A method for fully automated quantitative analysis of arterial flow using flow-sensitized MR images

Michael P Chwialkowski; Yaser M. Ibrahim; Hong F. Li; Ronald M Peshock

doi:10.1016/S0895-6111(96)00010-9

A method for fully automated quantitative analysis of arterial flow using flow-sensitized MR images

Michael P Chwialkowski, Yaser M. Ibrahim, Hong F. Li, Ronald M Peshock

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

Abstract

While the recent developments in the velocity-sensitive MR imaging offer great potential for non-invasive assessment of blood flow in major blood vessels, the clinical applications of this technique have been hampered by tedious, human-assisted data processing techniques. In this paper, we describe a robust system for automated extraction of quantitative as well as qualitative flow information from velocity-sensitive, phase contrast MR images. The algorithm accomplishes reliable segmentation of blood vessels using multiresolution analysis based on wavelet transform, and employs a multivariate scoring criterion to minimize the impact of imaging artifacts such as partial volume averaging and flow turbulence, which normally cause incomplete or inaccurate detection of vascular boundaries.

Original language	English (US)
Pages (from-to)	365-378
Number of pages	14
Journal	Computerized Medical Imaging and Graphics
Volume	20
Issue number	5
DOIs	https://doi.org/10.1016/S0895-6111(96)00010-9
State	Published - Sep 1996

Keywords

Blood flow
Image processing
Image segmentation
MRI
Wavelets

ASJC Scopus subject areas

Radiological and Ultrasound Technology
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Health Informatics
Computer Graphics and Computer-Aided Design

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10.1016/S0895-6111(96)00010-9

Cite this

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title = "A method for fully automated quantitative analysis of arterial flow using flow-sensitized MR images",

abstract = "While the recent developments in the velocity-sensitive MR imaging offer great potential for non-invasive assessment of blood flow in major blood vessels, the clinical applications of this technique have been hampered by tedious, human-assisted data processing techniques. In this paper, we describe a robust system for automated extraction of quantitative as well as qualitative flow information from velocity-sensitive, phase contrast MR images. The algorithm accomplishes reliable segmentation of blood vessels using multiresolution analysis based on wavelet transform, and employs a multivariate scoring criterion to minimize the impact of imaging artifacts such as partial volume averaging and flow turbulence, which normally cause incomplete or inaccurate detection of vascular boundaries.",

keywords = "Blood flow, Image processing, Image segmentation, MRI, Wavelets",

author = "Chwialkowski, {Michael P} and Ibrahim, {Yaser M.} and Li, {Hong F.} and Peshock, {Ronald M}",

note = "Funding Information: Acknowledgement-This work was supported in part by the AdvancedT echnologyP rogram, Texas Higher Education Coordinating Board (grants 003656118 and 003656116), and by Picker International, Inc.",

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AU - Ibrahim, Yaser M.

AU - Li, Hong F.

AU - Peshock, Ronald M

N1 - Funding Information: Acknowledgement-This work was supported in part by the AdvancedT echnologyP rogram, Texas Higher Education Coordinating Board (grants 003656118 and 003656116), and by Picker International, Inc.

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N2 - While the recent developments in the velocity-sensitive MR imaging offer great potential for non-invasive assessment of blood flow in major blood vessels, the clinical applications of this technique have been hampered by tedious, human-assisted data processing techniques. In this paper, we describe a robust system for automated extraction of quantitative as well as qualitative flow information from velocity-sensitive, phase contrast MR images. The algorithm accomplishes reliable segmentation of blood vessels using multiresolution analysis based on wavelet transform, and employs a multivariate scoring criterion to minimize the impact of imaging artifacts such as partial volume averaging and flow turbulence, which normally cause incomplete or inaccurate detection of vascular boundaries.

AB - While the recent developments in the velocity-sensitive MR imaging offer great potential for non-invasive assessment of blood flow in major blood vessels, the clinical applications of this technique have been hampered by tedious, human-assisted data processing techniques. In this paper, we describe a robust system for automated extraction of quantitative as well as qualitative flow information from velocity-sensitive, phase contrast MR images. The algorithm accomplishes reliable segmentation of blood vessels using multiresolution analysis based on wavelet transform, and employs a multivariate scoring criterion to minimize the impact of imaging artifacts such as partial volume averaging and flow turbulence, which normally cause incomplete or inaccurate detection of vascular boundaries.

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KW - Image processing

KW - Image segmentation

KW - MRI

KW - Wavelets

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A method for fully automated quantitative analysis of arterial flow using flow-sensitized MR images

Abstract

Keywords

ASJC Scopus subject areas

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