def align(*args) -> list:
"""
Aligns `SimpleNamespace` blocks as per specified alignment options by setting delays of the pulse sequence events
within the block. All previously configured delays within objects are taken into account during calculating of the
block duration but then reset according to the selected alignment. Possible values for align_spec are 'left',
'center', 'right'.
Parameters
----------
args : list
List of alignment options and `SimpleNamespace` blocks.
Template: [alignment_spec, 'SimpleNamespace` block, [alignment_spec, `SimpleNamespace` block, ...]].
Alignment spec can be one of `left`, `center` or `right`.
Returns
-------
objects : list
List of aligned `SimpleNamespace` blocks.
"""
alignment_options = ['left', 'center', 'right']
if not isinstance(args[0], str):
raise ValueError('First parameter must be of type str.')
curr_align = alignment_options.index(
args[0]) if args[0] in alignment_options else None
i_objects = []
alignments = []
for i in range(1, len(args)):
if curr_align is None:
raise ValueError('Invalid alignment spec.')
if isinstance(args[i], str):
curr_align = alignment_options.index(
args[i]) if args[i] in alignment_options else None
continue
i_objects.append(i)
alignments.append(curr_align)
args = np.array(args)
objects = args[i_objects]
dur = calc_duration(*objects)
for i in range(len(objects)):
if alignments[i] == 0:
objects[i].delay = 0
elif alignments[i] == 1:
objects[i].delay = (dur - calc_duration(objects[i])) / 2
elif alignments[i] == 2:
objects[i].delay = dur - calc_duration(
objects[i]) + objects[i].delay
return objects
def gradient_waveforms(self):
duration, num_blocks, _ = self.duration()
wave_length = math.ceil(duration / self.grad_raster_time)
grad_channels = 3
grad_waveforms = np.zeros((grad_channels, wave_length))
grad_channels = ['gx', 'gy', 'gz']
t0 = 0
t0_n = 0
for i in range(num_blocks):
block = self.get_block(i + 1)
for j in range(len(grad_channels)):
if hasattr(block, grad_channels[j]):
grad = getattr(block, grad_channels[j])
if grad.type == 'grad':
nt_start = round(
(grad.delay + grad.t[0]) / self.grad_raster_time)
waveform = grad.waveform
else:
nt_start = round(grad.delay / self.grad_raster_time)
if abs(grad.flat_time) > np.finfo(float).eps:
t = np.cumsum([
0, grad.rise_time, grad.flat_time,
grad.fall_time
])
trap_form = np.multiply([0, 1, 1, 0],
grad.amplitude)
else:
t = np.cumsum([0, grad.rise_time, grad.fall_time])
trap_form = np.multiply([0, 1, 0], grad.amplitude)
tn = math.floor(t[-1] / self.grad_raster_time)
t = np.append(t, t[-1] + self.grad_raster_time)
trap_form = np.append(trap_form, 0)
if abs(grad.amplitude) > np.finfo(float).eps:
waveform = points_to_waveform(
t, trap_form, self.grad_raster_time)
else:
waveform = np.zeros(tn + 1)
if waveform.size != np.sum(np.isfinite(waveform)):
raise Warning(
'Not all elements of the generated waveform are finite'
)
grad_waveforms[
j,
int(t0_n +
nt_start):int(t0_n + nt_start +
max(waveform.shape))] = waveform
t0 += calc_duration(block)
t0_n = round(t0 / self.grad_raster_time)
return grad_waveforms
def duration(self):
num_blocks = len(self.block_events)
event_count = np.zeros(len(self.block_events[1]))
duration = 0
for i in range(num_blocks):
block = self.get_block(i + 1)
event_count += self.block_events[i + 1] > 0
duration += calc_duration(block)
return duration, num_blocks, event_count
def _SAR_from_seq(
seq: Sequence, Qtmf: np.ndarray,
Qhmf: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
Compute global whole body and head only SAR values for the given `seq` object.
Parameters
----------
seq : Sequence
Sequence object to calculate for which SAR values will be calculated.
Qtmf : numpy.ndarray
Q-matrix of global SAR values for body-mass.
Qhmf : numpy.ndarray
Q-matrix of global SAR values for head-mass.
Returns
-------
SAR_wbg : numpy.ndarray
SAR values for body-mass.
SAR_hg : numpy.ndarray
SAR values for head-mass.
t : numpy.ndarray
Corresponding time points.
"""
# Identify RF blocks and compute SAR - 10 seconds must be less than twice and 6 minutes must be less than
# 4 (WB) and 3.2 (head-20)
block_events = seq.dict_block_events
num_events = len(block_events)
t = np.zeros(num_events)
SAR_wbg = np.zeros(t.shape)
SAR_hg = np.zeros(t.shape)
t_prev = 0
for block_counter in block_events:
block = seq.get_block(block_counter)
block_dur = calc_duration(block)
t[block_counter - 1] = t_prev + block_dur
t_prev = t[block_counter - 1]
if hasattr(block, 'rf'): # has rf
rf = block.rf
signal = rf.signal
# This rf could be parallel transmit as well
SAR_wbg[block_counter] = _calc_SAR(Qtmf, signal)
SAR_hg[block_counter] = _calc_SAR(Qhmf, signal)
return SAR_wbg, SAR_hg, t
def SARfromseq(fname, Qtmf, Qhmf):
"""
This definition computes the global whole body and head only SAR values
Parameters
----------
fname : str
Qtmf : numpy.ndarray
Qhmf : numpy.ndarray
Returns
-------
SARwbg_vec : numpy.ndarray
SARhg_vec : numpy.ndarray
t_vec : numpy.ndarray
contains the Q-matrix, GSAR head and body for now
"""
obj = Sequence()
obj.read(str(SAR_PATH / 'assets' / fname)) # replaced by
# Identify rf blocks and compute SAR - 10 seconds must be less than twice and 6 minutes must be less than 4 (WB) and 3.2 (head-20)
blockEvents = obj.block_events
numEvents = len(blockEvents)
t_vec = np.zeros(numEvents)
SARwbg_vec = np.zeros(t_vec.shape)
SARhg_vec = np.zeros(t_vec.shape)
t_prev = 0
for iB in blockEvents:
block = obj.get_block(iB)
block_dur = calc_duration(block)
t_vec[iB - 1] = t_prev + block_dur
t_prev = t_vec[iB - 1]
if ('rf' in block): # has rf
rf = block['rf']
t = rf.t
signal = rf.signal
# This rf could be parallel transmit as well
SARwbg_vec[iB] = calc_SAR(Qtmf, signal)
SARhg_vec[iB] = calc_SAR(Qhmf, signal)
return SARwbg_vec, SARhg_vec, t_vec
def check_timing(system, *events):
total_dur = calc_duration(*events)
is_ok = __div_check(total_dur, system.grad_raster_time)
if is_ok:
text_err = str()
else:
text_err = f'Total duration: {total_dur * 1e6} us'
for i in range(len(events)):
e = events[i]
ok = True
if hasattr(e, 'type') and e.type == 'adc' or e.type == 'rf':
raster = system.rf_raster_time
else:
raster = system.grad_raster_time
if hasattr(e, 'delay'):
if not __div_check(e.delay, raster):
ok = False
if hasattr(e, 'type') and e.type == 'trap':
if not __div_check(e.rise_time, system.grad_raster_time) or \
not __div_check(e.flat_time, system.grad_raster_time) or \
not __div_check(e.fall_time, system.grad_raster_time):
ok = False
if not ok:
is_ok = False
if len(text_err) != 0:
text_err += ' '
text_err += '[ '
if hasattr(e, 'type'):
text_err += f'type: {e.type} '
if hasattr(e, 'delay'):
text_err += f'delay: {e.delay * 1e6} us '
if hasattr(e, 'type') and e.type == 'trap':
text_err += f'rise time: {e.rise_time * 1e6} flat time: {e.flat_time * 1e6} ' \
f'fall time: {e.fall_time * 1e6} us '
text_err += ']'
return is_ok, text_err
def __SAR_from_seq(seq, Qtmf, Qhmf):
"""
Compute global whole body and head only SAR values.
Parameters
----------
seq : Sequence
Qtmf : numpy.ndarray
Qhmf : numpy.ndarray
Returns
-------
SAR_wbg_vec : numpy.ndarray
SAR_hg_vec : numpy.ndarray
t_vec : numpy.ndarray
Contains the Q-matrix, GSAR head and body for now.
"""
# Identify RF blocks and compute SAR - 10 seconds must be less than twice and 6 minutes must be less than
# 4 (WB) and 3.2 (head-20)
block_events = seq.block_events
num_events = len(block_events)
t_vec = np.zeros(num_events)
SAR_wbg_vec = np.zeros(t_vec.shape)
SAR_hg_vec = np.zeros(t_vec.shape)
t_prev = 0
for iB in block_events:
block = seq.get_block(iB)
block_dur = calc_duration(block)
t_vec[iB - 1] = t_prev + block_dur
t_prev = t_vec[iB - 1]
if hasattr(block, 'rf'): # has rf
rf = block.rf
t = rf.t
signal = rf.signal
# This rf could be parallel transmit as well
SAR_wbg_vec[iB] = __calc_SAR(Qtmf, signal)
SAR_hg_vec[iB] = __calc_SAR(Qhmf, signal)
return SAR_wbg_vec, SAR_hg_vec, t_vec
def duration(self):
"""
Get duration of this sequence.
Returns
-------
duration : float
Duration of this sequence in millis.
num_blocks : int
Number of blocks in this sequence.
event_count : int
Number of events in this sequence.
"""
num_blocks = len(self.block_events)
event_count = np.zeros(len(self.block_events[1]))
duration = 0
for i in range(num_blocks):
block = self.get_block(i + 1)
event_count += self.block_events[i + 1] > 0
duration += calc_duration(block)
return duration, num_blocks, event_count
gdir, nb0s = difunc.get_dirs(ndirs)
#to avoid exceeding gradient limits
gdfact = 1.0
tr_per_slice = tr / n_slices
system = Opts(max_grad=32, grad_unit='mT/m', max_slew=130, slew_unit='T/m/s', rf_ringdown_time=30e-6,
rf_dead_time=100e-6, adc_dead_time=20e-6)
b0 = 2.89
sat_ppm = -3.45
sat_freq = sat_ppm * 1e-6 * b0 * system.gamma
rf_fs, _, _ = make_gauss_pulse(flip_angle=110 * math.pi / 180, system=system, duration=8e-3, bandwidth=abs(sat_freq),
freq_offset=sat_freq)
gz_fs = make_trapezoid(channel='z', system=system, delay=calc_duration(rf_fs), area=1 / 1e-4)
rf, gz, gz_reph = make_sinc_pulse(flip_angle=math.pi / 2, system=system, duration=3e-3, slice_thickness=slice_thickness,
apodization=0.5, time_bw_product=4)
rf180, gz180, _ = make_sinc_pulse(flip_angle=math.pi, system=system, duration=5e-3, slice_thickness=slice_thickness,
apodization=0.5, time_bw_product=4)
rf180.phase_offset = math.pi/2
gz_spoil = make_trapezoid(channel='z', system=system, area=2*gz.area, duration=3e-3)
delta_k = 1 / fov
k_width = Nx * delta_k
readout_time = 1.05e-3
blip_dur = math.ceil(2 * math.sqrt(delta_k / system.max_slew) / seq.grad_raster_time / 2) * seq.grad_raster_time * 2
def opt_TE_bv_TRSE(bvalue_Dict, grads_times_Dict, seq_sys_Dict):
"""
Obtain optimal TE and diffusion-weighting gradient waveforms for twice-refocued spin echo (TRSE) DW sequence.
Parameters
----------
bvalue_Dict : dictionary
b-value related parameters needed in the TE optimization.
grads_times_Dict : dictionary
gradient times related parameters needed in the TE optimization.
seq_sys_Dict : dictionary
sequence and system related parameters needed in the TE optimization.
Returns
-------
TE : float
echo time (s).
bval : float
b-value in s/mm2
waveform : float
diffusion-weighting gradient waveform.
gscl_max : float
gradient scale factor of the diffusion-weighting gradient waveform.
d1 : float
duration of the first diffusion-weighting gradient lobe between the RF90 and the first RF180 (s).
d2 : float
duration of the second diffusion-weighting gradient lobe after the first RF180 (s).
d3 : float
duration of the third diffusion-weighting gradient lobe before the second RF180 (s).
d4 : float
duration of the last diffusion-weighting gradient lobe after the second RF180 (s).
"""
# Description
# For TRSE waveforms.
# From an initial TE, check we satisfy all constraints -> otherwise increase TE.
# Once all constraints are okay -> check b-value, if it is lower than the target one -> increase TE
# Finally, with TRSE low b-values can not be acquired, thus proper scaling is needed.
# Looks time-inefficient but it is fast enough to make it user-friendly.
bvalue = bvalue_Dict["bvalue"] # Target b-value.
nbvals = bvalue_Dict["nbvals"] # number of b-values.
gscl = bvalue_Dict["gscl"] # gradient scaling.
rf180 = grads_times_Dict["rf180"] # RF180
rf180_center_with_delay = grads_times_Dict[
"rf180_center_with_delay"] # time to the center of the RF180
rf_center_with_delay = grads_times_Dict[
"rf_center_with_delay"] # time to the center of the RF90
gz_spoil_1 = grads_times_Dict[
"gz_spoil_1"] # Spoil gradient to be placed around the first RF180
gz_spoil_2 = grads_times_Dict[
"gz_spoil_2"] # Spoil gradient to be placed around the second RF180
gz = grads_times_Dict["gz"] # RF90 gradient
duration_center = grads_times_Dict[
"duration_center"] # Time needed to achieve the center of the k-space.
pre_time = grads_times_Dict["pre_time"] # (s)
seq = seq_sys_Dict["seq"] # Sequence
system = seq_sys_Dict["system"] # System
# Find minimum TE considering the readout times and RF + spoil gradient durations
TE = 80e-3 # [s] It's a reasonable estimate for TRSE.
delay_tela = -1 # Large TE/2
while delay_tela <= 0:
TE = TE + 0.02e-3 # [ms]
delay_tela = math.ceil((TE / 2 + (- calc_duration(rf180) + rf180_center_with_delay - calc_duration(gz_spoil_2) - \
duration_center) + (- calc_duration(gz) + rf_center_with_delay - \
pre_time - calc_duration(gz_spoil_1) - rf180_center_with_delay)) / seq.grad_raster_time) * seq.grad_raster_time
# Sice there are 3 equations and 4 parameters (TGReese2002 - https://doi.org/10.1002/mrm.10308)
# One parameter can be tuned, while the other 3 are then fixed. This is why we fix d4.
d4 = 3e-3 # [ms]
# Find minimum TE for the target d4
# Waveform Ramp time
gdiff_rt = math.ceil(system.max_grad / system.max_slew /
seq.grad_raster_time) * seq.grad_raster_time
d1 = -1
while d1 <= 2 * gdiff_rt: # Include this condition to have trapezoids everywhere
TE = TE + 2 * seq.grad_raster_time # [ms]
# Large TE/2
delay_tela = math.ceil((TE / 2 + (- calc_duration(rf180) + rf180_center_with_delay - calc_duration(gz_spoil_2) - \
duration_center) + (- calc_duration(gz) + rf_center_with_delay - \
pre_time - calc_duration(gz_spoil_1) - rf180_center_with_delay)) / seq.grad_raster_time) * seq.grad_raster_time
# Short TE/2 (Time between RF180s)
delay_tes = math.ceil((TE / 2 - calc_duration(rf180) + rf180_center_with_delay - calc_duration(gz_spoil_1) - \
calc_duration(gz_spoil_2) - rf180_center_with_delay) / seq.grad_raster_time) * seq.grad_raster_time
d1 = math.ceil(
(delay_tela - d4) / seq.grad_raster_time) * seq.grad_raster_time
# Find minimum TE for the target b-value
bvalue_tmp = 0
# We initially substract 2 times the raster time because we don't know if it will achieve the desired b-value from the beginning.
# Note that for low b-values, the TE is the same for all of them due to the large number of elements of this sequence.
TE = TE - 2 * seq.grad_raster_time
while bvalue_tmp < np.max(bvalue):
# The following scalar multiplication is just to increase speed
TE = TE + int(math.ceil(
np.max(bvalue) / 500)) * 4 * seq.grad_raster_time # [ms]
# Large TE/2
delay_tela = math.ceil((TE / 2 + (- calc_duration(rf180) + rf180_center_with_delay - calc_duration(gz_spoil_2) - \
duration_center) + (- calc_duration(gz) + rf_center_with_delay - \
pre_time - calc_duration(gz_spoil_1) - rf180_center_with_delay)) / seq.grad_raster_time) * seq.grad_raster_time
# Short TE/2 (Time between RF180s)
delay_tes = math.ceil((TE / 2 - calc_duration(rf180) + rf180_center_with_delay - calc_duration(gz_spoil_1) - \
calc_duration(gz_spoil_2) - rf180_center_with_delay) / seq.grad_raster_time) * seq.grad_raster_time
d1 = math.ceil(
(delay_tela - d4) / seq.grad_raster_time) * seq.grad_raster_time
d3 = math.ceil(((d1 + delay_tes - d4) / 2) /
seq.grad_raster_time) * seq.grad_raster_time
d2 = math.ceil(
(delay_tes - d3) / seq.grad_raster_time) * seq.grad_raster_time
# Due to the complexity of the sequence and that I have not found its b-value, I implement it by its definition.
# However, for simplicity I compute each gradient lobe as ideal rectangular pulses.
# d1 start after the RF90
n1 = int(
math.ceil((calc_duration(gz) - rf_center_with_delay + pre_time +
seq.grad_raster_time) / seq.grad_raster_time) *
seq.grad_raster_time / seq.grad_raster_time)
nd1 = int(
math.ceil(d1 / seq.grad_raster_time) * seq.grad_raster_time /
seq.grad_raster_time)
# d2 start after the first RF180
n2 = int(
math.ceil(((n1 + nd1) * seq.grad_raster_time +
2 * calc_duration(gz_spoil_1) + calc_duration(rf180) +
seq.grad_raster_time) / seq.grad_raster_time) *
seq.grad_raster_time / seq.grad_raster_time)
nd2 = int(
math.ceil(d2 / seq.grad_raster_time) * seq.grad_raster_time /
seq.grad_raster_time)
# d3 starts after d2
n3 = int(
math.ceil(
((n2 + nd2) * seq.grad_raster_time + seq.grad_raster_time) /
seq.grad_raster_time) * seq.grad_raster_time /
seq.grad_raster_time)
nd3 = int(
math.ceil(d3 / seq.grad_raster_time) * seq.grad_raster_time /
seq.grad_raster_time)
# d4 starts after the second RF180
n4 = int(
math.ceil(((n2 + nd2 + nd3) * seq.grad_raster_time +
2 * calc_duration(gz_spoil_2) + calc_duration(rf180) +
seq.grad_raster_time) / seq.grad_raster_time) *
seq.grad_raster_time / seq.grad_raster_time)
nd4 = int(
math.ceil(d4 / seq.grad_raster_time) * seq.grad_raster_time /
seq.grad_raster_time)
# Due to the large TE of TRSE and the high resolution we might need to shrink the following array.
# To speed up computation we calculate the b-value with short_f times lower of the time resolution.
# Shortening factor:
short_f = 5
n = int(np.ceil(TE / seq.grad_raster_time) / short_f)
waveform = np.zeros(n)
# Compose waveform
waveform[math.ceil(n1 / short_f):math.floor((n1 + nd1 + 1) /
short_f)] = system.max_grad
waveform[math.ceil(n2 /
short_f):math.floor((n2 + nd2 + 1) /
short_f)] = -system.max_grad
waveform[math.ceil(n3 / short_f):math.floor((n3 + nd3 + 1) /
short_f)] = system.max_grad
waveform[math.ceil(n4 /
short_f):math.floor((n4 + nd4 + 1) /
short_f)] = -system.max_grad
# Include ramps
nrt = int(
math.floor(
math.floor(
system.max_grad / system.max_slew / seq.grad_raster_time) *
seq.grad_raster_time / seq.grad_raster_time / short_f))
ramp_values = np.linspace(
1, nrt, nrt) * seq.grad_raster_time * system.max_slew * short_f
# d1
if n1 % short_f == 0:
waveform[math.ceil(n1 / short_f):math.ceil(n1 / short_f) +
nrt] = ramp_values
else:
waveform[math.ceil(n1 / short_f):math.ceil((n1 + 1) / short_f) +
nrt] = ramp_values
waveform[math.ceil((n1 + nd1 - 1) / short_f -
nrt):math.ceil((n1 + nd1 - 1) /
short_f)] = np.flip(ramp_values)
# d2
if n2 % short_f == 0:
waveform[math.ceil(n2 / short_f):math.ceil(n2 / short_f) +
nrt] = -ramp_values
else:
waveform[math.ceil(n2 / short_f):math.ceil((n2 + 1) / short_f) +
nrt] = -ramp_values
waveform[math.ceil((n2 + nd2 - 1) / short_f -
nrt):math.ceil((n2 + nd2 - 1) /
short_f)] = -np.flip(ramp_values)
# d3
if n3 % short_f == 0:
waveform[math.ceil(n3 / short_f):math.ceil(n3 / short_f) +
nrt] = ramp_values
else:
waveform[math.ceil(n3 / short_f):math.ceil((n3 + 1) / short_f) +
nrt] = ramp_values
waveform[math.ceil((n3 + nd3 - 1) / short_f -
nrt):math.ceil((n3 + nd3 - 1) /
short_f)] = np.flip(ramp_values)
# d4
if n4 % short_f == 0:
waveform[math.ceil(n4 / short_f):math.ceil(n4 / short_f) +
nrt] = -ramp_values
else:
waveform[math.ceil(n4 / short_f):math.ceil((n4 + 1) / short_f) +
nrt] = -ramp_values
waveform[math.ceil((n4 + nd4 - 1) / short_f -
nrt):math.ceil((n4 + nd4 - 1) /
short_f)] = -np.flip(ramp_values)
# Prepare Integral
nRF180_1 = int(
math.ceil((n2 * seq.grad_raster_time - calc_duration(rf180) +
rf180_center_with_delay - calc_duration(gz_spoil_1)) /
short_f / seq.grad_raster_time) * seq.grad_raster_time /
seq.grad_raster_time)
nRF180_2 = int(
math.ceil((n4 * seq.grad_raster_time - calc_duration(rf180) +
rf180_center_with_delay - calc_duration(gz_spoil_2)) /
short_f / seq.grad_raster_time) * seq.grad_raster_time /
seq.grad_raster_time)
INV = np.ones(n)
INV[nRF180_1:nRF180_2 + 1] = -1
bvalue_tmp = calc_exact_bval(waveform, INV, short_f, seq)
print("b-value low time resolution:", round(bvalue_tmp, 2), "s/mm2")
# To know the exact b-value - repeat the above with full time resolution
short_f = 1
n = int(np.ceil(TE / seq.grad_raster_time) / short_f)
waveform = np.zeros(n)
# Compose waveform
waveform[math.ceil(n1 / short_f):math.floor((n1 + nd1 + 1) /
short_f)] = system.max_grad
waveform[math.ceil(n2 / short_f):math.floor((n2 + nd2 + 1) /
short_f)] = -system.max_grad
waveform[math.ceil(n3 / short_f):math.floor((n3 + nd3 + 1) /
short_f)] = system.max_grad
waveform[math.ceil(n4 / short_f):math.floor((n4 + nd4 + 1) /
short_f)] = -system.max_grad
# Include ramps
nrt = int(
math.floor(
math.floor(
system.max_grad / system.max_slew / seq.grad_raster_time) *
seq.grad_raster_time / seq.grad_raster_time / short_f))
ramp_values = np.linspace(
0, nrt, nrt) * seq.grad_raster_time * system.max_slew * short_f
# d1
if n1 % short_f == 0:
waveform[math.ceil(n1 / short_f):math.ceil(n1 / short_f) +
nrt] = ramp_values
else:
waveform[math.ceil(n1 / short_f):math.ceil((n1 + 1) / short_f) +
nrt] = ramp_values
if (n1 + nd1) % short_f == 0:
waveform[math.ceil((n1 + nd1) / short_f - nrt) +
1:math.floor((n1 + nd1 + 1) / short_f)] = np.flip(ramp_values)
else:
waveform[math.ceil((n1 + nd1) / short_f -
nrt):math.floor((n1 + nd1 + 1) /
short_f)] = np.flip(ramp_values)
# d2
if n2 % short_f == 0:
waveform[math.ceil(n2 / short_f):math.ceil(n2 / short_f) +
nrt] = -ramp_values
else:
waveform[math.ceil(n2 / short_f):math.ceil((n2 + 1) / short_f) +
nrt] = -ramp_values
if (n2 + nd2) % short_f == 0:
waveform[math.ceil((n2 + nd2) / short_f - nrt) +
1:math.floor((n2 + nd2 + 1) /
short_f)] = -np.flip(ramp_values)
else:
waveform[math.ceil((n2 + nd2) / short_f -
nrt):math.floor((n2 + nd2 + 1) /
short_f)] = -np.flip(ramp_values)
# d3
if n3 % short_f == 0:
waveform[math.ceil(n3 / short_f):math.ceil(n3 / short_f) +
nrt] = ramp_values
else:
waveform[math.ceil(n3 / short_f):math.ceil((n3 + 1) / short_f) +
nrt] = ramp_values
if (n3 + nd3) % short_f == 0:
waveform[math.ceil((n3 + nd3) / short_f - nrt) +
1:math.floor((n3 + nd3 + 1) / short_f)] = np.flip(ramp_values)
else:
waveform[math.ceil((n3 + nd3) / short_f -
nrt):math.floor((n3 + nd3 + 1) /
short_f)] = np.flip(ramp_values)
# d4
if n4 % short_f == 0:
waveform[math.ceil(n4 / short_f):math.ceil(n4 / short_f) +
nrt] = -ramp_values
else:
waveform[math.ceil(n4 / short_f):math.ceil((n4 + 1) / short_f) +
nrt] = -ramp_values
if (n4 + nd4) % short_f == 0:
waveform[math.ceil((n4 + nd4) / short_f - nrt) +
1:math.floor((n4 + nd4 + 1) /
short_f)] = -np.flip(ramp_values)
else:
waveform[math.ceil((n4 + nd4) / short_f -
nrt):math.floor((n4 + nd4 + 1) /
short_f)] = -np.flip(ramp_values)
# Prepare Integral
nRF180_1 = int(
math.ceil((n2 * seq.grad_raster_time - calc_duration(rf180) +
rf180_center_with_delay - calc_duration(gz_spoil_1)) /
short_f / seq.grad_raster_time) * seq.grad_raster_time /
seq.grad_raster_time)
nRF180_2 = int(
math.ceil((n4 * seq.grad_raster_time - calc_duration(rf180) +
rf180_center_with_delay - calc_duration(gz_spoil_2)) /
short_f / seq.grad_raster_time) * seq.grad_raster_time /
seq.grad_raster_time)
INV = np.ones(n)
INV[nRF180_1:nRF180_2 + 1] = -1
bval = calc_exact_bval(waveform, INV, short_f, seq)
print("b-value high time resolution:", round(bval, 2), "s/mm2")
# Scale gradients amplitude accordingly.
if bval > np.max(bvalue):
gscl_max = np.sqrt(np.max(bvalue) / bval)
else:
gscl_max = 1
# Show final TE and b-values:
print("Final times:")
print("TE:", round(TE * 1e3, 2), "ms")
for bv in range(1, nbvals + 1):
bval_tmp = calc_exact_bval(waveform * gscl_max * gscl[bv], INV,
short_f, seq)
print(round(bval_tmp, 2), "s/mm2")
return TE, bval, waveform, gscl_max, d1, d2, d3, d4
def opt_TE_bv_SE(bvalue_Dict, grads_times_Dict, seq_sys_Dict):
"""
Obtain optimal TE and diffusion-weighting gradient waveforms for twice-refocued spin echo (TRSE) DW sequence.
Parameters
----------
bvalue_Dict : dictionary
b-value related parameters needed in the TE optimization.
grads_times_Dict : dictionary
gradient times related parameters needed in the TE optimization.
seq_sys_Dict : dictionary
sequence and system related parameters needed in the TE optimization.
Returns
-------
n_TE : int
echo time in integers units.
bval : float
b-value in s/mm2
gdiff : pypulseq gradient
diffusion-weighting gradient waveform.
n_delay_te1 : int
delay between RF90 and RF180 in integers units.
n_delay_te2 : int
delay between RF180 and EPI readout in integers units.
"""
# Description
# For S&T monopolar waveforms.
# From an initial TE, check we satisfy all constraints -> otherwise increase TE.
# Once all constraints are okay -> check b-value, if it is lower than the target one -> increase TE
# Looks time-inefficient but it is fast enough to make it user-friendly.
# We need to compute the exact time sequence. For the normal SE-MONO-EPI sequence micro second differences
# are not important, however, if we wanna import external gradients the allocated time for them needs to
# be the same, and thus exact timing is mandatory. With this in mind, we establish the following rounding rules:
# Duration of RFs + spoiling, and EPI time to the center of the k-space is always math.ceil().
bvalue = bvalue_Dict["bvalue"] # Target b-value.
nbvals = bvalue_Dict["nbvals"] # number of b-values.
gscl = bvalue_Dict["gscl"] # gradient scaling.
n_rf90r = grads_times_Dict[
"n_rf90r"] # Half right duration of RF90 (integer).
n_rf180r = grads_times_Dict[
"n_rf180r"] # Half right duration of the RF180 (integer).
n_rf180l = grads_times_Dict[
"n_rf180l"] # Half left duration of the RF180 (integer).
gz_spoil = grads_times_Dict[
"gz_spoil"] # Spoil gradient to be placed around the RF180.
gz180 = grads_times_Dict["gz180"] # RF180 simultaneous gradient.
n_duration_center = grads_times_Dict[
"n_duration_center"] # Time needed to achieve the center of the k-space (integer).
seq = seq_sys_Dict["seq"] # Sequence
system = seq_sys_Dict["system"] # System
i_raster_time = seq_sys_Dict[
"i_raster_time"] # Manually inputed inverse raster time.
# Find minimum TE considering the readout times.
n_TE = math.ceil(40e-3 / seq.grad_raster_time)
n_delay_te2 = -1
while n_delay_te2 <= 0:
n_TE = n_TE + 2
n_tINV = math.floor(n_TE / 2)
n_delay_te2 = n_tINV - n_rf180r - n_duration_center
# Find minimum TE for the target b-value
bvalue_tmp = 0
while bvalue_tmp < np.max(bvalue):
n_TE = n_TE + 2
n_tINV = math.floor(n_TE / 2)
n_delay_te1 = n_tINV - n_rf90r - n_rf180l
n_delay_te2 = n_tINV - n_rf180r - n_duration_center
# Waveform Ramp time
n_gdiff_rt = math.ceil(system.max_grad / system.max_slew /
seq.grad_raster_time)
# Select the shortest available time
n_gdiff_delta = min(n_delay_te1, n_delay_te2)
n_gdiff_Delta = n_delay_te1 + 2 * math.ceil(
calc_duration(gz_spoil) / seq.grad_raster_time) + math.ceil(
calc_duration(gz180) / seq.grad_raster_time)
gdiff = make_trapezoid(channel='x',
system=system,
amplitude=system.max_grad,
duration=n_gdiff_delta / i_raster_time)
# delta only corresponds to the rectangle.
n_gdiff_delta = n_gdiff_delta - 2 * n_gdiff_rt
bv = calc_bval(system.max_grad, n_gdiff_delta / i_raster_time,
n_gdiff_Delta / i_raster_time,
n_gdiff_rt / i_raster_time)
bval = bv * 1e-6
# Show final TE and b-values:
print("TE:", round(n_TE / i_raster_time * 1e3, 2), "ms")
for bv in range(1, nbvals + 1):
print(
round(
calc_bval(system.max_grad * gscl[bv], n_gdiff_delta /
i_raster_time, n_gdiff_Delta / i_raster_time,
n_gdiff_rt / i_raster_time) * 1e-6, 2), "s/mm2")
return n_TE, bval, gdiff, n_delay_te1, n_delay_te2
def split_gradient(grad: SimpleNamespace,
system: Opts = Opts()) -> Tuple[SimpleNamespace, SimpleNamespace, SimpleNamespace]:
"""
Split gradient waveform `grad` into two gradient waveforms at the center.
Parameters
----------
grad : array_like
Gradient waveform to be split into two gradient waveforms.
system : Opts, optional, default=Opts()
System limits.
Returns
-------
grad1, grad2 : numpy.ndarray
Split gradient waveforms.
Raises
------
ValueError
If arbitrary gradients are passed.
If non-gradient event is passed.
"""
grad_raster_time = system.grad_raster_time
total_length = calc_duration(grad)
if grad.type == 'trap':
ch = grad.channel
grad.delay = round(grad.delay / grad_raster_time) * grad_raster_time
grad.rise_time = round(grad.rise_time / grad_raster_time) * grad_raster_time
grad.flat_time = round(grad.flat_time / grad_raster_time) * grad_raster_time
grad.fall_time = round(grad.fall_time / grad_raster_time) * grad_raster_time
times = [0, grad.rise_time]
amplitudes = [0, grad.amplitude]
ramp_up = make_extended_trapezoid(channel=ch, system=system, times=times, amplitudes=amplitudes,
skip_check=True)
ramp_up.delay = grad.delay
times = [0, grad.fall_time]
amplitudes = [grad.amplitude, 0]
ramp_down = make_extended_trapezoid(channel=ch, system=system, times=times, amplitudes=amplitudes,
skip_check=True)
ramp_down.delay = total_length - grad.fall_time
ramp_down.t = ramp_down.t * grad_raster_time
flat_top = SimpleNamespace()
flat_top.type = 'grad'
flat_top.channel = ch
flat_top.delay = grad.delay + grad.rise_time
flat_top.t = np.arange(step=grad_raster_time,
stop=ramp_down.delay - grad_raster_time - grad.delay - grad.rise_time)
flat_top.waveform = grad.amplitude * np.ones(len(flat_top.t))
flat_top.first = grad.amplitude
flat_top.last = grad.amplitude
return ramp_up, flat_top, ramp_down
elif grad.type == 'grad':
raise ValueError('Splitting of arbitrary gradients is not implemented yet.')
else:
raise ValueError('Splitting of unsupported event.')
rf_dead_time=100e-6,
adc_dead_time=10e-6)
# Fat saturation
if fatsat_enable:
b0 = 1.494
sat_ppm = -3.45
sat_freq = sat_ppm * 1e-6 * b0 * system.gamma
rf_fs, _, _ = make_gauss_pulse(flip_angle=110 * math.pi / 180,
system=system,
duration=8e-3,
bandwidth=abs(sat_freq),
freq_offset=sat_freq)
gz_fs = make_trapezoid(channel='z',
system=system,
delay=calc_duration(rf_fs),
area=1 / 1e-4)
# Create 90 degree slice selection pulse and gradient
rf, gz, _ = make_sinc_pulse(flip_angle=math.pi / 2,
system=system,
duration=3e-3,
slice_thickness=slice_thickness,
apodization=0.5,
time_bw_product=4)
# Refocusing pulse with spoiling gradients
rf180, gz180, _ = make_sinc_pulse(flip_angle=math.pi,
system=system,
duration=5e-3,
slice_thickness=slice_thickness,
def calculate_kspace(self, trajectory_delay=0):
c_excitation = 0
c_refocusing = 0
c_adc_samples = 0
for i in range(len(self.block_events)):
block = self.get_block(i + 1)
if hasattr(block, 'rf'):
if not hasattr(block.rf,
'use') or block.rf.use != 'refocusing':
c_excitation += 1
else:
c_refocusing += 1
if hasattr(block, 'adc'):
c_adc_samples += int(block.adc.num_samples)
t_excitation = np.zeros(c_excitation)
t_refocusing = np.zeros(c_refocusing)
k_time = np.zeros(c_adc_samples)
current_dur = 0
c_excitation = 0
c_refocusing = 0
k_counter = 0
traj_recon_delay = trajectory_delay
for i in range(len(self.block_events)):
block = self.get_block(i + 1)
if hasattr(block, 'rf'):
rf = block.rf
rf_center, _ = calc_rf_center(rf)
t = rf.delay + rf_center
if not hasattr(block.rf,
'use') or block.rf.use != 'refocusing':
t_excitation[c_excitation] = current_dur + t
c_excitation += 1
else:
t_refocusing[c_refocusing] = current_dur + t
c_refocusing += 1
if hasattr(block, 'adc'):
k_time[k_counter:k_counter + block.adc.num_samples] = np.arange(
block.adc.num_samples
) * block.adc.dwell + block.adc.delay + current_dur + traj_recon_delay
k_counter += block.adc.num_samples
current_dur += calc_duration(block)
gw = self.gradient_waveforms()
i_excitation = np.round(t_excitation / self.grad_raster_time)
i_refocusing = np.round(t_refocusing / self.grad_raster_time)
k_traj = np.zeros(gw.shape)
k = [0, 0, 0]
for i in range(gw.shape[1]):
k += gw[:, i] * self.grad_raster_time
k_traj[:, i] = k
if len(np.where(i_excitation == i + 1)[0]) >= 1:
k = 0
k_traj[:, i] = np.nan
if len(np.where(i_refocusing == i + 1)[0]) >= 1:
k = -k
k_traj_adc = []
for i in range(k_traj.shape[0]):
k_traj_adc.append(
np.interp(
k_time,
np.arange(1, k_traj.shape[1] + 1) * self.grad_raster_time,
k_traj[i]))
k_traj_adc = np.asarray(k_traj_adc)
t_adc = k_time
return k_traj_adc, k_traj, t_excitation, t_refocusing, t_adc
def gradient_waveforms(self) -> np.ndarray:
"""
Decompress the entire gradient waveform. Returns an array of shape `gradient_axesxtimepoints`.
`gradient_axes` is typically 3.
Returns
-------
grad_waveforms : numpy.ndarray
Decompressed gradient waveform.
"""
duration, num_blocks, _ = self.duration()
wave_length = math.ceil(duration / self.grad_raster_time)
grad_channels = 3
grad_waveforms = np.zeros((grad_channels, wave_length))
grad_channels = ['gx', 'gy', 'gz']
t0 = 0
t0_n = 0
for i in range(num_blocks):
block = self.get_block(i + 1)
for j in range(len(grad_channels)):
if hasattr(block, grad_channels[j]):
grad = getattr(block, grad_channels[j])
if grad.type == 'grad':
nt_start = round(
(grad.delay + grad.t[0]) / self.grad_raster_time)
waveform = grad.waveform
else:
nt_start = round(grad.delay / self.grad_raster_time)
if abs(grad.flat_time) > np.finfo(float).eps:
t = np.cumsum([
0, grad.rise_time, grad.flat_time,
grad.fall_time
])
trap_form = np.multiply([0, 1, 1, 0],
grad.amplitude)
else:
t = np.cumsum([0, grad.rise_time, grad.fall_time])
trap_form = np.multiply([0, 1, 0], grad.amplitude)
tn = math.floor(t[-1] / self.grad_raster_time)
t = np.append(t, t[-1] + self.grad_raster_time)
trap_form = np.append(trap_form, 0)
if abs(grad.amplitude) > np.finfo(float).eps:
waveform = points_to_waveform(
t, trap_form, self.grad_raster_time)
else:
waveform = np.zeros(tn + 1)
if waveform.size != np.sum(np.isfinite(waveform)):
raise Warning(
'Not all elements of the generated waveform are finite'
)
"""
Matlab dynamically resizes arrays during slice assignment operation if assignment is out of bounds
Numpy does not
Following lines are a workaround
"""
l1, l2 = int(t0_n + nt_start), int(t0_n + nt_start +
max(waveform.shape))
if l2 > grad_waveforms.shape[1]:
grad_waveforms.resize((len(grad_channels), l2))
grad_waveforms[j, l1:l2] = waveform
t0 += calc_duration(block)
t0_n = round(t0 / self.grad_raster_time)
return grad_waveforms
def align(
**kwargs: Union[SimpleNamespace, List[SimpleNamespace]]
) -> List[SimpleNamespace]:
"""
Aligns `SimpleNamespace` objects as per specified alignment options by setting delays of the pulse sequence events
within the block. All previously configured delays within objects are taken into account during calculating of the
block duration but then reset according to the selected alignment. Possible values for align_spec are 'left',
'center', 'right'.
Parameters
----------
args : dict[str, list[SimpleNamespace]
Dictionary mapping of alignment options and `SimpleNamespace` objects.
Template: alignment_spec1=SimpleNamespace, alignment_spec2=[SimpleNamespace, ...], ...
Alignment spec must be one of `left`, `center` or `right`.
Returns
-------
objects : list
List of aligned `SimpleNamespace` objects.
Raises
------
ValueError
If first parameter is not of type `str`.
If invalid alignment spec is passed. Must be one of `left`, `center` or `right`.
"""
alignment_specs = list(kwargs.keys())
if not isinstance(alignment_specs[0], str):
raise ValueError(
f'First parameter must be of type str. Passed: {type(alignment_specs[0])}'
)
alignment_options = ['left', 'center', 'right']
if np.any(
[align_opt not in alignment_options for align_opt in alignment_specs]):
raise ValueError('Invalid alignment spec.')
alignments = []
objects = []
for a in alignment_specs:
objects_to_align = kwargs[a]
a = alignment_options.index(a)
if isinstance(objects_to_align, (list, np.ndarray, tuple)):
alignments.extend([a] * len(objects_to_align))
objects.extend(objects_to_align)
elif isinstance(objects_to_align, SimpleNamespace):
alignments.extend([a])
objects.append(objects_to_align)
dur = calc_duration(*objects)
for i in range(len(objects)):
if alignments[i] == 0:
objects[i].delay = 0
elif alignments[i] == 1:
objects[i].delay = (dur - calc_duration(objects[i])) / 2
elif alignments[i] == 2:
objects[i].delay = dur - calc_duration(
objects[i]) + objects[i].delay
return objects
def bssfp_readout(seq, system, fov=200e-3, Nstartup=11, Ny=128):
"""
Creates a Balanced steady-state free precession (bSSFP) sequence and adds
to seq object
Parameters
----------
seq: object
Sequence object
system : Opts
System limits.
fov : float, optional
Field-of-view [m]. Default is 0.2 m.
Nstartup : float, optional
Number of start-up RF pulses. Default is 11
Ny : float, optional
Number of phase encoding lines. Default is 128.
Returns
-------
seq : SimpleNamespace
Seq object with bSSFP readout
TR : float
Repetition time
Ny : float
Final number of phase encoding lines.
"""
# Sequence Parameters
enc = 'xyz'
Nx = 128
thk = 6e-3
fa = 35 # [deg]
Nramp = Nstartup
# ADC duration (controls TR/TE)
adc_dur = 2560 / 2 # [us]
rf_dur = 490 # [us]
rf_apo = 0.5
rf_bwt = 1.5
#############################################################################
# Create slice selection pulse and gradient
rf, g_ss, __ = make_sinc_pulse(
flip_angle=fa * pi / 180,
system=system,
duration=rf_dur * 1e-6,
slice_thickness=thk,
apodization=rf_apo,
time_bw_product=rf_bwt
) # for next pyPulseq version add:, return_gz=True)
g_ss.channel = enc[2]
# Slice refocusing
g_ss_reph = make_trapezoid(channel=enc[2],
system=system,
area=-g_ss.area / 2,
duration=0.00017 * 2)
#############################################################################
rf.delay = calc_duration(g_ss) - calc_duration(rf) + rf.delay
#############################################################################
# Readout gradient and ADC
delta_k = 1 / fov
kWidth = Nx * delta_k
# Readout and ADC
g_ro = make_trapezoid(channel=enc[0],
system=system,
flat_area=kWidth,
flat_time=(adc_dur) * 1e-6)
adc = make_adc(num_samples=Nx,
duration=g_ro.flat_time,
delay=g_ro.rise_time)
# Readout rewinder
g_ro_pre = make_trapezoid(channel=enc[0],
system=system,
area=-g_ro.area / 2)
phaseAreas_tmp = np.arange(0, Ny, 1).tolist()
phaseAreas = np.dot(np.subtract(phaseAreas_tmp, Ny / 2), delta_k)
gs8_times = [
0, g_ro.fall_time, g_ro.fall_time + g_ro_pre.rise_time,
g_ro.fall_time + g_ro_pre.rise_time + g_ro_pre.flat_time,
g_ro.fall_time + g_ro_pre.rise_time + g_ro_pre.flat_time +
g_ro_pre.fall_time
]
gs8_amp = [g_ro.amplitude, 0, g_ro_pre.amplitude, g_ro_pre.amplitude, 0]
gx_2 = make_extended_trapezoid(channel=enc[0],
times=gs8_times,
amplitudes=gs8_amp)
# Calculate phase encoding gradient duration
pe_dur = calc_duration(gx_2)
gx_allExt_times = [
0, g_ro_pre.rise_time, g_ro_pre.rise_time + g_ro_pre.flat_time,
g_ro_pre.rise_time + g_ro_pre.flat_time + g_ro_pre.fall_time,
g_ro_pre.rise_time + g_ro_pre.flat_time + g_ro_pre.fall_time +
g_ro.rise_time, g_ro_pre.rise_time + g_ro_pre.flat_time +
g_ro_pre.fall_time + g_ro.rise_time + g_ro.flat_time + 1e-5,
g_ro_pre.rise_time + g_ro_pre.flat_time + g_ro_pre.fall_time +
g_ro.rise_time + g_ro.flat_time + 1e-5 + g_ro.fall_time,
g_ro_pre.rise_time + g_ro_pre.flat_time + g_ro_pre.fall_time +
g_ro.rise_time + g_ro.flat_time + 1e-5 + g_ro.fall_time +
g_ro_pre.rise_time, g_ro_pre.rise_time + g_ro_pre.flat_time +
g_ro_pre.fall_time + g_ro.rise_time + g_ro.flat_time + 1e-5 +
g_ro.fall_time + g_ro_pre.rise_time + g_ro_pre.flat_time,
g_ro_pre.rise_time + g_ro_pre.flat_time + g_ro_pre.fall_time +
g_ro.rise_time + g_ro.flat_time + 1e-5 + g_ro.fall_time +
g_ro_pre.rise_time + g_ro_pre.flat_time + g_ro_pre.fall_time
]
gx_allExt_amp = [
0, g_ro_pre.amplitude, g_ro_pre.amplitude, 0, g_ro.amplitude,
g_ro.amplitude, 0, g_ro_pre.amplitude, g_ro_pre.amplitude, 0
]
gx_all = make_extended_trapezoid(channel=enc[0],
times=gx_allExt_times,
amplitudes=gx_allExt_amp)
#############################################################################
gzrep_times = [
0, g_ss_reph.rise_time, g_ss_reph.rise_time + g_ss_reph.flat_time,
g_ss_reph.rise_time + g_ss_reph.flat_time + g_ss_reph.fall_time,
g_ss_reph.rise_time + g_ss_reph.flat_time + g_ss_reph.fall_time +
calc_duration(g_ro) + 2 * calc_duration(g_ro_pre) -
2 * calc_duration(g_ss_reph) + 1e-5, g_ss_reph.rise_time +
g_ss_reph.flat_time + g_ss_reph.fall_time + calc_duration(g_ro) +
2 * calc_duration(g_ro_pre) - 2 * calc_duration(g_ss_reph) + 1e-5 +
g_ss_reph.rise_time, g_ss_reph.rise_time + g_ss_reph.flat_time +
g_ss_reph.fall_time + calc_duration(g_ro) +
2 * calc_duration(g_ro_pre) - 2 * calc_duration(g_ss_reph) + 1e-5 +
g_ss_reph.rise_time + g_ss_reph.flat_time, g_ss_reph.rise_time +
g_ss_reph.flat_time + g_ss_reph.fall_time + calc_duration(g_ro) +
2 * calc_duration(g_ro_pre) - 2 * calc_duration(g_ss_reph) + 1e-5 +
g_ss_reph.rise_time + g_ss_reph.flat_time + g_ss_reph.fall_time
]
gzrep_amp = [
0, g_ss_reph.amplitude, g_ss_reph.amplitude, 0, 0, g_ss_reph.amplitude,
g_ss_reph.amplitude, 0
]
gzrep_all = make_extended_trapezoid(channel=enc[2],
times=gzrep_times,
amplitudes=gzrep_amp)
adc.delay = g_ro_pre.rise_time + g_ro_pre.flat_time + g_ro_pre.fall_time + g_ro.rise_time + 0.5e-5
# finish timing calculation
TR = calc_duration(g_ss) + calc_duration(gx_all)
TE = TR / 2
ni_acqu_pattern = np.arange(1, Ny + 1, 1)
Ny_aq = len(ni_acqu_pattern)
print('Acquisition window is: %3.2f ms' % (TR * Ny_aq * 1e3))
rf05 = rf
rf_waveform = rf.signal
############################################################################
# Start-up RF pulses
# (ramp-up of Nramp)
for nRamp in np.arange(1, Nramp + 1, 1):
if np.mod(nRamp, 2):
rf.phase_offset = 0
adc.phase_offset = 0
else:
rf.phase_offset = -pi
adc.phase_offset = -pi
rf05.signal = np.divide(nRamp, Nramp) * rf_waveform
gyPre_2 = make_trapezoid('y',
area=phaseAreas[0],
duration=pe_dur,
system=system)
gyPre_1 = make_trapezoid('y',
area=-phaseAreas[0],
duration=pe_dur,
system=system)
gyPre_times = [
0, gyPre_2.rise_time, gyPre_2.rise_time + gyPre_2.flat_time,
gyPre_2.rise_time + gyPre_2.flat_time + gyPre_2.fall_time,
gyPre_2.rise_time + gyPre_2.flat_time + gyPre_2.fall_time +
g_ro.flat_time + 1e-5, gyPre_2.rise_time + gyPre_2.flat_time +
gyPre_2.fall_time + g_ro.flat_time + 1e-5 + gyPre_1.rise_time,
gyPre_2.rise_time + gyPre_2.flat_time + gyPre_2.fall_time +
g_ro.flat_time + 1e-5 + gyPre_1.rise_time + gyPre_1.flat_time,
gyPre_2.rise_time + gyPre_2.flat_time + gyPre_2.fall_time +
g_ro.flat_time + 1e-5 + gyPre_1.rise_time + gyPre_1.flat_time +
gyPre_1.fall_time
]
gyPre_amp = [
0, gyPre_2.amplitude, gyPre_2.amplitude, 0, 0, gyPre_1.amplitude,
gyPre_1.amplitude, 0
]
gyPre_all = make_extended_trapezoid(channel=enc[1],
times=gyPre_times,
amplitudes=gyPre_amp)
if nRamp == 1:
seq.add_block(rf05, g_ss)
seq.add_block(gx_all, gyPre_all, gzrep_all)
else:
seq.add_block(rf05, g_ss)
seq.add_block(gx_all, gyPre_all, gzrep_all)
############################################################################
############################################################################
# Actual Readout iterate number of phase encoding steps Ny
for i in np.arange(1, Ny + 1, 1):
# *******************************
# Phase cycling
if np.mod(i + Nramp, 2):
rf.phase_offset = 0
adc.phase_offset = 0
else:
rf.phase_offset = -pi
adc.phase_offset = -pi
# *******************************
# ***************************************************************************************
# Create phase encoding gradients
if np.mod(Nramp, 2):
gyPre_2 = make_trapezoid('y',
area=phaseAreas[i - 1],
duration=pe_dur,
system=system)
if i > 1:
gyPre_1 = make_trapezoid(
'y',
area=-phaseAreas[np.mod(i + Ny - 2, Ny)],
duration=pe_dur,
system=system)
else:
gyPre_1 = make_trapezoid(
'y',
area=-phaseAreas[np.mod(i + Ny - 1, Ny)],
duration=pe_dur,
system=system)
else:
gyPre_2 = make_trapezoid('y',
area=phaseAreas[i],
duration=pe_dur,
system=system)
if i > 1:
gyPre_1 = make_trapezoid(
'y',
area=-phaseAreas[np.mod(i + Ny - 2, Ny)],
duration=pe_dur,
system=system)
else:
gyPre_1 = make_trapezoid('y',
area=phaseAreas[np.mod(
i + Ny - 2, Ny)],
duration=pe_dur,
system=system)
gyPre_times = [
0, gyPre_2.rise_time, gyPre_2.rise_time + gyPre_2.flat_time,
gyPre_2.rise_time + gyPre_2.flat_time + gyPre_2.fall_time,
gyPre_2.rise_time + gyPre_2.flat_time + gyPre_2.fall_time +
g_ro.flat_time + 1e-5, gyPre_2.rise_time + gyPre_2.flat_time +
gyPre_2.fall_time + g_ro.flat_time + 1e-5 + gyPre_2.rise_time,
gyPre_2.rise_time + gyPre_2.flat_time + gyPre_2.fall_time +
g_ro.flat_time + 1e-5 + gyPre_2.rise_time + gyPre_2.flat_time,
gyPre_2.rise_time + gyPre_2.flat_time + gyPre_2.fall_time +
g_ro.flat_time + 1e-5 + gyPre_2.rise_time + gyPre_2.flat_time +
gyPre_2.fall_time
]
gyPre_amp = [
0, gyPre_2.amplitude, gyPre_2.amplitude, 0, 0, -gyPre_2.amplitude,
-gyPre_2.amplitude, 0
]
# Verify if phase encoding gradient amplitude is not zero
try:
gyPre_all = make_extended_trapezoid(channel=enc[1],
times=gyPre_times,
amplitudes=gyPre_amp)
flag_zerogy = 0
except:
gyPre_times = [
0, pe_dur, pe_dur + g_ro.flat_time + 1e-5,
pe_dur + g_ro.flat_time + 1e-5 + pe_dur
]
gyPre_amp = [0, gyPre_2.amplitude, 0, 0]
flag_zerogy = 1
# ***************************************************************************************
# add RF and Slice selecitve gradient
seq.add_block(rf, g_ss)
# add Phase and frequency encoding gradients and ADC
if flag_zerogy == 1:
seq.add_block(gx_all, gzrep_all, adc)
else:
seq.add_block(gx_all, gyPre_all, gzrep_all, adc)
return seq, TR, Ny
# Scheme 5(3)3
NImage_perInv = [5, 3]
Nrecover_btwInv = [3]
TI_Vector = [
0.100, 0.100 + 0.08
] # [s] TI1 minimum TI of 100 ms, TI increment of 80 sec, Messroghli 2007
TI_Vector_real = np.zeros(
np.shape(TI_Vector)) # Initialize vector for final TI
N_inversion = np.shape(NImage_perInv) # Number of inversions
print('Number of inversion:', N_inversion[0])
print('Number of images per inversion:', NImage_perInv)
print('Number of recovery heart beats between inversion:', Nrecover_btwInv)
# Minimum Inversion Time
TI_min_attainable = (Nstartup + Ny / 2) * TR + calc_duration(gzSpoil_INV)
print('Minimum TI: %3.0f' % (TI_min_attainable * 1e3), 'ms')
# Iterate between Inversions
for nInv in np.arange(N_inversion[0]):
print('----------- Inversion %3.0f -----------' % (nInv + 1))
if nInv > 0:
for nRec in np.arange(Nrecover_btwInv[nInv - 1]):
seq.add_block(trig_BetweenInversion)
# ***************************************************************************************************
# Verify minimum possible TI
# (nearest to TI_Vector[0], i.e. 100 ms)
try:
assert (all(TI_Vector[nInv] >= TI_min_attainable)
) # verify if TI_vector value is possbile
def make_pulseq_se_oblique(fov, n, thk, fa, tr, te, enc='xyz', slice_locs=None, write=False):
"""Makes a Spin Echo (SE) sequence in any plane
2D oblique multi-slice Spin-Echo pulse sequence with Cartesian encoding
Oblique means that each of slice-selection, phase encoding, and frequency encoding
can point in any specified direction
Parameters
----------
fov : array_like
Isotropic field-of-view, or length-2 list [fov_readout, fov_phase], in meters
n : array_like
Isotropic matrix size, or length-2 list [n_readout, n_phase]
thk : float
Slice thickness in meters
fa : float
Flip angle in degrees
tr : float
Repetition time in seconds
te : float
Echo time in seconds
enc : str or array_like, optional
Spatial encoding directions
1st - readout; 2nd - phase encoding; 3rd - slice select
- Use str with any permutation of x, y, and z to obtain orthogonal slices
e.g. The default 'xyz' means axial(z) slice with readout in x and phase encoding in y
- Use list to indicate vectors in the encoding directions for oblique slices
They should be perpendicular to each other, but not necessarily unit vectors
e.g. [(2,1,0),(-1,2,0),(0,0,1)] rotates the two in-plane encoding directions for an axial slice
slice_locs : array_like, optional
Slice locations from isocenter in meters
Default is None which means a single slice at the center
write : bool, optional
Whether to write seq into file; default is False
Returns
-------
seq : Sequence
Pulse sequence as a Pulseq object
"""
# System options
kwargs_for_opts = {'max_grad': 32, 'grad_unit': 'mT/m',
'max_slew': 130, 'slew_unit': 'T/m/s', 'rf_ring_down_time': 30e-6,
'rf_dead_time': 100e-6, 'adc_dead_time': 20e-6}
system = Opts(kwargs_for_opts)
seq = Sequence(system)
# Sequence parameters
ug_fe, ug_pe, ug_ss = parse_enc(enc)
Nf, Np = (n, n) if isinstance(n, int) else (n[0], n[1])
delta_k_ro, delta_k_pe = (1 / fov, 1 / fov) if isinstance(fov, float) else (1 / fov[0], 1 / fov[1])
kWidth_ro = Nf * delta_k_ro
TE, TR = te, tr
# Non-180 pulse
flip1 = fa * pi / 180
kwargs_for_sinc = {"flip_angle": flip1, "system": system, "duration": 2e-3, "slice_thickness": thk,
"apodization": 0.5, "time_bw_product": 4}
rf, g_ss = make_sinc_pulse(kwargs_for_sinc, 2)
g_ss_x, g_ss_y, g_ss_z = make_oblique_gradients(g_ss, ug_ss)
# 180 pulse
flip2 = 180 * pi / 180
kwargs_for_sinc = {"flip_angle": flip2, "system": system, "duration": 2e-3, "slice_thickness": thk,
"apodization": 0.5, "time_bw_product": 4}
rf180, g_ss180 = make_sinc_pulse(kwargs_for_sinc, 2)
g_ss180_x, g_ss180_y, g_ss180_z = make_oblique_gradients(g_ss180, ug_ss)
# Readout gradient & ADC
readoutTime = 6.4e-3
kwargs_for_g_ro = {"channel": 'x', "system": system, "flat_area": kWidth_ro, "flat_time": readoutTime}
g_ro = make_trapezoid(kwargs_for_g_ro)
g_ro_x, g_ro_y, g_ro_z = make_oblique_gradients(g_ro, ug_fe)
kwargs_for_adc = {"num_samples": Nf, "system": system, "duration": g_ro.flat_time, "delay": g_ro.rise_time}
adc = makeadc(kwargs_for_adc)
# RO rewinder gradient
kwargs_for_g_ro_pre = {"channel": 'x', "system": system, "area": g_ro.area / 2,
"duration": 2e-3}
g_ro_pre = make_trapezoid(kwargs_for_g_ro_pre)
g_ro_pre_x, g_ro_pre_y, g_ro_pre_z = make_oblique_gradients(g_ro_pre, ug_fe)
# Slice refocusing gradient
kwargs_for_g_ss_reph = {"channel": 'z', "system": system, "area": -g_ss.area / 2, "duration": 2e-3}
g_ss_reph = make_trapezoid(kwargs_for_g_ss_reph)
g_ss_reph_x, g_ss_reph_y, g_ss_reph_z = make_oblique_gradients(g_ss_reph, ug_ss)
# Delays
delayTE1 = (TE - 2 * max(calc_duration(g_ss_reph), calc_duration(g_ro_pre)) - calc_duration(g_ss) - calc_duration(
g_ss180)) / 2
delayTE2 = (TE - calc_duration(g_ro) - calc_duration(g_ss180)) / 2
delayTE3 = TR - TE - (calc_duration(g_ss) + calc_duration(g_ro)) / 2
delay1 = make_delay(delayTE1)
delay2 = make_delay(delayTE2)
delay3 = make_delay(delayTE3)
# Construct sequence
if slice_locs is None:
locs = [0]
else:
locs = slice_locs
for u in range(len(locs)):
rf180.freq_offset = g_ss180.amplitude * locs[u]
rf.freq_offset = g_ss.amplitude * locs[u]
for i in range(Np):
seq.add_block(rf, g_ss_x, g_ss_y, g_ss_z) # 90-deg pulse
kwargs_for_g_pe = {"channel": 'y', "system": system, "area": -(Np / 2 - i) * delta_k_pe, "duration": 2e-3}
g_pe = make_trapezoid(kwargs_for_g_pe) # Phase encoding gradient
g_pe_x, g_pe_y, g_pe_z = make_oblique_gradients(g_pe, ug_pe)
pre_grads_list = [g_ro_pre_x, g_ro_pre_y, g_ro_pre_z,
g_ss_reph_x, g_ss_reph_y, g_ss_reph_z,
g_pe_x, g_pe_y, g_pe_z]
gtx, gty, gtz = combine_trap_grad_xyz(pre_grads_list, system, 2e-3)
seq.add_block(gtx, gty, gtz) # Add a combination of ro rewinder, phase encoding, and slice refocusing
seq.add_block(delay1) # Delay 1: until 180-deg pulse
seq.add_block(rf180, g_ss180_x, g_ss180_y, g_ss180_z) # 180 deg pulse for SE
seq.add_block(delay2) # Delay 2: until readout
seq.add_block(g_ro_x, g_ro_y, g_ro_z, adc) # Readout!
seq.add_block(delay3) # Delay 3: until next inversion pulse
if write:
seq.write(
"se_fov{:.0f}mm_Nf{:d}_Np{:d}_TE{:.0f}ms_TR{:.0f}ms.seq".format(fov * 1000, Nf, Np, TE * 1000, TR * 1000))
print('Spin echo sequence (oblique) constructed')
return seq
def plot(self, time_range=(0, np.inf)):
"""
Show Matplotlib plot of all Events in the Sequence object.
Parameters
----------
time_range : List
Time range (x-axis limits) for plot to be shown. Default is 0 to infinity (entire plot shown).
"""
fig1, fig2 = plt.figure(1), plt.figure(2)
f11, f12, f13 = fig1.add_subplot(311), fig1.add_subplot(
312), fig1.add_subplot(313)
f2 = [
fig2.add_subplot(311),
fig2.add_subplot(312),
fig2.add_subplot(313)
]
t0 = 0
for iB in range(1, len(self.block_events) + 1):
block = self.get_block(iB)
is_valid = time_range[0] <= t0 <= time_range[1]
if is_valid:
if block is not None:
if 'adc' in block:
adc = block['adc']
t = adc.delay + [
(x * adc.dwell)
for x in range(0, int(adc.num_samples))
]
f11.plot((t0 + t), np.zeros(len(t)))
if 'rf' in block:
rf = block['rf']
t = rf.t
f12.plot(np.squeeze(t0 + t), abs(rf.signal))
f13.plot(np.squeeze(t0 + t), np.angle(rf.signal))
grad_channels = ['gx', 'gy', 'gz']
for x in range(0, len(grad_channels)):
if grad_channels[x] in block:
grad = block[grad_channels[x]]
if grad.type == 'grad':
t = grad.t
waveform = 1e-3 * grad.waveform
else:
t = np.cumsum([
0, grad.rise_time, grad.flat_time,
grad.fall_time
])
waveform = [
1e-3 * grad.amplitude * x
for x in [0, 1, 1, 0]
]
f2[x].plot(np.squeeze(t0 + t), waveform)
t0 += calc_duration(block)
f11.set_ylabel('adc')
f12.set_ylabel('rf mag hz')
f13.set_ylabel('rf phase rad')
[f2[x].set_ylabel(grad_channels[x]) for x in range(3)]
# Setting display limits
disp_range = [time_range[0], min(t0, time_range[1])]
f11.set_xlim(disp_range)
f12.set_xlim(disp_range)
f13.set_xlim(disp_range)
[x.set_xlim(disp_range) for x in f2]
plt.show()
def make_pulseq_epi_oblique(fov, n, thk, fa, tr, te, enc='xyz', slice_locs=None, echo_type="se", n_shots=1,
seg_type='blocked', write=False):
"""Makes an Echo Planar Imaging (EPI) sequence in any plane
2D oblique multi-slice EPI pulse sequence with Cartesian encoding
Oblique means that each of slice-selection, phase encoding, and frequency encoding
can point in any specified direction
Parameters
----------
fov : array_like
Isotropic field-of-view, or length-2 list [fov_readout, fov_phase], in meters
n : array_like
Isotropic matrix size, or length-2 list [n_readout, n_phase]
thk : float
Slice thickness in meters
fa : float
Flip angle in degrees
tr : float
Repetition time in seconds
te : float
Echo time in seconds
enc : str or array_like, optional
Spatial encoding directions
1st - readout; 2nd - phase encoding; 3rd - slice select
- Use str with any permutation of x, y, and z to obtain orthogonal slices
e.g. The default 'xyz' means axial(z) slice with readout in x and phase encoding in y
- Use list to indicate vectors in the encoding directions for oblique slices
They should be perpendicular to each other, but not necessarily unit vectors
e.g. [(2,1,0),(-1,2,0),(0,0,1)] rotates the two in-plane encoding directions for an axial slice
slice_locs : array_like, optional
Slice locations from isocenter in meters
Default is None which means a single slice at the center
echo_type : str, optional {'se','gre'}
Type of echo generated
se (default) - spin echo (an 180 deg pulse is used)
gre - gradient echo
n_shots : int, optional
Number of shots used to encode each slicel; default is 1
seg_type : str, optional {'blocked','interleaved'}
Method to divide up k-space in the case of n_shots > 1; default is 'blocked'
'blocked' - each shot covers a rectangle, with no overlap between shots
'interleaved' - each shot samples the full k-space but with wider phase steps
write : bool, optional
Whether to write seq into file; default is False
Returns
-------
seq : Sequence
Pulse sequence as a Pulseq object
ro_dirs : numpy.ndarray
List of 0s and 1s indicating direction of readout
0 - left to right
1 - right to left (needs to be reversed at recon)
ro_order : numpy.ndarray
Order in which to re-arrange the readout lines
It is [] for blocked acquisition (retain original order)
"""
# Multi-slice, multi-shot (>=1)
# TE is set to be where the trajectory crosses the center of k-space
# System options
kwargs_for_opts = {'max_grad': 32, 'grad_unit': 'mT/m',
'max_slew': 130, 'slew_unit': 'T/m/s', 'rf_ring_down_time': 30e-6,
'rf_dead_time': 100e-6, 'adc_dead_time': 20e-6}
system = Opts(kwargs_for_opts)
seq = Sequence(system)
ug_fe, ug_pe, ug_ss = parse_enc(enc)
# Sequence parameters
Nf, Np = (n, n) if isinstance(n, int) else (n[0], n[1])
delta_k_ro, delta_k_pe = (1 / fov, 1 / fov) if isinstance(fov, float) else (1 / fov[0], 1 / fov[1])
kWidth_ro = Nf * delta_k_ro
TE, TR = te, tr
flip = fa * pi / 180
# RF Pulse (first)
kwargs_for_sinc = {"flip_angle": flip, "system": system, "duration": 2.5e-3, "slice_thickness": thk,
"apodization": 0.5, "time_bw_product": 4}
rf, g_ss = make_sinc_pulse(kwargs_for_sinc, 2)
g_ss_x, g_ss_y, g_ss_z = make_oblique_gradients(g_ss, ug_ss)
# Readout gradients
# readoutTime = Nf * 4e-6
dwell = 1e-5
readoutTime = Nf * dwell
kwargs_for_g_ro = {"channel": 'x', "system": system, "flat_area": kWidth_ro, "flat_time": readoutTime}
g_ro_pos = make_trapezoid(kwargs_for_g_ro)
g_ro_pos_x, g_ro_pos_y, g_ro_pos_z = make_oblique_gradients(g_ro_pos, ug_fe)
g_ro_neg = copy.deepcopy(g_ro_pos)
modify_gradient(g_ro_neg, scale=-1)
g_ro_neg_x, g_ro_neg_y, g_ro_neg_z = make_oblique_gradients(g_ro_neg, ug_fe)
kwargs_for_adc = {"num_samples": Nf, "system": system, "duration": g_ro_pos.flat_time,
"delay": g_ro_pos.rise_time + dwell / 2}
adc = makeadc(kwargs_for_adc)
pre_time = 8e-4
# 180 deg pulse for SE
if echo_type == "se":
# RF Pulse (180 deg for SE)
flip180 = 180 * pi / 180
kwargs_for_sinc = {"flip_angle": flip180, "system": system, "duration": 2.5e-3, "slice_thickness": thk,
"apodization": 0.5, "time_bw_product": 4}
rf180, g_ss180 = make_sinc_pulse(kwargs_for_sinc, 2)
# Slice-select direction spoilers
kwargs_for_g_ss_spoil = {"channel": 'z', "system": system, "area": g_ss.area * 2, "duration": 3 * pre_time}
g_ss_spoil = make_trapezoid(kwargs_for_g_ss_spoil)
##
modify_gradient(g_ss_spoil, 0)
##
g_ss_spoil_x, g_ss_spoil_y, g_ss_spoil_z = make_oblique_gradients(g_ss_spoil, ug_ss)
# Readout rewinder
ro_pre_area = g_ro_neg.area / 2 if echo_type == 'gre' else g_ro_pos.area / 2
kwargs_for_g_ro_pre = {"channel": 'x', "system": system, "area": ro_pre_area, "duration": pre_time}
g_ro_pre = make_trapezoid(kwargs_for_g_ro_pre)
g_ro_pre_x, g_ro_pre_y, g_ro_pre_z = make_oblique_gradients(g_ro_pre, ug_fe)
# Slice-selective rephasing
kwargs_for_g_ss_reph = {"channel": 'z', "system": system, "area": -g_ss.area / 2, "duration": pre_time}
g_ss_reph = make_trapezoid(kwargs_for_g_ss_reph)
g_ss_reph_x, g_ss_reph_y, g_ss_reph_z = make_oblique_gradients(g_ss_reph, ug_ss)
# Phase encode rewinder
if echo_type == 'gre':
pe_max_area = (Np / 2) * delta_k_pe
elif echo_type == 'se':
pe_max_area = -(Np / 2) * delta_k_pe
kwargs_for_g_pe_max = {"channel": 'y', "system": system, "area": pe_max_area, "duration": pre_time}
g_pe_max = make_trapezoid(kwargs_for_g_pe_max)
# Phase encoding blips
dur = ceil(2 * sqrt(delta_k_pe / system.max_slew) / 10e-6) * 10e-6
kwargs_for_g_blip = {"channel": 'y', "system": system, "area": delta_k_pe, "duration": dur}
g_blip = make_trapezoid(kwargs_for_g_blip)
# Delays
duration_to_center = (Np / 2) * calc_duration(g_ro_pos) + (Np - 1) / 2 * calc_duration(g_blip) # why?
if echo_type == 'se':
delayTE1 = TE / 2 - calc_duration(g_ss) / 2 - pre_time - calc_duration(g_ss_spoil) - calc_duration(rf180) / 2
delayTE2 = TE / 2 - calc_duration(rf180) / 2 - calc_duration(g_ss_spoil) - duration_to_center
delay1 = make_delay(delayTE1)
delay2 = make_delay(delayTE2)
elif echo_type == 'gre':
delayTE = TE - calc_duration(g_ss) / 2 - pre_time - duration_to_center
delay12 = make_delay(delayTE)
delayTR = TR - TE - calc_duration(rf) / 2 - duration_to_center
delay3 = make_delay(delayTR) # This might be different for each rep though. Fix later
#####################################################################################################
# Multi-shot calculations
ro_dirs = []
ro_order = []
# Find number of lines in each block
if seg_type == 'blocked':
# Number of lines in each full readout block
nl = ceil(Np / n_shots)
# Number of k-space lines per readout
if Np % nl == 0:
nlines_list = nl * np.ones(n_shots)
else:
nlines_list = nl * np.ones(n_shots - 1)
nlines_list = np.append(nlines_list, Np % nl)
pe_scales = 2 * np.append([0], np.cumsum(nlines_list)[:-1]) / Np - 1
g_blip_x, g_blip_y, g_blip_z = make_oblique_gradients(g_blip, ug_pe)
for nlines in nlines_list:
ro_dirs = np.append(ro_dirs, ((-1) ** (np.arange(0, nlines) + 1) + 1) / 2)
elif seg_type == 'interleaved':
# Minimum number of lines per readout
nb = floor(Np / n_shots)
# Number of k-space lines per readout
nlines_list = np.ones(n_shots) * nb
nlines_list[:Np % n_shots] += 1
# Phase encoding scales (starts from -1; i.e. bottom left combined with pre-readout)
pe_scales = 2 * np.arange(0, (Np - n_shots) / Np, 1 / Np)[0:n_shots] - 1
print(pe_scales)
# Larger blips
modify_gradient(g_blip, scale=n_shots)
g_blip_x, g_blip_y, g_blip_z = make_oblique_gradients(g_blip, ug_pe)
# ro_order = np.reshape(np.reshape(np.arange(0,Np),(),order='F'),(0,Np))
ro_order = np.zeros((nb + 1, n_shots))
ro_inds = np.arange(Np)
# Readout order for recon
for k in range(n_shots):
cs = int(nlines_list[k])
ro_order[:cs, k] = ro_inds[:cs]
ro_inds = np.delete(ro_inds, range(cs))
ro_order = ro_order.flatten()[:Np].astype(int)
print(ro_order)
# Readout directions in original (interleaved) order
for nlines in nlines_list:
ro_dirs = np.append(ro_dirs, ((-1) ** (np.arange(0, nlines) + 1) + 1) / 2)
#####################################################################################################
# Add blocks
for u in range(len(slice_locs)): # For each slice
# Offset rf
rf.freq_offset = g_ss.amplitude * slice_locs[u]
for v in range(n_shots):
# Find init. phase encode
g_pe = copy.deepcopy(g_pe_max)
modify_gradient(g_pe, pe_scales[v])
g_pe_x, g_pe_y, g_pe_z = make_oblique_gradients(g_pe, ug_pe)
# First RF
seq.add_block(rf, g_ss_x, g_ss_y, g_ss_z)
# Pre-winder gradients
pre_grads_list = [g_ro_pre_x, g_ro_pre_y, g_ro_pre_z,
g_pe_x, g_pe_y, g_pe_z,
g_ss_reph_x, g_ss_reph_y, g_ss_reph_z]
gtx, gty, gtz = combine_trap_grad_xyz(pre_grads_list, system, pre_time)
seq.add_block(gtx, gty, gtz)
# 180 deg pulse and spoilers, only for Spin Echo
if echo_type == 'se':
# First delay
seq.add_block(delay1)
# Second RF : 180 deg with spoilers on both sides
seq.add_block(g_ss_spoil_x, g_ss_spoil_y, g_ss_spoil_z) # why?
seq.add_block(rf180)
seq.add_block(g_ss_spoil_x, g_ss_spoil_y, g_ss_spoil_z)
# Delay between rf180 and beginning of readout
seq.add_block(delay2)
# For gradient echo it's just a delay
elif echo_type == 'gre':
seq.add_block(delay12)
# EPI readout with blips
for i in range(int(nlines_list[v])):
if i % 2 == 0:
seq.add_block(g_ro_pos_x, g_ro_pos_y, g_ro_pos_z, adc) # ro line in the positive direction
else:
seq.add_block(g_ro_neg_x, g_ro_neg_y, g_ro_neg_z, adc) # ro line backwards
seq.add_block(g_blip_x, g_blip_y, g_blip_z) # blip
seq.add_block(delay3)
# Display 1 TR
# seq.plot(time_range=(0, TR))
if write:
seq.write("epi_{}_FOVf{:.0f}mm_FOVp{:.0f}mm_Nf{:d}_Np{:d}_TE{:.0f}ms_TR{:.0f}ms_{:d}shots.seq" \
.format(echo_type, fov[0] * 1000, fov[1] * 1000, Nf, Np, TE * 1000, TR * 1000, n_shots))
print('EPI sequence (oblique) constructed')
return seq, ro_dirs, ro_order
}
gz_reph = make_trapezoid(kwargs_for_gz_reph)
flip = 180 * pi / 180
kwargs_for_sinc = {
"flip_angle": flip,
"system": system,
"duration": 2e-3,
"slice_thickness": slice_thickness,
"apodization": 0.5,
"time_bw_product": 4
}
rf180, gz180 = make_sinc_pulse(kwargs_for_sinc, 2)
TE, TR = 100e-3, 4408e-3
delayTE1 = TE / 2 - calc_duration(gz_reph) - calc_duration(
rf) - calc_duration(rf180) / 2
delayTE2 = TE / 2 - calc_duration(gx) / 2 - calc_duration(rf180) / 2
delayTE3 = TR - TE - calc_duration(gx)
delay1 = make_delay(delayTE1)
delay2 = make_delay(delayTE2)
delay3 = make_delay(delayTE3)
for i in range(2):
seq.add_block(rf, gz)
kwargs_for_gy_pre = {
"channel": 'y',
"system": system,
"area": -(Ny / 2 - i) * delta_k,
"duration": readoutTime / 2
}
def calculate_kspace(self, trajectory_delay: int = 0):
"""
Calculates the k-space trajectory of the entire pulse sequence.
Parameters
----------
trajectory_delay : int
Compensation factor in millis to align ADC and gradients in the reconstruction.
Returns
-------
k_traj_adc : numpy.ndarray
K-space trajectory sampled at `t_adc` timepoints.
k_traj : numpy.ndarray
K-space trajectory of the entire pulse sequence.
t_excitation : numpy.ndarray
Excitation timepoints.
t_refocusing : numpy.ndarray
Refocusing timepoints.
t_adc : numpy.ndarray
Sampling timepoints.
"""
c_excitation = 0
c_refocusing = 0
c_adc_samples = 0
for i in range(len(self.block_events)):
block = self.get_block(i + 1)
if hasattr(block, 'rf'):
if not hasattr(block.rf,
'use') or block.rf.use != 'refocusing':
c_excitation += 1
else:
c_refocusing += 1
if hasattr(block, 'adc'):
c_adc_samples += int(block.adc.num_samples)
t_excitation = np.zeros(c_excitation)
t_refocusing = np.zeros(c_refocusing)
k_time = np.zeros(c_adc_samples)
current_dur = 0
c_excitation = 0
c_refocusing = 0
k_counter = 0
traj_recon_delay = trajectory_delay
for i in range(len(self.block_events)):
block = self.get_block(i + 1)
if hasattr(block, 'rf'):
rf = block.rf
rf_center, _ = calc_rf_center(rf)
t = rf.delay + rf_center
if not hasattr(block.rf,
'use') or block.rf.use != 'refocusing':
t_excitation[c_excitation] = current_dur + t
c_excitation += 1
else:
t_refocusing[c_refocusing] = current_dur + t
c_refocusing += 1
if hasattr(block, 'adc'):
k_time[k_counter:k_counter + block.adc.num_samples] = np.arange(
block.adc.num_samples
) * block.adc.dwell + block.adc.delay + current_dur + traj_recon_delay
k_counter += block.adc.num_samples
current_dur += calc_duration(block)
gw = self.gradient_waveforms()
i_excitation = np.round(t_excitation / self.grad_raster_time)
i_refocusing = np.round(t_refocusing / self.grad_raster_time)
k_traj = np.zeros(gw.shape)
k = [0, 0, 0]
for i in range(gw.shape[1]):
k += gw[:, i] * self.grad_raster_time
k_traj[:, i] = k
if len(np.where(i_excitation == i + 1)[0]) >= 1:
k = 0
k_traj[:, i] = np.nan
if len(np.where(i_refocusing == i + 1)[0]) >= 1:
k = -k
k_traj_adc = []
for i in range(k_traj.shape[0]):
k_traj_adc.append(
np.interp(
k_time,
np.arange(1, k_traj.shape[1] + 1) * self.grad_raster_time,
k_traj[i]))
k_traj_adc = np.asarray(k_traj_adc)
t_adc = k_time
return k_traj_adc, k_traj, t_excitation, t_refocusing, t_adc
rf_dur *= 1e-3 # [s]
slice_res *= 1e-3 # [m]
if Nintl/redfac%1 != 0:
raise ValueError('Number of interleaves is not multiple of reduction factor')
if spiraltype!=1 and spiraltype!=4:
ValueError('Right now only spiraltype 1 (spiral out) and 4 (ROI) possible.')
#%% RF Pulse and slab/slice selection gradient
# make rf pulse and calculate duration of excitation and rewinding
rf, gz, gz_rew, rf_del = make_sinc_pulse(flip_angle=flip_angle*np.pi/180, system=system, duration=rf_dur, slice_thickness=slice_res,
apodization=0.5, time_bw_product=tbp_exc, use='excitation', return_gz=True, return_delay=True)
exc_to_rew = rf_del.delay - rf_dur/2 - rf.delay # time from middle of rf pulse to rewinder, rf_del.delay equals the block length
rew_dur = calc_duration(gz_rew)
# RF spoiling parameters
rf_spoiling_inc = 50 # increment of RF spoiling [°]
rf_phase = 0
rf_inc = 0
# Fat saturation
if fatsat:
if B0 > 4:
fatsat_bw = 1000 # bandwidth of fatsat pulse [Hz]
else:
fatsat_bw = 300
fatsat_fa = 110 # flip angle [°]
fatsat_dur = ph.round_up_to_raster(fatsat_tbp/fatsat_bw, decimals=5)
def plot(self,
type: str = 'Gradient',
time_range=(0, np.inf),
time_disp: str = 's',
save: bool = False):
"""
Plot `Sequence`.
Parameters
----------
type : str
Gradients display type, must be one of either 'Gradient' or 'Kspace'.
time_range : List
Time range (x-axis limits) for plotting the sequence. Default is 0 to infinity (entire sequence).
time_disp : str
Time display type, must be one of `s`, `ms` or `us`.
save_as : bool
Boolean flag indicating if plots should be saved. The two figures will be saved as JPG with numerical
suffixes to the filename 'seq_plot'.
"""
valid_plot_types = ['Gradient', 'Kspace']
valid_time_units = ['s', 'ms', 'us']
if type not in valid_plot_types:
raise Exception()
if time_disp not in valid_time_units:
raise Exception()
fig1, fig2 = plt.figure(1), plt.figure(2)
sp11 = fig1.add_subplot(311)
sp12, sp13 = fig1.add_subplot(312, sharex=sp11), fig1.add_subplot(
313, sharex=sp11)
fig2_sp_list = [
fig2.add_subplot(311, sharex=sp11),
fig2.add_subplot(312, sharex=sp11),
fig2.add_subplot(313, sharex=sp11)
]
t_factor_list = [1, 1e3, 1e6]
t_factor = t_factor_list[valid_time_units.index(time_disp)]
t0 = 0
for iB in range(1, len(self.block_events) + 1):
block = self.get_block(iB)
is_valid = time_range[0] <= t0 <= time_range[1]
if is_valid:
if hasattr(block, 'adc'):
adc = block.adc
t = adc.delay + [(x * adc.dwell)
for x in range(0, int(adc.num_samples))]
sp11.plot((t0 + t), np.zeros(len(t)), 'rx')
if hasattr(block, 'rf'):
rf = block.rf
tc, ic = calc_rf_center(rf)
t = rf.t + rf.delay
tc = tc + rf.delay
sp12.plot(t_factor * (t0 + t), abs(rf.signal))
sp13.plot(
t_factor * (t0 + t),
np.angle(
rf.signal * np.exp(1j * rf.phase_offset) *
np.exp(1j * 2 * math.pi * rf.t * rf.freq_offset)),
t_factor * (t0 + tc),
np.angle(rf.signal[ic] * np.exp(1j * rf.phase_offset) *
np.exp(1j * 2 * math.pi * rf.t[ic] *
rf.freq_offset)), 'xb')
grad_channels = ['gx', 'gy', 'gz']
for x in range(0, len(grad_channels)):
if hasattr(block, grad_channels[x]):
grad = getattr(block, grad_channels[x])
if grad.type == 'grad':
# In place unpacking of grad.t with the starred expression
t = grad.delay + [
0, *(grad.t + (grad.t[1] - grad.t[0]) / 2),
grad.t[-1] + grad.t[1] - grad.t[0]
]
waveform = np.array([grad.first, grad.last])
waveform = 1e-3 * np.insert(
waveform, 1, grad.waveform)
else:
t = np.cumsum([
0, grad.delay, grad.rise_time, grad.flat_time,
grad.fall_time
])
waveform = 1e-3 * grad.amplitude * np.array(
[0, 0, 1, 1, 0])
fig2_sp_list[x].plot(t_factor * (t0 + t), waveform)
t0 += calc_duration(block)
grad_plot_labels = ['x', 'y', 'z']
sp11.set_ylabel('ADC')
sp12.set_ylabel('RF mag (Hz)')
sp13.set_ylabel('RF phase (rad)')
[
fig2_sp_list[x].set_ylabel(f'G{grad_plot_labels[x]} (kHz/m)')
for x in range(3)
]
# Setting display limits
disp_range = t_factor * np.array(
[time_range[0], min(t0, time_range[1])])
sp11.set_xlim(disp_range)
sp12.set_xlim(disp_range)
sp13.set_xlim(disp_range)
[x.set_xlim(disp_range) for x in fig2_sp_list]
fig1.tight_layout()
fig2.tight_layout()
if save:
fig1.savefig('seq_plot1.jpg')
fig2.savefig('seq_plot2.jpg')
plt.show()
"system": system,
"area": -gx.area / 2,
"duration": 2e-3
}
gx_pre = make_trapezoid(kwargs_for_gxpre)
kwargs_for_gz_reph = {
"channel": 'z',
"system": system,
"area": -gz.area / 2,
"duration": 2e-3
}
gz_reph = make_trapezoid(kwargs_for_gz_reph)
phase_areas = (np.arange(Ny) - (Ny / 2)) * delta_k
TE, TR = 10e-3, 200e-3
delayTE = TE - calc_duration(
gx_pre) - calc_duration(gz) / 2 - calc_duration(gx) / 2
delayTR = TR - calc_duration(gx_pre) - calc_duration(gz) - calc_duration(
gx) - delayTE
delay1 = make_delay(delayTE)
delay2 = make_delay(delayTR)
for i in range(Ny):
seq.add_block(rf, gz)
kwargsForGyPre = {
"channel": 'y',
"system": system,
"area": phase_areas[i],
"duration": 2e-3
}
gyPre = make_trapezoid(kwargsForGyPre)
seq.add_block(gx_pre, gyPre, gz_reph)
sys = Opts(max_grad=28, grad_unit='mT/m', max_slew=150, slew_unit='T/m/s', rf_ringdown_time=20e-6, rf_dead_time=100e-6,
adc_dead_time=10e-6)
rf, gz, gzr = make_sinc_pulse(flip_angle=alpha * math.pi / 180, duration=4e-3, slice_thickness=slice_thickness,
apodization=0.5, time_bw_product=4, system=sys)
deltak = 1 / fov
gx = make_trapezoid(channel='x', flat_area=Nx * deltak, flat_time=6.4e-3, system=sys)
adc = make_adc(num_samples=Nx, duration=gx.flat_time, delay=gx.rise_time, system=sys)
gx_pre = make_trapezoid(channel='x', area=-gx.area / 2, duration=2e-3, system=sys)
gz_reph = make_trapezoid(channel='z', area=-gz.area / 2, duration=2e-3, system=sys)
phase_areas = (np.arange(Ny) - Ny / 2) * deltak
gx_spoil = make_trapezoid(channel='x', area=2 * Nx * deltak, system=sys)
gz_spoil = make_trapezoid(channel='z', area=4 / slice_thickness, system=sys)
delay_TE = math.ceil((TE - calc_duration(gx_pre) - gz.fall_time - gz.flat_time / 2 - calc_duration(
gx) / 2) / seq.grad_raster_time) * seq.grad_raster_time
delay_TR = math.ceil((TR - calc_duration(gx_pre) - calc_duration(gz) - calc_duration(
gx) - delay_TE) / seq.grad_raster_time) * seq.grad_raster_time
if not np.all(delay_TR >= calc_duration(gx_spoil, gz_spoil)):
raise Exception()
rf_phase = 0
rf_inc = 0
for i in range(Ny):
rf.phase_offset = rf_phase / 180 * np.pi
adc.phase_offset = rf_phase / 180 * np.pi
rf_inc = divmod(rf_inc + rf_spoiling_inc, 360.0)[1]
rf_phase = divmod(rf_phase + rf_inc, 360.0)[1]
def add_gradients(grads: Union[list, tuple],
system=Opts(),
max_grad: int = 0,
max_slew: int = 0) -> SimpleNamespace:
"""
Superpose several gradient events.
Parameters
----------
grads : list or tuple
List or tuple of 'SimpleNamespace' gradient events.
system : Opts, optional, default=Opts()
System limits.
max_grad : float, optional, default=0
Maximum gradient amplitude.
max_slew : float, optional, default=0
Maximum slew rate.
Returns
-------
grad : SimpleNamespace
Superimposition of gradient events from `grads`.
"""
max_grad = max_grad if max_grad > 0 else system.max_grad
max_slew = max_slew if max_slew > 0 else system.max_slew
if len(grads) < 2:
raise Exception()
# First gradient defines channel
channel = grads[0].channel
# Find out the general delay of all gradients and other statistics
delays, firsts, lasts, durs = [], [], [], []
for ii in range(len(grads)):
delays.append(grads[ii].delay)
firsts.append(grads[ii].first)
lasts.append(grads[ii].last)
durs.append(calc_duration(grads[ii]))
# Convert to numpy.ndarray for fancy-indexing later on
firsts, lasts = np.array(firsts), np.array(lasts)
common_delay = min(delays)
total_duration = max(durs)
waveforms = dict()
max_length = 0
for ii in range(len(grads)):
g = grads[ii]
if g.type == 'grad':
waveforms[ii] = g.waveform
elif g.type == 'trap':
if g.flat_time > 0: # Triangle or trapezoid
times = [
g.delay - common_delay,
g.delay - common_delay + g.rise_time,
g.delay - common_delay + g.rise_time + g.flat_time,
g.delay - common_delay + g.rise_time + g.flat_time +
g.fall_time
]
amplitudes = [0, g.amplitude, g.amplitude, 0]
else:
times = [
g.delay - common_delay,
g.delay - common_delay + g.rise_time,
g.delay - common_delay + g.rise_time + g.flat_time
]
amplitudes = [0, g.amplitude, 0]
waveforms[ii] = points_to_waveform(
times=times,
amplitudes=amplitudes,
grad_raster_time=system.grad_raster_time)
else:
raise ValueError('Unknown gradient type')
if g.delay - common_delay > 0:
# Stop for numpy.arange is not g.delay - common_delay - system.grad_raster_time like in Matlab
# so as to include the endpoint
t_delay = np.arange(0,
g.delay - common_delay,
step=system.grad_raster_time)
waveforms[ii] = np.insert(waveforms[ii], 0, t_delay)
num_points = len(waveforms[ii])
max_length = num_points if num_points > max_length else max_length
w = np.zeros(max_length)
for ii in range(len(grads)):
wt = np.zeros(max_length)
wt[0:len(waveforms[ii])] = waveforms[ii]
w += wt
grad = make_arbitrary_grad(channel,
w,
system,
max_slew=max_slew,
max_grad=max_grad,
delay=common_delay)
grad.first = np.sum(firsts[np.array(delays) == common_delay])
grad.last = np.sum(lasts[np.where(durs == total_duration)])
return grad
def add_block(self, block_index: int, *args):
"""
Inserts pulse sequence events into `self.block_events` at position `block_index`. Also performs gradient checks.
Parameters
----------
block_index : int
Index at which `SimpleNamespace` events have to be inserted into `self.block_events`.
args : list
List of `SimpleNamespace` pulse sequence events to be added to `self.block_events`.
"""
block_duration = calc_duration(*args)
self.block_events[block_index] = np.zeros(6, dtype=np.int)
duration = 0
check_g = {}
for event in args:
if event.type == 'rf':
mag = np.abs(event.signal)
amplitude = np.max(mag)
mag = np.divide(mag, amplitude)
# Following line of code is a workaround for numpy's divide functions returning NaN when mathematical
# edge cases are encountered (eg. divide by 0)
mag[np.isnan(mag)] = 0
phase = np.angle(event.signal)
phase[phase < 0] += 2 * np.pi
phase /= 2 * np.pi
mag_shape = compress_shape(mag)
data = np.insert(mag_shape.data, 0, mag_shape.num_samples)
mag_id, found = self.shape_library.find(data)
if not found:
self.shape_library.insert(mag_id, data)
phase_shape = compress_shape(phase)
data = np.insert(phase_shape.data, 0, phase_shape.num_samples)
phase_id, found = self.shape_library.find(data)
if not found:
self.shape_library.insert(phase_id, data)
use = 0
use_cases = {'excitation': 1, 'refocusing': 2, 'inversion': 3}
if hasattr(event, 'use'):
use = use_cases[event.use]
data = np.array((amplitude, mag_id, phase_id, event.delay,
event.freq_offset, event.phase_offset,
event.dead_time, event.ringdown_time, use))
data_id, found = self.rf_library.find(data)
if not found:
self.rf_library.insert(data_id, data)
self.block_events[block_index][1] = data_id
duration = max(
duration,
max(mag.shape) * self.rf_raster_time + event.dead_time +
event.ringdown_time + event.delay)
elif event.type == 'grad':
channel_num = ['x', 'y', 'z'].index(event.channel)
idx = 2 + channel_num
check_g[channel_num] = SimpleNamespace()
check_g[channel_num].idx = idx
check_g[channel_num].start = np.array(
(event.delay + min(event.t), event.first))
check_g[channel_num].stop = np.array(
(event.delay + max(event.t) + self.system.grad_raster_time,
event.last))
amplitude = max(abs(event.waveform))
if amplitude > 0:
g = event.waveform / amplitude
else:
g = event.waveform
shape = compress_shape(g)
data = np.insert(shape.data, 0, shape.num_samples)
shape_id, found = self.shape_library.find(data)
if not found:
self.shape_library.insert(shape_id, data)
data = np.array(
[amplitude, shape_id, event.delay, event.first, event.last])
grad_id, found = self.grad_library.find(data)
if not found:
self.grad_library.insert(grad_id, data, 'g')
idx = 2 + channel_num
self.block_events[block_index][idx] = grad_id
duration = max(duration, len(g) * self.grad_raster_time)
elif event.type == 'trap':
channel_num = ['x', 'y', 'z'].index(event.channel)
idx = 2 + channel_num
check_g[channel_num] = SimpleNamespace()
check_g[channel_num].idx = idx
check_g[channel_num].start = np.array((0, 0))
check_g[channel_num].stop = np.array(
(event.delay + event.rise_time + event.fall_time +
event.flat_time, 0))
data = np.array([
event.amplitude, event.rise_time, event.flat_time,
event.fall_time, event.delay
])
trap_id, found = self.grad_library.find(data)
if not found:
self.grad_library.insert(trap_id, data, 't')
self.block_events[block_index][idx] = trap_id
duration = max(
duration, event.delay + event.rise_time + event.flat_time +
event.fall_time)
elif event.type == 'adc':
data = np.array([
event.num_samples, event.dwell,
max(event.delay, event.dead_time), event.freq_offset,
event.phase_offset, event.dead_time
])
adc_id, found = self.adc_library.find(data)
if not found:
self.adc_library.insert(adc_id, data)
self.block_events[block_index][5] = adc_id
duration = max(
duration, event.delay + event.num_samples * event.dwell +
event.dead_time)
elif event.type == 'delay':
data = np.array([event.delay])
delay_id, found = self.delay_library.find(data)
if not found:
self.delay_library.insert(delay_id, data)
self.block_events[block_index][0] = delay_id
duration = max(duration, event.delay)
# =========
# PERFORM GRADIENT CHECKS
# =========
for cg_temp in check_g.keys():
cg = check_g[cg_temp]
if abs(cg.start[1]
) > self.system.max_slew * self.system.grad_raster_time:
if cg.start[0] != 0:
raise ValueError(
'No delay allowed for gradients which start with a non-zero amplitude'
)
if block_index > 1:
prev_id = self.block_events[block_index - 1][cg.idx]
if prev_id != 0:
prev_lib = self.grad_library.get(prev_id)
prev_dat = prev_lib['data']
prev_type = prev_lib['type']
if prev_type == 't':
raise Exception(
'Two consecutive gradients need to have the same amplitude at the connection point'
)
elif prev_type == 'g':
last = prev_dat[4]
if abs(
last - cg.start[1]
) > self.system.max_slew * self.system.grad_raster_time:
raise Exception(
'Two consecutive gradients need to have the same amplitude at the connection point'
)
else:
raise Exception(
'First gradient in the the first block has to start at 0.')
if cg.stop[
1] > self.system.max_slew * self.system.grad_raster_time and abs(
cg.stop[0] - block_duration) > 1e-7:
raise Exception(
'A gradient that doesnt end at zero needs to be aligned to the block boundary.'
)
def write_irse_interleaved_split_gradient(n=256,
fov=250e-3,
thk=5e-3,
fa=90,
te=12e-3,
tr=2000e-3,
ti=150e-3,
slice_locations=[0],
enc='xyz'):
"""
2D IRSE sequence with overlapping gradient ramps and interleaved slices
Inputs
------
n : integer
Matrix size (isotropic)
fov : float
Field-of-View in [meters]
thk : float
Slice thickness in [meters]
fa : float
Flip angle in [degrees]
te : float
Echo Time in [seconds]
tr : float
Repetition Time in [seconds]
ti : float
Inversion Time in [seconds]
slice_locations : array_like
Array of slice locations from isocenter in [meters]
enc : str
Spatial encoding directions; 1st - readout; 2nd - phase encoding; 3rd - slice select
Use str with any permutation of x, y, and z to obtain orthogonal slices
e.g. The default 'xyz' means axial(z) slice with readout in x and phase encoding in y
Returns
-------
seq : pypulseq.Sequence.sequence Sequence object
Output sequence object. Can be saved with seq.write('file_name.seq')
sl_order : numpy.ndarray
Randomly generated slice order. Useful for reconstruction.
"""
# =========
# SYSTEM LIMITS
# =========
# Set the hardware limits and initialize sequence object
dG = 250e-6 # Fixed ramp time for all gradients
system = Opts(max_grad=32,
grad_unit='mT/m',
max_slew=130,
slew_unit='T/m/s',
rf_ringdown_time=100e-6,
rf_dead_time=100e-6,
adc_dead_time=10e-6)
seq = Sequence(system)
# =========
# TIME CALCULATIONS
# =========
readout_time = 6.4e-3 + 2 * system.adc_dead_time
t_ex = 2.5e-3
t_exwd = t_ex + system.rf_ringdown_time + system.rf_dead_time
t_ref = 2e-3
t_refwd = t_ref + system.rf_ringdown_time + system.rf_dead_time
t_sp = 0.5 * (te - readout_time - t_refwd)
t_spex = 0.5 * (te - t_exwd - t_refwd)
fsp_r = 1
fsp_s = 0.5
# =========
# ENCODING DIRECTIONS
# ==========
ch_ro = enc[0]
ch_pe = enc[1]
ch_ss = enc[2]
# =========
# RF AND GRADIENT SHAPES - BASED ON RESOLUTION REQUIREMENTS : kmax and Npe
# =========
# RF Phases
rf_ex_phase = np.pi / 2
rf_ref_phase = 0
# Excitation phase (90 deg)
flip_ex = 90 * np.pi / 180
rf_ex, gz, _ = make_sinc_pulse(flip_angle=flip_ex,
system=system,
duration=t_ex,
slice_thickness=thk,
apodization=0.5,
time_bw_product=4,
phase_offset=rf_ex_phase,
return_gz=True)
gs_ex = make_trapezoid(channel=ch_ss,
system=system,
amplitude=gz.amplitude,
flat_time=t_exwd,
rise_time=dG)
# Refocusing (same gradient & RF is used for initial inversion)
flip_ref = fa * np.pi / 180
rf_ref, gz, _ = make_sinc_pulse(flip_angle=flip_ref,
system=system,
duration=t_ref,
slice_thickness=thk,
apodization=0.5,
time_bw_product=4,
phase_offset=rf_ref_phase,
use='refocusing',
return_gz=True)
gs_ref = make_trapezoid(channel=ch_ss,
system=system,
amplitude=gs_ex.amplitude,
flat_time=t_refwd,
rise_time=dG)
ags_ex = gs_ex.area / 2
gs_spr = make_trapezoid(channel=ch_ss,
system=system,
area=ags_ex * (1 + fsp_s),
duration=t_sp,
rise_time=dG)
gs_spex = make_trapezoid(channel=ch_ss,
system=system,
area=ags_ex * fsp_s,
duration=t_spex,
rise_time=dG)
delta_k = 1 / fov
k_width = n * delta_k
gr_acq = make_trapezoid(channel=ch_ro,
system=system,
flat_area=k_width,
flat_time=readout_time,
rise_time=dG)
adc = make_adc(num_samples=n,
duration=gr_acq.flat_time - 40e-6,
delay=20e-6)
gr_spr = make_trapezoid(channel=ch_ro,
system=system,
area=gr_acq.area * fsp_r,
duration=t_sp,
rise_time=dG)
gr_spex = make_trapezoid(channel=ch_ro,
system=system,
area=gr_acq.area * (1 + fsp_r),
duration=t_spex,
rise_time=dG)
agr_spr = gr_spr.area
agr_preph = gr_acq.area / 2 + agr_spr
gr_preph = make_trapezoid(channel=ch_ro,
system=system,
area=agr_preph,
duration=t_spex,
rise_time=dG)
phase_areas = (np.arange(n) - n / 2) * delta_k
# Split gradients and recombine into blocks
gs1_times = [0, gs_ex.rise_time]
gs1_amp = [0, gs_ex.amplitude]
gs1 = make_extended_trapezoid(channel=ch_ss,
times=gs1_times,
amplitudes=gs1_amp)
gs2_times = [0, gs_ex.flat_time]
gs2_amp = [gs_ex.amplitude, gs_ex.amplitude]
gs2 = make_extended_trapezoid(channel=ch_ss,
times=gs2_times,
amplitudes=gs2_amp)
gs3_times = [
0, gs_spex.rise_time, gs_spex.rise_time + gs_spex.flat_time,
gs_spex.rise_time + gs_spex.flat_time + gs_spex.fall_time
]
gs3_amp = [
gs_ex.amplitude, gs_spex.amplitude, gs_spex.amplitude, gs_ref.amplitude
]
gs3 = make_extended_trapezoid(channel=ch_ss,
times=gs3_times,
amplitudes=gs3_amp)
gs4_times = [0, gs_ref.flat_time]
gs4_amp = [gs_ref.amplitude, gs_ref.amplitude]
gs4 = make_extended_trapezoid(channel=ch_ss,
times=gs4_times,
amplitudes=gs4_amp)
gs5_times = [
0, gs_spr.rise_time, gs_spr.rise_time + gs_spr.flat_time,
gs_spr.rise_time + gs_spr.flat_time + gs_spr.fall_time
]
gs5_amp = [gs_ref.amplitude, gs_spr.amplitude, gs_spr.amplitude, 0]
gs5 = make_extended_trapezoid(channel=ch_ss,
times=gs5_times,
amplitudes=gs5_amp)
gs7_times = [
0, gs_spr.rise_time, gs_spr.rise_time + gs_spr.flat_time,
gs_spr.rise_time + gs_spr.flat_time + gs_spr.fall_time
]
gs7_amp = [0, gs_spr.amplitude, gs_spr.amplitude, gs_ref.amplitude]
gs7 = make_extended_trapezoid(channel=ch_ss,
times=gs7_times,
amplitudes=gs7_amp)
gr3 = gr_preph
gr5_times = [
0, gr_spr.rise_time, gr_spr.rise_time + gr_spr.flat_time,
gr_spr.rise_time + gr_spr.flat_time + gr_spr.fall_time
]
gr5_amp = [0, gr_spr.amplitude, gr_spr.amplitude, gr_acq.amplitude]
gr5 = make_extended_trapezoid(channel=ch_ro,
times=gr5_times,
amplitudes=gr5_amp)
gr6_times = [0, readout_time]
gr6_amp = [gr_acq.amplitude, gr_acq.amplitude]
gr6 = make_extended_trapezoid(channel=ch_ro,
times=gr6_times,
amplitudes=gr6_amp)
gr7_times = [
0, gr_spr.rise_time, gr_spr.rise_time + gr_spr.flat_time,
gr_spr.rise_time + gr_spr.flat_time + gr_spr.fall_time
]
gr7_amp = [gr_acq.amplitude, gr_spr.amplitude, gr_spr.amplitude, 0]
gr7 = make_extended_trapezoid(channel=ch_ro,
times=gr7_times,
amplitudes=gr7_amp)
t_ex = gs1.t[-1] + gs2.t[-1] + gs3.t[-1]
t_ref = gs4.t[-1] + gs5.t[-1] + gs7.t[-1] + readout_time
t_end = gs4.t[-1] + gs5.t[-1]
# Calculate maximum number of slices that can fit in one TR
# Without spoilers on each side
TE_prime = 0.5 * calc_duration(gs_ref) + ti + te + 0.5 * readout_time + np.max(
[calc_duration(gs7), calc_duration(gr7)]) + \
calc_duration(gs4) + calc_duration(gs5)
ns_per_TR = np.floor(tr / TE_prime)
print('Number of slices that can be accommodated = ' + str(ns_per_TR))
# Lengthen TR to accommodate slices if needed, and display message
n_slices = len(slice_locations)
if (ns_per_TR < n_slices):
print(
f'TR too short, adapted to include all slices to: {n_slices * TE_prime + 50e-6} s'
)
TR = round(n_slices * TE_prime + 50e-6, ndigits=5)
print('New TR = ' + str(TR))
ns_per_TR = np.floor(TR / TE_prime)
if (n_slices < ns_per_TR):
ns_per_TR = n_slices
# randperm so that adjacent slices do not get excited one after the other
sl_order = np.random.permutation(n_slices)
print('Number of slices acquired per TR = ' + str(ns_per_TR))
# Delays
TI_fill = ti - (0.5 * calc_duration(gs_ref) + calc_duration(gs1) +
0.5 * calc_duration(gs2))
delay_TI = make_delay(TI_fill)
TR_fill = tr - ns_per_TR * TE_prime
delay_TR = make_delay(TR_fill)
for k_ex in range(n):
phase_area = phase_areas[k_ex]
gp_pre = make_trapezoid(channel=ch_pe,
system=system,
area=phase_area,
duration=t_sp,
rise_time=dG)
gp_rew = make_trapezoid(channel=ch_pe,
system=system,
area=-phase_area,
duration=t_sp,
rise_time=dG)
s_in_TR = 0
for s in range(len(sl_order)):
# rf_ex.freq_offset = gs_ex.amplitude * slice_thickness * (sl_order[s] - (n_slices - 1) / 2)
# rf_ref.freq_offset = gs_ref.amplitude * slice_thickness * (sl_order[s] - (n_slices - 1) / 2)
rf_ex.freq_offset = gs_ex.amplitude * slice_locations[sl_order[s]]
rf_ref.freq_offset = gs_ref.amplitude * slice_locations[
sl_order[s]]
rf_ex.phase_offset = rf_ex_phase - 2 * np.pi * rf_ex.freq_offset * calc_rf_center(
rf_ex)[0]
rf_ref.phase_offset = rf_ref_phase - 2 * np.pi * rf_ref.freq_offset * calc_rf_center(
rf_ref)[0]
# Inversion using refocusing pulse
seq.add_block(gs_ref, rf_ref)
seq.add_block(delay_TI)
# SE portion
seq.add_block(gs1)
seq.add_block(gs2, rf_ex)
seq.add_block(gs3, gr3)
seq.add_block(gs4, rf_ref)
seq.add_block(gs5, gr5, gp_pre)
seq.add_block(gr6, adc)
seq.add_block(gs7, gr7, gp_rew)
seq.add_block(gs4)
seq.add_block(gs5)
s_in_TR += 1
if (s_in_TR == ns_per_TR):
seq.add_block(delay_TR)
s_in_TR = 0
# Check timing to make sure sequence runs on scanner
seq.check_timing()
return seq, sl_order
