def test_srtm_zhou2003_voxelwise(self):
results_img = SRTM_Zhou2003.volume_wrapper(
ti=self.ti,
refRegionMaskFile=self.refRegionMaskFile,
startActivity=self.startActivity,
weights=self.weights,
fwhm=self.fwhm)
'''
axes[1].set_ylabel('R1')
axes[1].legend()
# In[12]:
import temporalimage
ti = temporalimage.TemporalImage(TAC_img,
np.eye(4),
t - dt / 2,
t + dt / 2,
time_unit='min')
# In[13]:
results_img = SRTM_Zhou2003.volume_wrapper(ti=ti,
refTAC=refTAC,
fwhm=(2 * np.sqrt(2 * np.log(2))) *
5)
# In[14]:
results_img.keys()
# In[15]:
results_img['BP']
# In[16]:
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
axes[0].plot(results_img['BP'].flatten(), '.', label='Zhou 2003')
local_install_path = 'H:\gitrepos\kineticmodel'
data_path = local_install_path + '/data/nru/'
# loading real volumetric data
inputTac_filename = os.path.join(data_path, 'input.mni305.2mm.sm6.nii.gz')
cerebMask_filename = os.path.join(data_path, 'cereb.mni305.2mm.nii.gz')
tim_filename = os.path.join(data_path, 'info_tim.csv')
inputTac = nib.load(inputTac_filename)
cerebMask = nib.load(cerebMask_filename)
# using Zhou's algorithm to run it
# Note: The srtm Lammertsmaa implementation is very slow in the volume and hence not shown here
results_img = SRTM_Zhou2003.volume_wrapper(
timeSeriesImgFile=inputTac_filename,
frameTimingCsvFile=tim_filename,
refRegionMaskFile=cerebMask_filename,
time_unit='s',
startActivity='flat',
fwhm=(2 * np.sqrt(2 * np.log(2))) * 5)
# In[2]:
results_img.keys()
BP_img = results_img['BP']
import matplotlib.pyplot as plt
get_ipython().magic('matplotlib inline')
plt.imshow(BP_img[:, :, 20])
# In[3]: