deepmr.piecewise_fa#
- deepmr.piecewise_fa(fa_ref=[1.5556, 70.0, 0.7778], length=[350, 230], recovery=300, head=None)[source]#
Design a multi-segment linear flip angle train.
The resulting train, similar to the one introduced by Gomez et al. [1], consists of
len(fa_max)
linear sections. Then
-th sections starts atfa_ref[n] [deg]
, ends atfa_ref[n+1] [deg]
and consists oflength[n]
pulses. The train is followed by a constant flip angle section of lengthrecovery
.- Parameters:
fa_ref (Iterable[float], optional) – Starting flip angle in
[deg]
per linear segment. The default is(1.5556, 70., 0.7778) [deg]
.length (Iterable[int], optional) – Linear segment length. The default is
(350, 200)
pulses.recovery (int, optional) – Constant flip angle recovery train length. The default is
300
.head (Header, optional) – Pre-existing acquisition header. If provided, interpolate FA train to
ncontrast = head.traj.shape[0]
and include it in thehead.FA
field. The default isNone
.
- Returns:
If
head
is not provided, returns piecewise linear flip angle train in[deg]
as atorch.Tensor
. Ifhead
is provided, insert flip angle train (linearly interpolated toncontrast = head.traj.shape[0]
) inhead.FA
field.- Return type:
torch.Tensor | deepmr.Header
References
[1] Gómez, P.A., Cencini, M., Golbabaee, M. et al. Rapid three-dimensional multiparametric MRI with quantitative transient-state imaging. Sci Rep 10, 13769 (2020). https://doi.org/10.1038/s41598-020-70789-2