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Bolus characteristics based on Magnetic Resonance Angiography
BACKGROUND: A detailed contrast bolus propagation model is essential for optimizing bolus-chasing Computed Tomography Angiography (CTA). Bolus characteristics were studied using bolus-timing datasets from Magnetic Resonance Angiography (MRA) for adaptive controller design and validation. METHODS: MR...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2006
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1624834/ https://www.ncbi.nlm.nih.gov/pubmed/17044929 http://dx.doi.org/10.1186/1475-925X-5-53 |
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author | Cai, Zhijun Stolpen, Alan Sharafuddin, Melhem J McCabe, Robert Bai, Henri Potts, Tom Vannier, Michael Li, Debiao Bi, Xiaoming Bennett, James Golzarian, Jafar Sun, Shiliang Wang, Ge Bai, Er-Wei |
author_facet | Cai, Zhijun Stolpen, Alan Sharafuddin, Melhem J McCabe, Robert Bai, Henri Potts, Tom Vannier, Michael Li, Debiao Bi, Xiaoming Bennett, James Golzarian, Jafar Sun, Shiliang Wang, Ge Bai, Er-Wei |
author_sort | Cai, Zhijun |
collection | PubMed |
description | BACKGROUND: A detailed contrast bolus propagation model is essential for optimizing bolus-chasing Computed Tomography Angiography (CTA). Bolus characteristics were studied using bolus-timing datasets from Magnetic Resonance Angiography (MRA) for adaptive controller design and validation. METHODS: MRA bolus-timing datasets of the aorta in thirty patients were analyzed by a program developed with MATLAB. Bolus characteristics, such as peak position, dispersion and bolus velocity, were studied. The bolus profile was fit to a convolution function, which would serve as a mathematical model of bolus propagation in future controller design. RESULTS: The maximum speed of the bolus in the aorta ranged from 5–13 cm/s and the dwell time ranged from 7–13 seconds. Bolus characteristics were well described by the proposed propagation model, which included the exact functional relationships between the parameters and aortic location. CONCLUSION: The convolution function describes bolus dynamics reasonably well and could be used to implement the adaptive controller design. |
format | Text |
id | pubmed-1624834 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-16248342006-10-26 Bolus characteristics based on Magnetic Resonance Angiography Cai, Zhijun Stolpen, Alan Sharafuddin, Melhem J McCabe, Robert Bai, Henri Potts, Tom Vannier, Michael Li, Debiao Bi, Xiaoming Bennett, James Golzarian, Jafar Sun, Shiliang Wang, Ge Bai, Er-Wei Biomed Eng Online Research BACKGROUND: A detailed contrast bolus propagation model is essential for optimizing bolus-chasing Computed Tomography Angiography (CTA). Bolus characteristics were studied using bolus-timing datasets from Magnetic Resonance Angiography (MRA) for adaptive controller design and validation. METHODS: MRA bolus-timing datasets of the aorta in thirty patients were analyzed by a program developed with MATLAB. Bolus characteristics, such as peak position, dispersion and bolus velocity, were studied. The bolus profile was fit to a convolution function, which would serve as a mathematical model of bolus propagation in future controller design. RESULTS: The maximum speed of the bolus in the aorta ranged from 5–13 cm/s and the dwell time ranged from 7–13 seconds. Bolus characteristics were well described by the proposed propagation model, which included the exact functional relationships between the parameters and aortic location. CONCLUSION: The convolution function describes bolus dynamics reasonably well and could be used to implement the adaptive controller design. BioMed Central 2006-10-17 /pmc/articles/PMC1624834/ /pubmed/17044929 http://dx.doi.org/10.1186/1475-925X-5-53 Text en Copyright © 2006 Cai et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Cai, Zhijun Stolpen, Alan Sharafuddin, Melhem J McCabe, Robert Bai, Henri Potts, Tom Vannier, Michael Li, Debiao Bi, Xiaoming Bennett, James Golzarian, Jafar Sun, Shiliang Wang, Ge Bai, Er-Wei Bolus characteristics based on Magnetic Resonance Angiography |
title | Bolus characteristics based on Magnetic Resonance Angiography |
title_full | Bolus characteristics based on Magnetic Resonance Angiography |
title_fullStr | Bolus characteristics based on Magnetic Resonance Angiography |
title_full_unstemmed | Bolus characteristics based on Magnetic Resonance Angiography |
title_short | Bolus characteristics based on Magnetic Resonance Angiography |
title_sort | bolus characteristics based on magnetic resonance angiography |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1624834/ https://www.ncbi.nlm.nih.gov/pubmed/17044929 http://dx.doi.org/10.1186/1475-925X-5-53 |
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