Method for obtaining arterial input function from region of interest

A method for computing an arterial input function (AIF) from a region of interest (ROI), the method comprising:

• a. obtaining a plurality of dynamic image data sets comprising volumetric image data from one or more regions of interest (ROI) over multiple scanning intervals;
• b. utilizing an artificial neural network to segment the plurality of dynamic image data sets displaying one or more arterial input function(s) (AIF) in the one or more regions of interest;
• c. automatically estimating, using artificial intelligence, an arterial input function (AIF) based on plurality of dynamic image data sets combined with one or more-time activity curves (TAC) in the one or more regions of interest (ROI) in target organ(s);
• and
• d. computing a pre-trained predictive pharmacokinetic AI model arterial input function using time activity curve (TAC) input associated with the one or more regions of interest (ROI) of target organ(s);
• wherein the pre-trained predictive pharmacokinetic AI model is used for estimating the associated parameter to determine the parametric map.

A method for computing an arterial input function (AIF) from a region of interest (ROI), the method comprising:

• a. obtaining a plurality of PET dynamic image data sets comprising volumetric image data from the region of interest (ROI) over multiple scanning intervals;
• b. utilizing an artificial neural network to segment the plurality of dynamic image data sets displaying one or more arterial input function(s) (AIF) in the region of interest (ROI);
• c. automatically estimating, using artificial intelligence, an arterial input function (AIF) based on plurality of dynamic image data sets combined with one or more-time activity curves (TAC) in the region of interest (ROI) in a target organ;
• and
• d. computing a pre-trained predictive pharmacokinetic AI model arterial input function (AIF) using time activity curve (TAC) input associated with the region of interest (ROI) of the target organ;
• wherein the pre-trained predictive pharmacokinetic AI model is used for estimating the associated parameter comprising K1, K2 and TBV to estimate myocardial flow reserve (MFR) map and/or coronary flow reserve (CFR) map.