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Renal artery stenosis: extracting quantitative parameters with a mathematical model fitted to magnetic resonance blood flow data
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.ORCID iD: 0000-0002-9446-6981
Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medicine, Department of Nephrology.ORCID iD: 0000-0002.3555-7162
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.ORCID iD: 0000-0003-3124-8044
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2008 (English)In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 27, no 1, 140-147 p.Article in journal (Refereed) Published
Abstract [en]

Purpose: To investigate the feasibility of quantitative parameter extraction from a mathematical model fitted to renal artery magnetic resonance flow data.

Material and methods: Sixteen subjects, 8 patients and 8 normal controls, were examined with cine phase-contrast velocity measurements, and blood flow data from the aorta and both renal arteries were extracted by means of contour detection. A mathematical model with eight parameters describing the time, duration and amplitude of the systolic acceleration and the diastolic deceleration was fitted to the aorta and renal artery blood flow data from each subject. The curve fitting was evaluated with R2 values. Statistical analysis was performed with unpaired Wilcoxon tests and stepwise logistic regression.

Results: Three data sets out of 48 yielded R2 values below 0.80 and were considered unreliable for parameter estimation. Basal flow was significantly, and systolic peak amplitude almost significantly, lower in stenotic arteries. Logistic regression indicated that two parameters describing basal flow and the duration of acceleration can accurately predict stenosis.

Conclusion: The results suggest that it is technically feasible to fit a mathematical model to renal blood flow data, extracting quantitative parameters that may prove useful for quantification and diagnosis of renal artery stenosis.

Place, publisher, year, edition, pages
John Wiley & Sons, 2008. Vol. 27, no 1, 140-147 p.
Keyword [en]
Flow measurement, Magnetic Resonance Angiography, Blood flow, Renal artery stenosis
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-10712DOI: 10.1002/jmri.21232ISI: 000252104100020PubMedID: 18050354OAI: oai:DiVA.org:liu-10712DiVA: diva2:17421
Note

Original publication: Martin Larsson, Anders Persson, Per Eriksson, Johan Kihlberg, Örjan Smedby, Renal Artery Stenosis: Extracting Quantitative Parameters With a Mathematical Model Fitted to Magnetic Resonance Blood Flow Data, 2008, Journal of Magnetic Resonance Imaging, (27), 1, 140-147. http://dx.doi.org/10.1002/jmri.21232. Copyright: John Wiley and Sons, Inc, http://www3.interscience.wiley.com/journal/10005199/home

Available from: 2008-02-27 Created: 2008-02-27 Last updated: 2017-10-25Bibliographically approved

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Persson, AndersEriksson, PerKihlberg, JohanSmedby, Örjan

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Larsson, MartinPersson, AndersEriksson, PerKihlberg, JohanSmedby, Örjan
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Center for Medical Image Science and Visualization (CMIV)Faculty of Health SciencesRadiologyDepartment of Radiology in LinköpingDepartment of Medical and Health SciencesDepartment of Nephrology
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