Issue reported by Wibeke - see below. We need to change the implementation of this that it uses a slightly different labeling efficiency for BS of the Danny's sequence than for the other sequences. This is connected with issue #429 where we also need to recognize Danny's sequence and take a specific action.

I went into the ASL module in xASL and found out how you calculate the labeling efficacies, which have substantial impact on the perfusion values. For 2 B.S. pulses in a 3D GRASE sequence you have that the labeling efficacy will be reduced with a factor of 0.83, so that the new labeling efficacy will be 0.85 (pcasl) x 0.83 = 0.7055.

Whereas Danny has told us that in his sequence the labeling pulses will decrease the efficacy with 0.93 per B.S. pulse, i.e. with 0.8649, so that the new labeling efficacy is 0.735:

Wang: 0.93 x 0.93 x 0.85 = 0.735

Isn't that ~4 % difference in CBF values? CBF ~ 1/LabEff

switch x.Q.BackgroundSuppressionNumberPulses
        case 0 % when you have an M0, but no background suppression used for ASL
            % Then the labeling efficiency doesn't change by background suppression
        case 2 % e.g. Philips 2D EPI or Siemens 3D GRASE
            x.Q.LabEff_Bsup = 0.83; % 0.83 = 2 background suppression pulses (Garcia et al., MRM 2005)

Shouldn't we change this for the Wang-sequence?

Also - this Wang-Siemens sequence doesn't have the scaling 10, so GM_M0 contrast has to be set to 0.1 in DataPar.json - we should automatically identify this sequence and make sure that the typical siemens scaling is not applied.