2015IEEE Transactions on Ultrasonics Ferroelectrics and Frequency ControlRequires access

Absolute ultrasound perfusion parameter quantification of a tissue-mimicking phantom using bolus tracking [Correspondence]

Martin Mézl, Radovan Jiřík, Vratislav Harabiš, Radim Kolář, Michal Standara, Kim Nylund, Odd Helge Gilja, Torfinn Taxt

Open publisher page 6 citations

Abstract

This study presents three methods for absolute quantification in ultrasound perfusion analysis based on bolus tracking. The first two methods deconvolve the perfusion time sequence with a measured AIF, using a nonparametric or a parametric model of the tissue residue function, respectively. The third method is a simplified approach avoiding deconvolution by assuming a narrow AIF. A phantom with a dialyzer filter as a tissue-mimicking model was used for evaluation. Estimated mean transit times and blood volumes were compared with the theoretical values. A match with a maximum error of 12% was achieved.

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What this paper is about

This study presents three methods for absolute quantification in ultrasound perfusion analysis based on bolus tracking. The first two methods deconvolve the perfusion time sequence with a measured AIF, using a nonparametric or a parametric model of the tissue residue function, respectively. The third method is a simplified approach avoiding deconvolution by assuming a narrow AIF. A phantom with a dialyzer filter as a tissue-mimicking model was used for evaluation. Estimated mean transit times and blood volumes were compared with the theoretical values. A match with a maximum error of 12% was achieved.

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Available abstract

This study presents three methods for absolute quantification in ultrasound perfusion analysis based on bolus tracking. The first two methods deconvolve the perfusion time sequence with a measured AIF, using a nonparametric or a parametric model of the tissue residue function, respectively. The third method is a simplified approach avoiding deconvolution by assuming a narrow AIF. A phantom with a dialyzer filter as a tissue-mimicking model was used for evaluation. Estimated mean transit times and blood volumes were compared with the theoretical values. A match with a maximum error of 12% was achieved.

Key concepts: Imaging phantom, Deconvolution, Biomedical engineering, Perfusion, Bolus (digestion), Ultrasound, Nuclear medicine, Materials science

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