Implementation of a phase detection algorithm for dynamic cardiac computed tomography analysis based on time dependent contrast agent distribution.
This paper presents a phase detection algorithm for four-dimensional (4D) cardiac computed tomography (CT) analysis. The algorithm detects a phase, i.e. a specific three-dimensional (3D) image out of several time-distributed 3D images, with high contrast in the left ventricle and low contrast in the...
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2014-01-01
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doaj-7b9b65b71ab045e9a67db3eadbe69df02020-11-25T02:33:56ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01912e11610310.1371/journal.pone.0116103Implementation of a phase detection algorithm for dynamic cardiac computed tomography analysis based on time dependent contrast agent distribution.Carsten KendziorraHenning MeyerMarc DeweyThis paper presents a phase detection algorithm for four-dimensional (4D) cardiac computed tomography (CT) analysis. The algorithm detects a phase, i.e. a specific three-dimensional (3D) image out of several time-distributed 3D images, with high contrast in the left ventricle and low contrast in the right ventricle. The purpose is to use the automatically detected phase in an existing algorithm that automatically aligns the images along the heart axis. Decision making is based on the contrast agent distribution over time. It was implemented in KardioPerfusion--a software framework currently being developed for 4D CT myocardial perfusion analysis. Agreement of the phase detection algorithm with two reference readers was 97% (95% CI: 82-100%). Mean duration for detection was 0.020 s (95% CI: 0.018-0.022 s), which was 800 times less than the readers needed (16±7 s, p<03001). Thus, this algorithm is an accurate and fast tool that can improve work flow of clinical examinations.http://europepmc.org/articles/PMC4278835?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Carsten Kendziorra Henning Meyer Marc Dewey |
spellingShingle |
Carsten Kendziorra Henning Meyer Marc Dewey Implementation of a phase detection algorithm for dynamic cardiac computed tomography analysis based on time dependent contrast agent distribution. PLoS ONE |
author_facet |
Carsten Kendziorra Henning Meyer Marc Dewey |
author_sort |
Carsten Kendziorra |
title |
Implementation of a phase detection algorithm for dynamic cardiac computed tomography analysis based on time dependent contrast agent distribution. |
title_short |
Implementation of a phase detection algorithm for dynamic cardiac computed tomography analysis based on time dependent contrast agent distribution. |
title_full |
Implementation of a phase detection algorithm for dynamic cardiac computed tomography analysis based on time dependent contrast agent distribution. |
title_fullStr |
Implementation of a phase detection algorithm for dynamic cardiac computed tomography analysis based on time dependent contrast agent distribution. |
title_full_unstemmed |
Implementation of a phase detection algorithm for dynamic cardiac computed tomography analysis based on time dependent contrast agent distribution. |
title_sort |
implementation of a phase detection algorithm for dynamic cardiac computed tomography analysis based on time dependent contrast agent distribution. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2014-01-01 |
description |
This paper presents a phase detection algorithm for four-dimensional (4D) cardiac computed tomography (CT) analysis. The algorithm detects a phase, i.e. a specific three-dimensional (3D) image out of several time-distributed 3D images, with high contrast in the left ventricle and low contrast in the right ventricle. The purpose is to use the automatically detected phase in an existing algorithm that automatically aligns the images along the heart axis. Decision making is based on the contrast agent distribution over time. It was implemented in KardioPerfusion--a software framework currently being developed for 4D CT myocardial perfusion analysis. Agreement of the phase detection algorithm with two reference readers was 97% (95% CI: 82-100%). Mean duration for detection was 0.020 s (95% CI: 0.018-0.022 s), which was 800 times less than the readers needed (16±7 s, p<03001). Thus, this algorithm is an accurate and fast tool that can improve work flow of clinical examinations. |
url |
http://europepmc.org/articles/PMC4278835?pdf=render |
work_keys_str_mv |
AT carstenkendziorra implementationofaphasedetectionalgorithmfordynamiccardiaccomputedtomographyanalysisbasedontimedependentcontrastagentdistribution AT henningmeyer implementationofaphasedetectionalgorithmfordynamiccardiaccomputedtomographyanalysisbasedontimedependentcontrastagentdistribution AT marcdewey implementationofaphasedetectionalgorithmfordynamiccardiaccomputedtomographyanalysisbasedontimedependentcontrastagentdistribution |
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