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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2010-01-01
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Series: | EURASIP Journal on Advances in Signal Processing |
Online Access: | http://asp.eurasipjournals.com/content/2010/942131 |
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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 CaitríonaFlamini 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–<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: 5–35).</p>http://asp.eurasipjournals.com/content/2010/942131 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kerskens ChristianM Lally Caitríona Flamini Vittoria Moerman KevinM Simms CiaranK |
spellingShingle |
Kerskens ChristianM Lally Caitríona 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 Caitríona 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–<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: 5–35).</p> |
url |
http://asp.eurasipjournals.com/content/2010/942131 |
work_keys_str_mv |
AT kerskenschristianm evaluationofavalidationmethodformrimagingbasedmotiontrackingusingimagesimulation AT lallycaitr237ona evaluationofavalidationmethodformrimagingbasedmotiontrackingusingimagesimulation AT flaminivittoria evaluationofavalidationmethodformrimagingbasedmotiontrackingusingimagesimulation AT moermankevinm evaluationofavalidationmethodformrimagingbasedmotiontrackingusingimagesimulation AT simmsciarank evaluationofavalidationmethodformrimagingbasedmotiontrackingusingimagesimulation |
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1725527874526511104 |