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Table 2 Design and realisation of brain perfusion 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
Brain phantoms
 Boese, 2013 [23]1Ap800 A  xCTxMBD1–3xx xx  
 Hashimoto, 2018 [24]2Ac60 A x CTxSVD2, 3  xxxM 
 Suzuki, 2017 [25]2Ac60 A x CTxSVD3xxxxxM 
 Noguchi, 2007 [26]2Ac0–2.16 C   MRI ASL1 x  x  
 Wang, 2010 [27]2Bc45–180 A   MRI ASL1 x  xM, H 
 Cangür, 2004 [28]2Bc1.8–21.6 A x USx 1 x     
 Klotz, 1999 [29]2Bc50–140 A x CTxMSM1xx  xH 
 Claasse, 2001 [30]2Bp180–540 A   USxMBD1, 2 xx  A 
 Mathys, 2012 [31]3Ac200–600 A x CTxSVD, MSM1–4xx xx  
 Ebrahimi, 2010 [32]3Ac012–1.2 A   MRIxSVD1xxxxxM 
 Ohno, 2017 [33]3Bp240–480 A   MRI ASL1 x  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