def image_to_parametric(pet_image,
dt,
model_name,
model_inputs,
km_outputs,
thr=0.005):
parametric_images = []
for p in range(len(km_outputs)):
parametric_images.append(np.zeros(pet_image.shape[0:3]))
pet_image_fit = np.zeros(pet_image.shape)
thr = thr * np.amax(pet_image)
mask = np.argwhere(np.mean(pet_image, axis=-1) > thr)
for i in range(mask.shape[0]):
print(str(i) + '/' + str(mask.shape[0]))
tac = TAC(pet_image[mask[i][0], mask[i][1], mask[i][2], ], dt)
tac.run_model(model_name, model_inputs)
# #
plt.plot(tac.mft, tac.km_results['tacf'], 'r', tac.mft, tac.tac, 'go')
plt.show()
# #
pet_image_fit[mask[i][0], mask[i][1],
mask[i][2], ] = tac.km_results['tacf']
print(tac.km_results)
for p in range(len(km_outputs)):
parametric_images[p][mask[i][0], mask[i][1],
mask[i][2], ] = tac.km_results[
km_outputs[p].lower()]
parametric_images_dict = dict(zip(km_outputs, parametric_images))
return parametric_images_dict, pet_image_fit
def parametric_to_image(parametric_images_dict, dt, model, km_inputs):
parametric_images = list(parametric_images_dict.values())
pet_image = np.zeros(parametric_images[0].shape[0:3] + (dt.shape[-1], ))
mask = np.argwhere(parametric_images[0] != 0)
for i in range(mask.shape[0]):
km_inputs_local = km_inputs.copy()
for p in parametric_images_dict.keys():
km_inputs_local.update({
p.lower():
parametric_images_dict[p][mask[i][0], mask[i][1], mask[i][2]]
})
tac = TAC([], dt)
getattr(tac, 'run_' + model + '_para2tac')(**km_inputs_local)
pet_image[mask[i][0], mask[i][1],
mask[i][2], ] = tac.km_results['tacf']
return pet_image
7.29047317769810, 6.22241739146864, 5.38418292132310, 4.82308160452273,
4.51763401957080, 4.23271724360837, 3.99675706316514
])
tac = np.array([
0.0612937319754616, 2.56733416202755, 13.2302628562565, 17.5212376697891,
19.8554469827973, 21.2888518029975, 22.3164453083215, 21.9106339934451,
21.6976991361882, 21.2760967663021, 21.0320027737135, 20.3356078483447,
19.7789261620624, 18.7016942272702, 17.5381378584246, 16.6955628809652,
15.8305514461795, 15.1466577726934, 14.2137260024906, 13.7598553636470,
12.9989332998008, 11.8293518915139, 10.3648913297944, 8.93110113265639,
7.50503727314817, 6.49229487908344, 5.83027369667342, 5.35683342894270,
4.94744092837665, 4.79454967790696, 4.49907835991146
])
tac = TAC(tac, dt)
ref = Ref(ref, dt)
ref.interp_1()
w = dt2tdur(tac.dt)
w = w / np.amax(w)
beta_lim = [0.0100000 / 60, 0.300000 / 60]
n_beta = 40
b = basis.make_basis(ref.inputf1, dt, beta_lim=beta_lim, n_beta=n_beta, w=w)
# provide all user inputs in one dict here and later 'get_model_inputs' will select the needed ones
user_inputs = {
'dt': dt,
'inputf1': ref.inputf1,
'w': w,
'r1': 0.905,
'k2p': 0.00025,
user_inputs = {'dt': dt,
'inputf1': ref.inputf1cubic,
'w': None,
'r1': 0.905,
'k2p': 0.00025,
'beta_lim': beta_lim,
'n_beta': n_beta,
'b': b
}
user_inputs_fill_gaps = {'b': b_fill_gaps,
'inputf1_dt': inputf1_dt_fill_gaps
}
km_outputs = ['R1', 'k2', 'BP']
tac = TAC(ref.tac, dt)
for model_name in models:
model_inputs = get_model_inputs(user_inputs, model_name)
tac.run_model(model_name, model_inputs)
# make
tac.km_results.update(user_inputs_fill_gaps)
km_inputs = tac.km_results
model_km_inputs = get_model_inputs(km_inputs, model_name+ '_para2tac')
tac.run_model_para2tac(model_name + '_para2tac', model_km_inputs)
plt.plot(tac.mft, tac.tac, 'b*', label='tac')
plt.plot(dt2mft(dt_fill_gaps), tac.km_results['tacf'], 'r', label='fit')
plt.legend()
plt.show()