dcmri.aif_tristan_rat#

dcmri.aif_tristan_rat(t, BAT=276.0, duration=30) → ndarray[source]#

Population AIF model for rats measured with a standard dose of gadoxetate.

Parameters:

t (array_like) – time points in units of sec.
BAT (float, optional) – Time in seconds before the bolus arrives. Defaults to 4.6 min.
duration (float, optional) – Duration of the injection. Defaults to 30s.

Returns:

Blood 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

Melillo N, Scotcher D, Kenna JG, Green C, Hines CDG, Laitinen I, et al. Use of In Vivo Imaging and Physiologically-Based Kinetic Modelling to Predict Hepatic Transporter Mediated Drug-Drug Interactions in Rats. Pharmaceutics 2023;15(3):896.
Gunwhy, E. R., & Sourbron, S. (2023). TRISTAN-RAT (v3.0.0). Zenodo

Example:

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> import dcmri as dc

Create an array of time points over 30 minutes

>>> t = np.arange(0, 30*60, 0.1)

Generate the rat input function for these time points:

>>> cb = dc.aif_tristan_rat(t)

Plot the result:

>>> plt.plot(t/60, 1000*cb, 'r-')
>>> plt.title('TRISTAN rat AIF')
>>> plt.xlabel('Time (min)')
>>> plt.ylabel('Blood concentration (mM)')
>>> plt.show()