Estimation of tissue perfusion by dynamic contrast-enhanced imaging: simulation-based evaluation of the steepest slope method

Objective Tissue perfusion is frequently determined from dynamic contrast-enhanced CT or MRI image series by means of the steepest slope method. It was thus the aim of this study to systematically evaluate the reliability of this analysis method on the basis of simulated tissue curves. Methods 9600...

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Veröffentlicht in:European radiology 2010-09, Vol.20 (9), p.2166-2175
Hauptverfasser: Brix, Gunnar, Zwick, Stefan, Griebel, Jürgen, Fink, Christian, Kiessling, Fabian
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container_issue 9
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container_title European radiology
container_volume 20
creator Brix, Gunnar
Zwick, Stefan
Griebel, Jürgen
Fink, Christian
Kiessling, Fabian
description Objective Tissue perfusion is frequently determined from dynamic contrast-enhanced CT or MRI image series by means of the steepest slope method. It was thus the aim of this study to systematically evaluate the reliability of this analysis method on the basis of simulated tissue curves. Methods 9600 tissue curves were simulated for four noise levels, three sampling intervals and a wide range of physiological parameters using an axially distributed reference model and subsequently analysed by the steepest slope method. Results Perfusion is systematically underestimated with errors becoming larger with increasing perfusion and decreasing intravascular volume. For curves sampled after rapid contrast injection with a temporal resolution of 0.72 s, the bias was less than 23% when the mean residence time of tracer molecules in the intravascular distribution space was greater than 6 s. Increasing the sampling interval and the noise level substantially reduces the accuracy and precision of estimates, respectively. Conclusions The steepest slope method allows absolute quantification of tissue perfusion in a computationally simple and numerically robust manner. The achievable degree of accuracy and precision is considered to be adequate for most clinical applications.
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It was thus the aim of this study to systematically evaluate the reliability of this analysis method on the basis of simulated tissue curves. Methods 9600 tissue curves were simulated for four noise levels, three sampling intervals and a wide range of physiological parameters using an axially distributed reference model and subsequently analysed by the steepest slope method. Results Perfusion is systematically underestimated with errors becoming larger with increasing perfusion and decreasing intravascular volume. For curves sampled after rapid contrast injection with a temporal resolution of 0.72 s, the bias was less than 23% when the mean residence time of tracer molecules in the intravascular distribution space was greater than 6 s. Increasing the sampling interval and the noise level substantially reduces the accuracy and precision of estimates, respectively. Conclusions The steepest slope method allows absolute quantification of tissue perfusion in a computationally simple and numerically robust manner. 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It was thus the aim of this study to systematically evaluate the reliability of this analysis method on the basis of simulated tissue curves. Methods 9600 tissue curves were simulated for four noise levels, three sampling intervals and a wide range of physiological parameters using an axially distributed reference model and subsequently analysed by the steepest slope method. Results Perfusion is systematically underestimated with errors becoming larger with increasing perfusion and decreasing intravascular volume. For curves sampled after rapid contrast injection with a temporal resolution of 0.72 s, the bias was less than 23% when the mean residence time of tracer molecules in the intravascular distribution space was greater than 6 s. Increasing the sampling interval and the noise level substantially reduces the accuracy and precision of estimates, respectively. 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It was thus the aim of this study to systematically evaluate the reliability of this analysis method on the basis of simulated tissue curves. Methods 9600 tissue curves were simulated for four noise levels, three sampling intervals and a wide range of physiological parameters using an axially distributed reference model and subsequently analysed by the steepest slope method. Results Perfusion is systematically underestimated with errors becoming larger with increasing perfusion and decreasing intravascular volume. For curves sampled after rapid contrast injection with a temporal resolution of 0.72 s, the bias was less than 23% when the mean residence time of tracer molecules in the intravascular distribution space was greater than 6 s. Increasing the sampling interval and the noise level substantially reduces the accuracy and precision of estimates, respectively. Conclusions The steepest slope method allows absolute quantification of tissue perfusion in a computationally simple and numerically robust manner. The achievable degree of accuracy and precision is considered to be adequate for most clinical applications.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><pmid>20407900</pmid><doi>10.1007/s00330-010-1787-6</doi><tpages>10</tpages></addata></record>
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subjects Algorithms
Computer Simulation
Contrast Media - pharmacokinetics
Diagnostic Radiology
Diffusion Magnetic Resonance Imaging - methods
Experimental
Humans
Image Interpretation, Computer-Assisted - methods
Imaging
Internal Medicine
Interventional Radiology
Inverse problems
Magnetic Resonance Angiography - methods
Magnetic resonance imaging
Medicine
Medicine & Public Health
Models, Biological
Neuroradiology
Nuclear medicine
Physiology
Plasma
Radiation
Radiology
Reproducibility of Results
Sensitivity and Specificity
Ultrasound
title Estimation of tissue perfusion by dynamic contrast-enhanced imaging: simulation-based evaluation of the steepest slope method
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