"""MPRAGE simulator"""
__all__ = ["mprage"]
import warnings
import numpy as np
import dacite
from dacite import Config
from .. import blocks
from .. import ops
from . import epg
[docs]def mprage(
nshots,
flip,
TR,
T1,
T2,
spoil_inc=117.0,
sliceprof=False,
diff=None,
device="cpu",
TI=0.0,
**kwargs
):
"""
Simulate a Magnetization Prepared (MP) Rapid Gradient Echo sequence.
Parameters
----------
nshots : int
Number of pulse in the ``Inversion`` block.
flip : float | np.ndarray | torch.Tensor
Flip angle in ``[deg]`` of shape ``(npulses,)`` or ``(npulses, nmodes)``.
TR : float
Repetition time in [ms].
T1 : float | np.ndarray | torch.Tensor
Longitudinal relaxation time for main pool in ``[ms]``.
T2 : float | np.ndarray | torch.Tensor
Transverse relaxation time for main pool in ``[ms]``.
sliceprof : float | np.ndarray | torch.Tensor
Excitation slice profile (i.e., flip angle scaling across slice).
If ``False``, pulse are non selective. If ``True``, pulses are selective but ideal profile is assumed.
If array, flip angle scaling along slice is simulated. Defaults to ``False``.
spoil_inc : float, optional
RF spoiling increment in ``[deg]``. Defaults to ``117°``.
diff : str | tuple[str], optional
String or tuple of strings, saying which arguments
to get the signal derivative with respect to.
Defaults to ``None`` (no differentation).
device : str
Computational device (e.g., ``cpu`` or ``cuda:n``, with ``n=0,1,2...``).
Defaults to ``cpu``.
TI : float
Inversion time in ``[ms]``.
Defaults to ``None`` (no preparation).
Other Parameters
----------------
nstates : int, optional
Maximum number of EPG states to be retained during simulation.
High numbers improve accuracy but decrease performance.
Defaults to ``10``.
max_chunk_size : int, optional
Maximum number of atoms to be simulated in parallel.
High numbers increase speed and memory footprint.
Defaults to ``natoms``.
verbose : bool, optional
If ``True``, prints execution time for signal (and gradient) calculations.
Defaults to ``False``.
B1sqrdTau : float, optional
Pulse energies in ``[uT**2 * ms]`` when ``flip = 1.0 [deg]``.
global_inversion : bool, optional
Assume nonselective (``True``) or selective (``False``) inversion.
Defaults to ``True``.
inv_B1sqrdTau : float, optional
Inversion pulse energy in ``[uT**2 * ms]`` when ``flip = 1.0 [deg]``.
grad_tau : float, optional
Gradient lobe duration in ``[ms]``.
grad_amplitude : float, optional
Gradient amplitude along unbalanced direction in ``[mT / m]``.
If total_dephasing is not provided, this is used to compute diffusion and flow effects.
grad_dephasing : float, optional
Total gradient-induced dephasing across a voxel (in grad direction).
If gradient_amplitude is not provided, this is used to compute diffusion and flow effects.
voxelsize : str | list | tuple | np.ndarray | torch.Tensor, optional
Voxel size (``dx``, ``dy``, ``dz``) in ``[mm]``.
If scalar, assume isotropic voxel. Defaults to ``None``.
grad_orient : str | list | tuple | np.ndarray | torch.Tensor, optional
Gradient orientation (``"x"``, ``"y"``, ``"z"`` or ``versor``). Defaults to ``"z"``.
slice_orient : str | list | tuple | np.ndarray | torch.Tensor, optional
Slice orientation (``"x"``, ``"y"``, ``"z"`` or ``versor``).
Ignored if pulses are non-selective. Defaults to ``"z"``.
B1 : float | np.ndarray | torch.Tensor , optional
Flip angle scaling factor (``1.0 := nominal flip angle``).
Defaults to ``None``.
B0 : float | np.ndarray | torch.Tensor , optional
Bulk off-resonance in [Hz]. Defaults to ``None``
B1Tx2 : float | np.ndarray | torch.Tensor
Flip angle scaling factor for secondary RF mode (``1.0 := nominal flip angle``).
Defaults to ``None``.
B1phase : float | np.ndarray | torch.Tensor
B1 relative phase in ``[deg]``. (``0.0 := nominal rf phase``).
Defaults to ``None``.
T2star : float | np.ndarray | torch.Tensor
Effective relaxation time for main pool in ``[ms]``.
Defaults to ``None``.
D : float | np.ndarray | torch.Tensor
Apparent diffusion coefficient in ``[um**2 / ms]``.
Defaults to ``None``.
v : float | np.ndarray | torch.Tensor
Spin velocity ``[cm / s]``. Defaults to ``None``.
chemshift : float | np.ndarray | torch.Tensor
Chemical shift for main pool in ``[Hz]``.
Defaults to ``None``.
T1bm : float | np.ndarray | torch.Tensor
Longitudinal relaxation time for secondary pool in ``[ms]``.
Defaults to ``None``.
T2bm : float | np.ndarray | torch.Tensor
Transverse relaxation time for main secondary in ``[ms]``.
Defaults to ``None``.
kbm : float | np.ndarray | torch.Tensor
Nondirectional exchange between main and secondary pool in ``[Hz]``.
Defaults to ``None``.
weight_bm : float | np.ndarray | torch.Tensor
Relative secondary pool fraction.
Defaults to ``None``.
chemshift_bm : float | np.ndarray | torch.Tensor
Chemical shift for secondary pool in ``[Hz]``.
Defaults to ``None``.
kmt : float | np.ndarray | torch.Tensor
Nondirectional exchange between free and bound pool in ``[Hz]``.
If secondary pool is defined, exchange is between secondary and bound pools
(i.e., myelin water and macromolecular), otherwise exchange
is between main and bound pools.
Defaults to ``None``.
weight_mt : float | np.ndarray | torch.Tensor
Relative bound pool fraction.
Defaults to ``None``.
"""
# constructor
init_params = {
"flip": flip,
"TR": TR,
"T1": T1,
"T2": T2,
"diff": diff,
"device": device,
"TI": TI,
**kwargs,
}
# get TE
if "TE" not in init_params:
TE = 0.0
else:
TE = init_params["TE"]
# get verbosity
if "verbose" in init_params:
verbose = init_params["verbose"]
else:
verbose = False
# get verbosity
if "asnumpy" in init_params:
asnumpy = init_params["asnumpy"]
else:
asnumpy = True
# get selectivity:
if sliceprof:
selective_exc = True
else:
selective_exc = False
# add moving pool if required
if selective_exc and "v" in init_params:
init_params["moving"] = True
# check for global inversion
if "global_inversion" in init_params:
selective_inv = not (init_params["global_inversion"])
else:
selective_inv = False
# check for conflicts in inversion selectivity
if selective_exc is False and selective_inv is True:
warnings.warn("3D acquisition - forcing inversion pulse to global.")
selective_inv = False
# inversion pulse properties
if TI is None:
inv_props = {}
else:
inv_props = {"slice_selective": selective_inv}
if "inv_B1sqrdTau" in kwargs:
inv_props["b1rms"] = kwargs["inv_B1sqrdTau"] ** 0.5
inv_props["duration"] = 1.0
# excitation pulse properties
rf_props = {"slice_selective": selective_exc}
if "B1sqrdTau" in kwargs:
inv_props["b1rms"] = kwargs["B1sqrdTau"] ** 0.5
inv_props["duration"] = 1.0
if np.isscalar(sliceprof) is False:
rf_props["slice_profile"] = kwargs["sliceprof"]
# get nlocs
if "nlocs" in init_params:
nlocs = init_params["nlocs"]
else:
if selective_exc:
nlocs = 15
else:
nlocs = 1
# interpolate slice profile:
if "slice_profile" in rf_props:
nlocs = min(nlocs, len(rf_props["slice_profile"]))
else:
nlocs = 1
# assign nlocs
init_params["nlocs"] = nlocs
# unbalanced gradient properties
grad_props = {}
if "grad_tau" in kwargs:
grad_props["duration"] = kwargs["grad_tau"]
if "grad_dephasing" in kwargs:
grad_props["total_dephasing"] = kwargs["grad_dephasing"]
if "voxelsize" in kwargs:
grad_props["voxelsize"] = kwargs["voxelsize"]
if "grad_amplitude" in kwargs:
grad_props["grad_amplitude"] = kwargs["grad_amplitude"]
if "grad_orient" in kwargs:
grad_props["grad_direction"] = kwargs["grad_orient"]
if "slice_orient" in kwargs:
grad_props["slice_direction"] = kwargs["slice_orient"]
# check for possible inconsistencies:
if "total_dephasing" in rf_props and "grad_amplitude" in rf_props:
warnings.warn(
"Both total_dephasing and grad_amplitude are provided - using the first"
)
# put all properties together
props = {
"inv_props": inv_props,
"rf_props": rf_props,
"grad_props": grad_props,
"nshots": nshots,
"spoil_inc": spoil_inc,
}
# initialize simulator
simulator = dacite.from_dict(MPRAGE, init_params, config=Config(check_types=False))
# run simulator
if diff:
# actual simulation
sig, dsig = simulator(flip=flip, TR=TR, TI=TI, TE=TE, props=props)
# post processing
if asnumpy:
sig = sig.detach().cpu().numpy()
dsig = dsig.detach().cpu().numpy()
# prepare info
info = {"trun": simulator.trun, "tgrad": simulator.tgrad}
if verbose:
return sig, dsig, info
else:
return sig, dsig
else:
# actual simulation
sig = simulator(flip=flip, TR=TR, TI=TI, TE=TE, props=props)
# post processing
if asnumpy:
sig = sig.cpu().numpy()
# prepare info
info = {"trun": simulator.trun}
if verbose:
return sig, info
else:
return sig
# %% utils
spin_defaults = {"T2star": None, "D": None, "v": None}
class MPRAGE(epg.EPGSimulator):
"""Class to simulate inversion-prepared Rapid Gradient Echo."""
@staticmethod
def sequence(
flip,
TR,
TI,
TE,
props,
T1,
T2,
B1,
df,
weight,
k,
chemshift,
D,
v,
states,
signal,
):
# parsing pulses and grad parameters
inv_props = props["inv_props"]
rf_props = props["rf_props"]
grad_props = props["grad_props"]
spoil_inc = props["spoil_inc"]
npulses = props["nshots"]
# define preparation
Prep = blocks.InversionPrep(TI, T1, T2, weight, k, inv_props)
# prepare RF pulse
RF = blocks.ExcPulse(states, B1, rf_props)
# prepare free precession period
X, XS = blocks.SSFPFidStep(
states, TE, TR, T1, T2, weight, k, chemshift, D, v, grad_props
)
# initialize phase
phi = 0
dphi = 0
# magnetization prep
states = Prep(states)
# actual sequence loop
for n in range(npulses):
# update phase
dphi = (phi + spoil_inc) % 360.0
phi = (phi + dphi) % 360.0
# apply pulse
states = RF(states, flip[n], phi)
# relax, recover and record signal for each TE
states = X(states)
signal[n] = ops.observe(states, RF.phi)
# relax, recover and spoil
states = XS(states)
return ops.susceptibility(signal, TE, df)
