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Updated: Dec 27, 2025

Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling
Published on: May 30, 2011
Quantitative imaging: systematic review of perfusion/flow phantoms
Marije E Kamphuis1,2, Marcel J W Greuter3,4, Riemer H J A Slart4,5
1Multimodality Medical Imaging M3i Group, Faculty of Science and Technology, Technical Medical Centre, University of Twente, PO Box 217, Enschede, The Netherlands. m.e.kamphuis@utwente.nl.
Background:
We aimed at reviewing design and realisation of perfusion/flow phantoms for validating quantitative perfusion imaging (PI) applications to encourage best practices.
Methods:
A systematic search was performed on the Scopus database for "perfusion", "flow", and "phantom", limited to articles written in English published between January 1999 and December 2018. Information on phantom design, used PI and phantom applications was extracted.
Results:
Of 463 retrieved articles, 397 were rejected after abstract screening and 32 after full-text reading. The 37 accepted articles resulted to address PI simulation in brain (n = 11), myocardial (n = 8), liver (n = 2), tumour (n = 1), finger (n = 1), and non-specific tissue (n = 14), with diverse modalities: ultrasound (n = 11), computed tomography (n = 11), magnetic resonance imaging (n = 17), and positron emission tomography (n = 2). Three phantom designs were described: basic (n = 6), aligned capillary (n = 22), and tissue-filled (n = 12). Microvasculature and tissue perfusion were combined in one compartment (n = 23) or in two separated compartments (n = 17). With the only exception of one study, inter-compartmental fluid exchange could not be controlled. Nine studies compared phantom results with human or animal perfusion data. Only one commercially available perfusion phantom was identified.
Conclusion:
We provided insights into contemporary phantom approaches to PI, which can be used for ground truth evaluation of quantitative PI applications. Investigators are recommended to verify and validate whether assumptions underlying PI phantom modelling are justified for their intended phantom application.
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