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Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using statistical modeling
Umeå University, Faculty of Medicine, Department of Radiation Sciences.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. (Mathematical Statistics)
Lund University. (Department of Medical Radiation Physics)
Umeå University, Faculty of Medicine, Department of Radiation Sciences.
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2015 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 74, no 4, 1156-1164 p.Article in journal (Refereed) Published
Abstract [en]

Purpose: The purpose of this study was to investigate, using simulations, a method for improved contrast agent (CA) quantification in DCE-MRI.

Methods: We developed a maximum likelihood estimator that combines the phase signal in the DCE-MRI image series with an additional CA estimate, e.g. the estimate obtained from magnitude data. A number of simulations were performed to investigate the ability of the estimator to reduce bias and noise in CA estimates. Noise levels ranging from that of a body coil to that of a dedicated head coil were investigated at both 1.5T and 3T.

Results: Using the proposed method, the root mean squared error in the bolus peak was reduced from 2.24 to 0.11 mM in the vessels and 0.16 to 0.08 mM in the tumor rim for a noise level equivalent of a 12-channel head coil at 3T. No improvements were seen for tissues with small CA uptake, such as white matter.

Conclusion: Phase information reduces errors in the estimated CA concentrations. A larger phase response from higher field strengths or higher CA concentrations yielded better results. Issues such as background phase drift need to be addressed before this method can be applied in vivo.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2015. Vol. 74, no 4, 1156-1164 p.
Keyword [en]
dynamic contrast-enhanced MRI, contrast agent quantification, phase, inverse problem
National Category
Medical Image Processing Probability Theory and Statistics Radiology, Nuclear Medicine and Medical Imaging
URN: urn:nbn:se:umu:diva-95051DOI: 10.1002/mrm.25490ISI: 000364215200028PubMedID: 25324043OAI: diva2:757085
Swedish Research Council, 340-2013- 5342; 13514Swedish Cancer Society, CAN 2010/381
Available from: 2014-10-21 Created: 2014-10-21 Last updated: 2015-11-30Bibliographically approved

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