def srtmb_asl(tac, dt, inputf1, beta_lim, n_beta, w, r1):
b = basis.make_basis(inputf1, dt, beta_lim=beta_lim, n_beta=n_beta, w=w)
n_beta = b['beta'].size
ssq = np.zeros(n_beta)
if b['w'] is None:
b['w'] = 1
y = tac - r1*b['input']
for i in range(0,n_beta):
theta = r1
theta = np.append(theta, np.dot(y, b['basis'][i, :])/np.dot(b['basis'][i, :], b['basis'][i, :])) # w won't make a difference here
a = np.column_stack((b['input'], b['basis'][i, :]))
tacf = np.dot(a, theta)
res = (tac - tacf) * b['w'] # w works here
ssq[i] = np.sum(res ** 2)
i = np.argmin(ssq)
theta = r1
theta = np.append(theta, np.dot(y, b['basis'][i, :]) / np.dot(b['basis'][i, :], b['basis'][i, :]))
a = np.column_stack((b['input'], b['basis'][i, :]))
tacf = np.dot(a, theta)
theta = np.append(theta, b['beta'][i])
r1, k2, bp = kp.srtm_theta2kp(theta)
kps = {'r1': r1, 'k2': k2, 'bp': bp, 'tacf': tacf}
return kps
def srtmb_k2p(tac, dt, inputf1, beta_lim, n_beta, w, k2p):
b = basis.make_basis(inputf1,
dt,
beta_lim=beta_lim,
n_beta=n_beta,
w=w,
k2p=k2p)
kps = srtmb_k2p_basis(tac, b)
return kps
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,
'beta_lim': beta_lim,
'n_beta': n_beta,
'b': b,
'linear_phase_start': 500,
'linear_phase_end': None,
'fig': True
}
5696, 5996, 6296],
[5, 10, 15, 20, 25, 30, 40, 50, 60, 120, 180, 240, 300, 450, 600, 900, 1200, 1800, 2400, 3000, 3600,
5696, 5996, 6296, 6596]])
# ref
cer_right_GM = np.array([0, 0, 0, 0, 592.221900000000, 2487.12200000000, 4458.12800000000, 5239.16900000000, 5655.47800000000, 6740.88200000000, 7361.56300000000, 7315.28400000000, 7499.59700000000, 7067.78900000000, 6663.79200000000, 5921.15200000000, 5184.79900000000, 4268.11900000000, 3431.98000000000, 2886.08500000000, 2421.19200000000, 1687.55500000000, 1538.81800000000, 1440.42100000000, 1439.46900000000])
cer_left_GM = np.array([0, 0, 0, 0, 915.895900000000, 3751.55300000000, 5377.27800000000, 5896.48700000000, 6752.62900000000, 7299.80200000000, 7566.03600000000, 7440.07100000000, 7539.30500000000, 7271.21300000000, 6646.04300000000, 6109.30200000000, 5246.95700000000, 4447.90400000000, 3464.82700000000, 2863.01500000000, 2445.84400000000, 1658.95300000000, 1525.59300000000, 1382.73300000000, 1363.93900000000])
cer_GM = cer_right_GM/2 + cer_left_GM/2
ref = Ref(cer_GM, dt)
ref.interp_1cubic()
beta_lim = [0.0100000/60, 0.300000/60]
n_beta = 40
k2p = 0.00025
b = basis.make_basis(ref.inputf1cubic, dt, beta_lim=beta_lim, n_beta=n_beta, w=None, k2p=k2p)
dt_fill_gaps = dt_fill_gaps(dt)
b_fill_gaps = basis.make_basis(ref.inputf1cubic, dt_fill_gaps, beta_lim=beta_lim, n_beta=n_beta, w=None, k2p=k2p)
inputf1_dt_fill_gaps = ref.inputf1cubic, dt_fill_gaps
models = ['srtmb_basis', 'srtm', 'srtmb_k2p_basis']
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
def srtmb(tac, dt, inputf1, beta_lim, n_beta, w):
tac[tac < 0] = 0.0
b = basis.make_basis(inputf1, dt, beta_lim=beta_lim, n_beta=n_beta, w=w)
kps = srtmb_basis(tac, b)
return kps
def kinetic_model(src,
dst=None,
params=None,
model='srtmb_basis',
w=None,
r1=0.905,
k2p=0.000250,
beta_lim=None,
n_beta=40,
linear_phase_start=500,
linear_phase_end=None,
km_outputs=None,
thr=0.1,
fig=False):
"""
Args:
src (Path or str): input patient directory or filename
dst (Path or str): output directory (default: `src` directory)
params (Path or str): config (relative to `src` directory)
model (str): srtmb_basis, srtmb_k2p_basis, srtmb_asl_basis, logan_ref, logan_ref_k2p,
mrtm, mrtm_k2p
"""
import nibabel as nib
import numpy as np
from niftypad import basis
from niftypad.image_process.parametric_image import image_to_parametric
from niftypad.models import get_model_inputs
from niftypad.tac import Ref
src_path = Path(src)
if src_path.is_dir():
fpath = next(src_path.glob('*.nii'))
else:
fpath = src_path
src_path = fpath.parent
log.debug("file:%s", fpath)
if dst is None:
dst_path = src_path
else:
dst_path = Path(dst)
assert dst_path.is_dir()
meta = readers.find_meta(src_path, filter(None, [params, fpath.stem]))
dt = np.asarray(meta['dt'])
ref = np.asarray(meta['ref'])
ref = Ref(ref, dt)
ref.interp_1cubic()
log.debug("looking for first `*.nii` file in %s", src_path)
img = nib.load(fpath)
# pet_image = img.get_fdata(dtype=np.float32)
pet_image = np.asanyarray(img.dataobj)
# basis functions
if beta_lim is None:
beta_lim = [0.01 / 60, 0.3 / 60]
b = basis.make_basis(ref.inputf1cubic,
dt,
beta_lim=beta_lim,
n_beta=n_beta,
w=w,
k2p=k2p)
if km_outputs is None:
km_outputs = ['R1', 'k2', 'BP']
user_inputs = {
'dt': dt,
'inputf1': ref.inputf1cubic,
'w': w,
'r1': r1,
'k2p': k2p,
'beta_lim': beta_lim,
'n_beta': n_beta,
'b': b,
'linear_phase_start': linear_phase_start,
'linear_phase_end': linear_phase_end,
'fig': fig
}
model_inputs = get_model_inputs(user_inputs, model)
# log.debug("model_inputs:%s", model_inputs)
parametric_images_dict, pet_image_fit = image_to_parametric(pet_image,
dt,
model,
model_inputs,
km_outputs,
thr=thr)
for kp in parametric_images_dict:
nib.save(nib.Nifti1Image(parametric_images_dict[kp], img.affine),
f"{dst_path / fpath.stem}_{model}_{kp}_{fpath.suffix}")
nib.save(nib.Nifti1Image(pet_image_fit, img.affine),
f"{dst_path / fpath.stem}_{model}_fit_{fpath.suffix}")
