digital medical data are now available. The newest imaging advancement, functional MRI (fMRI) technology, has changed medical imaging from viewing static to dynamic physiology (4D) over time, particularly to study brain activity. Add this to the rapid adoption of mobile devices for everyday work and the need to visualize fMRI data on tablets or smartphones arises. However, there are few mobile tools available to visualize 3D MRI data, let alone 4D fMRI data. Building volume rendering tools on mobile devices to visualize 3D and 4D medical data is challenging given the limited computational power of the devices. This paper describes research that explored the feasibility of performing real-time 3D and 4D volume raycasting on a tablet device. The prototype application was tested on a 9.7' iPad Pro using two different fMRI datasets of brain activity. The results show that mobile raycasting is able to achieve between 20 and 40 frames per second for traditional 3D datasets, depending on the sampling interval, and up to 9 frames per second for 4D data. While the prototype application did not always achieve true real-time interaction, these results clearly demonstrated that visualizing 3D and 4D digital medical data is feasible with a properly constructed software framework. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08971889
Volume :
30
Issue :
6
Database :
Complementary Index
Journal :
Journal of Digital Imaging
Publication Type :
Academic Journal
Accession number :
126169905
Full Text :
https://doi.org/10.1007/s10278-017-9986-1