def test_doptimal():
""" Test D-optimal optimization method """
params = opt.ASLParams(f=50.0 / 6000)
att_dist = opt.ATTDist(0.2, 2.1, 0.001, 0.3)
scan = opt.ASLScan('var_multi_pCASL', duration=300, npld=6, readout=0.5)
lims = opt.Limits(0.1, 3.0, 0.025)
optimizer = opt.DOptimal(params, scan, att_dist, lims)
output = optimizer.optimize()
assert np.allclose(output.plds, [0.2, 0.7, 0.725, 1.55, 1.875, 2.075])
def test_loptimal_att():
""" Test L-optimal optimization method for ATT """
params = opt.ASLParams(f=50.0 / 6000)
att_dist = opt.ATTDist(0.2, 2.1, 0.001, 0.3)
scan = opt.ASLScan('var_multi_pCASL', duration=300, npld=6, readout=0.5)
lims = opt.Limits(0.1, 3.0, 0.025)
optimizer = opt.LOptimal([[0, 0], [0, 1]], params, scan, att_dist, lims)
output = optimizer.optimize()
assert np.allclose(output.plds, [0.1, 0.475, 0.7, 1.025, 1.725, 2.1])
def test_loptimal_cbf_multislice():
""" Test L-optimal optimization method for CBF with 2D readout """
params = opt.ASLParams(f=50.0 / 6000)
att_dist = opt.ATTDist(0.2, 2.1, 0.001, 0.3)
scan = opt.ASLScan('var_multi_pCASL',
duration=300,
npld=6,
readout=0.5,
nslices=10,
slicedt=0.0452)
lims = opt.Limits(0.1, 3.0, 0.025)
optimizer = opt.LOptimal([[1, 0], [0, 0]], params, scan, att_dist, lims)
output = optimizer.optimize()
assert np.allclose(output.plds, [0.1, 1.025, 1.625, 1.8, 1.95, 2.1])
def test_doptimal_multislice():
""" Test D-optimal optimization method with 2D readout """
params = opt.ASLParams(f=50.0 / 6000)
att_dist = opt.ATTDist(0.2, 2.1, 0.001, 0.3)
scan = opt.ASLScan('var_multi_pCASL',
duration=300,
npld=6,
readout=0.5,
nslices=10,
slicedt=0.0452)
lims = opt.Limits(0.1, 3.0, 0.025)
optimizer = opt.DOptimal(params, scan, att_dist, lims)
output = optimizer.optimize()
assert np.allclose(output.plds, [0.1, 0.575, 0.725, 1.4, 1.75, 2.025])
def main():
"""
Simple example of optimizing the PLDs of a standard pCASL acquisition
as per Buxton et al. MRM 1998
"""
# Define the ASL parameters
params = opt.ASLParams(f=50.0/6000)
# ATT (BAT) distribution
att_dist = opt.ATTDist(0.2, 2.1, 0.001, 0.3)
# Details of the desired scan to optimize for
scan = opt.ASLScan(opt.VAR_MULTI_PCASL, duration=300, npld=6, readout=0.5, slices=3, slicedt=0.0452)
# PLD limits and step size to search over
lims = opt.Limits(0.1, 3.0, 0.025)
# Type of optimisation
# Note: the output best_min_variance is not comparable between D-optimal and L-optimal
#opttype = opt.LOptimal([[1, 0], [0, 0]])
optimizer = opt.DOptimal(params, scan, att_dist, lims)
# Run the optimisation
output = optimizer.optimize()
# Plot optimized protocol
plt.subplot(1, 2, 1)
plt.title("CBFSD")
plt.ylabel('SD (ml/100g/min)')
plt.xlabel("ATT (s)")
plt.ylim(0, 9)
for slice_idx in range(scan.slices):
plt.plot(att, np.squeeze(np.sqrt(np.abs(output.cov_optimized[slice_idx, :, 0, 0]))),
label="Optimised Protocol - slice %i" % slice_idx)
plt.legend()
plt.subplot(1, 2, 2)
plt.title("BATSD")
plt.ylabel("SD (s)")
plt.xlabel("ATT (s)")
plt.ylim(0, 0.25)
for slice_idx in range(scan.slices):
plt.plot(att, np.squeeze(np.sqrt(np.abs(output.cov_optimized[slice_idx, :, 1, 1]))),
label="Optimised Protocol - slice %i" % slice_idx)
plt.legend()
plt.show()