dcmri.aif_parker#
- dcmri.aif_parker(t, BAT: float = 0.0) ndarray[source]#
Population AIF model as defined by Parker et al (2006)
- Parameters:
t (array_like) – time points in units of sec.
BAT (float, optional) – Time in seconds before the bolus arrives. Defaults to 0 sec (no delay).
- Returns:
Concentrations in M for each time point in t. If t is a scalar, the return value is a scalar too.
- Return type:
np.ndarray
References
Adapted from a contribution by the QBI lab of the University of Manchester to the OSIPI code repository.
Example
>>> import numpy as np >>> import dcmri as dc
Create an array of time points covering 20sec in steps of 1.5sec, which rougly corresponds to the first pass of the Paeker AIF:
>>> t = np.arange(0, 20, 1.5)
Calculate the Parker AIF at these time points, and output the result in units of mM:
>>> 1000*dc.aif_parker(t) array([0.08038467, 0.23977987, 0.63896354, 1.45093969, 2.75255937, 4.32881325, 5.6309778 , 6.06793854, 5.45203828, 4.1540079 , 2.79568217, 1.81335784, 1.29063036, 1.08751679])
