Evaluation of a Validation Method for MR Imaging-Based Motion Tracking Using Image Simulation

<p/> <p>Magnetic Resonance (MR) imaging-based motion and deformation tracking techniques combined with finite element (FE) analysis are a powerful method for soft tissue constitutive model parameter identification. However, deriving deformation data from MR images is complex and generall...

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Main Authors: Kerskens ChristianM, Lally Caitr&#237;ona, Flamini Vittoria, Moerman KevinM, Simms CiaranK
Format: Article
Language:English
Published: SpringerOpen 2010-01-01
Series:EURASIP Journal on Advances in Signal Processing
Online Access:http://asp.eurasipjournals.com/content/2010/942131
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spelling doaj-64c5c0dc6a674a76a7722133abd4336e2020-11-24T23:34:45ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-0120101942131Evaluation of a Validation Method for MR Imaging-Based Motion Tracking Using Image SimulationKerskens ChristianMLally Caitr&#237;onaFlamini VittoriaMoerman KevinMSimms CiaranK<p/> <p>Magnetic Resonance (MR) imaging-based motion and deformation tracking techniques combined with finite element (FE) analysis are a powerful method for soft tissue constitutive model parameter identification. However, deriving deformation data from MR images is complex and generally requires validation. In this paper a validation method is presented based on a silicone gel phantom containing contrasting spherical markers. Tracking of these markers provides a direct measure of deformation. Validation of in vivo medical imaging techniques is often challenging due to the lack of appropriate reference data and the validation method may lack an appropriate reference. This paper evaluates a validation method using simulated MR image data. This provided an appropriate reference and allowed different error sources to be studied independently and allowed evaluation of the method for various signal-to-noise ratios (SNRs). The geometric bias error was between 0&#8211;<inline-formula> <graphic file="1687-6180-2010-942131-i1.gif"/></inline-formula> voxels while the noisy magnitude MR image simulations demonstrated errors under 0.1161 voxels (SNR: 5&#8211;35).</p>http://asp.eurasipjournals.com/content/2010/942131
collection DOAJ
language English
format Article
sources DOAJ
author Kerskens ChristianM
Lally Caitr&#237;ona
Flamini Vittoria
Moerman KevinM
Simms CiaranK
spellingShingle Kerskens ChristianM
Lally Caitr&#237;ona
Flamini Vittoria
Moerman KevinM
Simms CiaranK
Evaluation of a Validation Method for MR Imaging-Based Motion Tracking Using Image Simulation
EURASIP Journal on Advances in Signal Processing
author_facet Kerskens ChristianM
Lally Caitr&#237;ona
Flamini Vittoria
Moerman KevinM
Simms CiaranK
author_sort Kerskens ChristianM
title Evaluation of a Validation Method for MR Imaging-Based Motion Tracking Using Image Simulation
title_short Evaluation of a Validation Method for MR Imaging-Based Motion Tracking Using Image Simulation
title_full Evaluation of a Validation Method for MR Imaging-Based Motion Tracking Using Image Simulation
title_fullStr Evaluation of a Validation Method for MR Imaging-Based Motion Tracking Using Image Simulation
title_full_unstemmed Evaluation of a Validation Method for MR Imaging-Based Motion Tracking Using Image Simulation
title_sort evaluation of a validation method for mr imaging-based motion tracking using image simulation
publisher SpringerOpen
series EURASIP Journal on Advances in Signal Processing
issn 1687-6172
1687-6180
publishDate 2010-01-01
description <p/> <p>Magnetic Resonance (MR) imaging-based motion and deformation tracking techniques combined with finite element (FE) analysis are a powerful method for soft tissue constitutive model parameter identification. However, deriving deformation data from MR images is complex and generally requires validation. In this paper a validation method is presented based on a silicone gel phantom containing contrasting spherical markers. Tracking of these markers provides a direct measure of deformation. Validation of in vivo medical imaging techniques is often challenging due to the lack of appropriate reference data and the validation method may lack an appropriate reference. This paper evaluates a validation method using simulated MR image data. This provided an appropriate reference and allowed different error sources to be studied independently and allowed evaluation of the method for various signal-to-noise ratios (SNRs). The geometric bias error was between 0&#8211;<inline-formula> <graphic file="1687-6180-2010-942131-i1.gif"/></inline-formula> voxels while the noisy magnitude MR image simulations demonstrated errors under 0.1161 voxels (SNR: 5&#8211;35).</p>
url http://asp.eurasipjournals.com/content/2010/942131
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