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%matplotlib inline
%load_ext autoreload
%autoreload 1
%aimport nanslice
%aimport nanslice.jupyter
%aimport QUIT.base
%aimport QUIT.interfaces.rufis
import nibabel as nib
import numpy as np
import requests
import gzip
from os.path import isfile
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if not isfile('classes.mnc'):
print('Downloading classes')
params = {'download_for_real':'[Start Download!]',
'do_download_alias':'phantom_1.0mm_normal_crisp',
'format_value':'minc',
'who_name': 'Tobias Wood',
'who_institution': 'KCL',
'who_email': 'tobias.wood@kcl.ac.uk'}
response = requests.get(url='http://brainweb.bic.mni.mcgill.ca/cgi/brainweb1', params=params)
minc_file = open('classes.mnc', 'wb')
minc_file.write(response.content)
if not isfile('rf20_B.mnc'):
print('Downloading B0')
params = {'download_for_real':'[Start Download!]',
'do_download_alias':'rf20_B.mnc.gz',
'format_value':'minc',
'zip_value':'none',
'who_name': 'Tobias Wood',
'who_institution': 'KCL',
'who_email': 'tobias.wood@kcl.ac.uk'}
response = requests.get(url='https://brainweb.bic.mni.mcgill.ca/cgi/brainweb1', params=params)
b0_file = open('rf20_B.mnc', 'wb')
b0_file.write(response.content)
if not isfile('rf20_C.mnc'):
print('Downloading B1')
params = {'download_for_real':'[Start Download!]',
'do_download_alias':'rf20_C.mnc.gz',
'format_value':'minc',
'zip_value':'none',
'who_name': 'Tobias Wood',
'who_institution': 'KCL',
'who_email': 'tobias.wood@kcl.ac.uk'}
response = requests.get(url='https://brainweb.bic.mni.mcgill.ca/cgi/brainweb1', params=params)
b1_file = open('rf20_C.mnc', 'wb')
b1_file.write(response.content)
classes = nanslice.Layer('classes.mnc')
b0_minc = nanslice.Layer('rf20_B.mnc')
b1_minc = nanslice.Layer('rf20_C.mnc')
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# 0=Background
# 1=CSF
# 2=Grey Matter
# 3=White Matter
# 4=Fat
# 5=Muscle/Skin
# 6=Skin
# 7=Skull
# 8=Glial Matter
# 9=Connective
def classes_to_params(M0Vals, T1Vals, T2vals, subsamp=1):
class_data = classes.image.get_data().astype('int32')
M0data = np.choose(class_data[::subsamp,::subsamp,::subsamp], M0vals).astype('float32')
T1data = np.choose(class_data[::subsamp,::subsamp,::subsamp], T1vals).astype('float32')
T2data = np.choose(class_data[::subsamp,::subsamp,::subsamp], T2vals).astype('float32')
f0data = (b0_minc.image.get_data().astype('float32')[::subsamp,::subsamp,::subsamp] - 1) * 500
B1data = b1_minc.image.get_data().astype('float32')[::subsamp,::subsamp,::subsamp]
# PDdata = np.array(list(map(PDFunc, classes.image.get_data())))
M0image = nib.nifti1.Nifti1Image(M0data, affine=classes.image.affine)
T1image = nib.nifti1.Nifti1Image(T1data, affine=classes.image.affine)
T2image = nib.nifti1.Nifti1Image(T2data, affine=classes.image.affine)
f0image = nib.nifti1.Nifti1Image(f0data, affine=classes.image.affine)
B1image = nib.nifti1.Nifti1Image(B1data, affine=classes.image.affine)
nib.save(M0image, 'M0.nii.gz')
nib.save(T1image, 'T1.nii.gz')
nib.save(T2image, 'T2.nii.gz')
nib.save(f0image, 'f0.nii.gz')
nib.save(B1image, 'B1.nii.gz')
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M0vals = np.array([0, 1, 0.8, 0.7, 0, 0, 0, 0, 0, 0])
T1vals = np.array([0, 3.0, 1.3, 0.9, 0, 0, 0, 0, 0, 0])
T2vals = np.array([0, 0.5, 0.08, 0.06, 0, 0, 0, 0, 0, 0])
classes_to_params(M0vals, T1vals, T2vals, 1)
region='90,0,0,1,217,181'
region2='45,0,0,1,109,91'
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seq = {
"TR": 0.002,
"Tramp": 0.01,
"Tspoil": 0.05,
"FA": [2, 8, 8],
"Trf": [28e-6, 28e-6, 28e-6],
"spokes_per_seg": 48,
"groups_per_seg": [1, 1, 1],
"prep_FA": [0, 0, 360],
"prep_df": [0, 0, 0],
"prep_Trf": 0.001,
"prep_p1": 1.,
"prep_p2": 1.
}
inputs={'M0':'M0.nii.gz', 'T1':'T1.nii.gz', 'B1':'B1.nii.gz'}
sim_result = QUIT.interfaces.rufis.SteadyStateSim(sequence=seq, in_file='sim.nii.gz', noise=0.00025, mask='M0.nii.gz', subregion=region, **inputs).run()
display(nanslice.jupyter.timeseries(sim_result.outputs.out_file, clim=[0,0.025]))
mupa = QUIT.interfaces.rufis.SteadyState(sequence=seq, in_file='sim.nii.gz', mask_file='M0.nii.gz', subregion=region).run()
for name, fname in inputs.items():
display(nanslice.jupyter.compare(fname, 'SS_' + fname, axis='z', title=name))
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seq = {
"TR": 0.002,
"Tramp": 0.01,
"Tspoil": 0.05,
"FA": [2, 8, 2, 8, 2, 8],
"Trf": [28e-6, 28e-6, 28e-6, 28e-6, 28e-6, 28e-6],
"spokes_per_seg": 48,
"groups_per_seg": [1, 1, 1, 1, 1, 1],
"prep_FA": [0, 180, 180, 360, 540, 540],
"prep_df": [0, -0.25, 0.25, 0, -0.1, 0.1],
"prep_Trf": 0.01,
"prep_p1": 1.,
"prep_p2": 1.
}
inputs={'M0':'M0.nii.gz', 'T1':'T1.nii.gz', 'T2':'T2.nii.gz', 'f0': 'f0.nii.gz', 'B1':'B1.nii.gz'}
sim_result = QUIT.interfaces.rufis.SteadyStateSim(sequence=seq, fitT2=True, in_file='sim.nii.gz', noise=0.00025, mask='M0.nii.gz', subregion=region, **inputs).run()
display(nanslice.jupyter.timeseries(sim_result.outputs.out_file, clim=[0,0.025]))
mupa = QUIT.interfaces.rufis.SteadyState(sequence=seq, fitT2=True, in_file='sim.nii.gz', mask_file='M0.nii.gz', subregion=region).run()
for name, fname in inputs.items():
display(nanslice.jupyter.compare(fname, 'SS_' + fname, axis='z', title=name))
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seq = {
"TR": 0.002,
"Tramp": 0.01,
"Tspoil": 0.05,
"FA": [2, 8, 2, 2, 2, 2, 2],
"Trf": [28e-6, 28e-6, 28e-6, 28e-6, 28e-6, 28e-6, 28e-6],
"spokes_per_seg": 48,
"groups_per_seg": [1, 1, 1, 1, 1, 1, 1],
"prep_FA": [0, 0, 180, 180, 360, 540, 540],
"prep_df": [0, 0, -0.25, 0.25, 0, -0.1, 0.1],
"prep_Trf": 0.01,
"prep_p1": 1.,
"prep_p2": 1.
}
inputs={'M0':'M0.nii.gz', 'T1':'T1.nii.gz', 'T2':'T2.nii.gz', 'f0': 'f0.nii.gz', 'B1':'B1.nii.gz'}
sim_result = QUIT.interfaces.rufis.SteadyStateSim(sequence=seq, fitT2=True, in_file='sim.nii.gz', noise=0.00025, mask='M0.nii.gz', subregion=region, **inputs).run()
display(nanslice.jupyter.timeseries(sim_result.outputs.out_file, clim=[0,0.025]))
mupa = QUIT.interfaces.rufis.SteadyState(sequence=seq, fitT2=True, in_file='sim.nii.gz', mask_file='M0.nii.gz', subregion=region).run()
for name, fname in inputs.items():
display(nanslice.jupyter.compare(fname, 'SS_' + fname, axis='z', title=name))
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