Table 1:

Description of the neural network architectures used in estimating hemodynamic parameters in our signal model, as well as the respective maximum and minimum values of the ranges used in the training data. The ‘Architecture’ column provides the number of nodes in every layer, separated by hyphens, starting from the input. Each node in the network learns a weight and a bias during training. The input to the networks are fingerprints generated from our designed optimized sequence, which has 700 frames.

Parameter	Depth	Architecture (nodes per layer)	min value	max value
Perfusion	3	10-10-10	0 mL/100g/min	90 mL/100g/min
CBVa	3	10-10-10	0	0.015
BAT	2	10-5	0.3 s	3.0 s
MTR	4	10-10-5-5	0 s−1	0.03 s−1
T 1	1	20	0.33 s	3.33 s
Flip	1	20	48°	112°