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Table 3 Design and realisation of general myocardial phantoms for quantitative perfusion imaging (PI)

From: Quantitative imaging: systematic review of perfusion/flow phantoms

PublicationPhantom designPI applicationPhantom application
1st author,
year [reference]
(see Fig. 3)
Flow profileFlow rangeMotion simulationSurrounding tissue simulationPerfusion deficit simulationImaging modalityContrast protocolBlood flow modelInput variablesAIFRFMTTBVBFData comparisonCommercial
Myocardial phantoms
 Zarinabad, 2014 [34]2Ac1–5 B   MRIxMBD (Fermi)1, 4xx  xM, H 
 Chiribiri 2013 [8]2Ac1–10 B   MRIx 1, 2xx     
 Zarinabad, 2012 [35]2Ac1–5 B   MRIxMBD (Fermi), SVD1, 3, 4xx  xM, H 
 O’Doherty, 2017 [36]2Ac3 B   PET,MRIx1-TCM2, 3xx  x  
 O’Doherty, 2017 [37]2Ac1–5 B   PET,MRIx1-TCM1, 3xx  x  
 Otton, 2013 [38]2Ac2–4 B   MR,CTx 1, 3xx     
 Ressner, 2006 [39]3Ac5–10 Cx  USx 1, 2 x  xH 
 Ziemer, 2015 [40]3Ap0.96–2.49 B x CTxMSM1, 4xx  x  
  1. c Continuous, p Pulsatile, A in mL/min, B in mL/min/g, C in cm/s, FAIR Flow-sensitive alternating inversion recovery, MBD Model-based deconvolution, 1-TCM Single tissue compartment model, DWI Diffusion weighted imaging, ASL Arterial spin labelling, SVD Singular value decomposition, MSM Maximum slope model, 1 = Phantom/patient characteristics, 2 = Contrast protocol, 3 = Imaging method, 4 = Flow quantification method, AIF Arterial input function, RF Response function, MTT Mean transit time, BV Blood volume, BF Blood flow, H Human, A Animal, M Mathematical