Table 2.

Design and realisation of brain perfusion phantoms for quantitative perfusion imaging (PI)

Publication	Phantom design			PI application						Phantom application
1st author, year [reference]	Configuration (see Fig. 3)	Flow profile	Flow range	Motion simulation	Surrounding tissue simulation	Perfusion deficit simulation	Imaging modality	Contrast protocol	Blood flow model	Input variables	AIF	RF	MTT	BV	BF	Data comparison	Commercial
Brain phantoms
Boese, 2013 [23]	1A	p	800 A			x	CT	x	MBD	1–3	x	x		x	x
Hashimoto, 2018 [24]	2A	c	60 A		x		CT	x	SVD	2, 3			x	x	x	M
Suzuki, 2017 [25]	2A	c	60 A		x		CT	x	SVD	3	x	x	x	x	x	M
Noguchi, 2007 [26]	2A	c	0–2.16 C				MRI		ASL	1		x			x
Wang, 2010 [27]	2B	c	45–180 A				MRI		ASL	1		x			x	M, H
Cangür, 2004 [28]	2B	c	1.8–21.6 A		x		US	x		1		x
Klotz, 1999 [29]	2B	c	50–140 A		x		CT	x	MSM	1	x	x			x	H
Claasse, 2001 [30]	2B	p	180–540 A				US	x	MBD	1, 2		x	x			A
Mathys, 2012 [31]	3A	c	200–600 A		x		CT	x	SVD, MSM	1–4	x	x		x	x
Ebrahimi, 2010 [32]	3A	c	012–1.2 A				MRI	x	SVD	1	x	x	x	x	x	M
Ohno, 2017 [33]	3B	p	240–480 A				MRI		ASL	1		x			x

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