Bolus characteristics based on Magnetic Resonance Angiography

Zhijun Cai; Alan Stolpen; Melhem J Sharafuddin; Robert McCabe; Henri Bai; Tom Potts; Michael Vannier; Debiao Li; Xiaoming Bi; James Bennett; Jafar Golzarian; Shiliang Sun; Ge Wang; Er-Wei Bai

doi:10.1186/1475-925X-5-53

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Bolus characteristics based on Magnetic Resonance Angiography

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Bolus characteristics based on Magnetic Resonance Angiography

Zhijun Cai, Alan Stolpen, Melhem J Sharafuddin, Robert McCabe, Henri Bai, Tom Potts, Michael Vannier, Debiao Li, Xiaoming Bi, James Bennett, …

Biomedical engineering online, Vol.5(1), pp.53-53

10/17/2006

DOI: 10.1186/1475-925X-5-53

PMCID: PMC1624834

PMID: 17044929

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Abstract

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.

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Title: Subtitle: Bolus characteristics based on Magnetic Resonance Angiography
Creators: Zhijun Cai - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
Alan Stolpen - Department of Radiology, University of Iowa, Iowa City, IA, USA
Melhem J Sharafuddin - University of Iowa Hospital and Clinic, Iowa City, IA, USA
Robert McCabe - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
Henri Bai - Department of Computer Engineering, Iowa State University, Ames, IA, USA
Tom Potts - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
Michael Vannier - Department of Radiology, University of Chicago, Chicago, IL, USA
Debiao Li - Department of Radiology and Biomedical Engineering Northwestern University, Chicago, IL, USA
Xiaoming Bi - Department of Radiology and Biomedical Engineering Northwestern University, Chicago, IL, USA
James Bennett - Department of Radiology, University of Iowa, Iowa City, IA, USA
Jafar Golzarian - Department of Radiology, University of Iowa, Iowa City, IA, USA
Shiliang Sun - Department of Radiology, University of Iowa, Iowa City, IA, USA
Ge Wang - Department of Radiology, University of Iowa, Iowa City, IA, USA
Er-Wei Bai - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
Resource Type: Journal article
Publication Details: Biomedical engineering online, Vol.5(1), pp.53-53
DOI: 10.1186/1475-925X-5-53
PMID: 17044929
PMCID: PMC1624834
NLM abbreviation: Biomed Eng Online
ISSN: 1475-925X
eISSN: 1475-925X
Publisher: BioMed Central; London
Language: English
Date published: 10/17/2006
Academic Unit: Radiology; Electrical and Computer Engineering; Surgery; Internal Medicine
Record Identifier: 9984051765302771

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