def obtain_tissue_tmax(self):
cbf_seq = np.zeros((256 * 256))
tmax_seq = np.zeros((256 * 256))
comb_seq = np.zeros((256 * 256))
for ii in tqdm(range(256 * 256)):
y = int(ii // 256)
x = int(ii % 256)
if self.brain_mask[y, x] == 0:
continue
tissue_lst = [0] * len(self.img_lst)
for idx, img in enumerate(self.images):
tissue_lst[idx] = get_ct_value_neighbor_avg(
img, x, y, self.brain_mask, self.img_mask, g_d)
array_ct_value_tissue = np.array(tissue_lst)
array_ct_value_tissue_bl = baseline_process(
array_ct_value_tissue, g_std_t)
array_ct_value_tissue_bl_f = filter_lp(array_ct_value_tissue,
cutoff_l=None,
cutoff_h=0.41,
ftype="lowpass")
final_signal_tissue, base_tissue = baseline_correction(
array_ct_value_tissue_bl_f)
tmax_seq[ii] = np.argmax(final_signal_tissue)
cbf_seq[ii] = final_signal_tissue.max()
comb_seq[ii] = tmax_seq[ii] * cbf_seq[ii]
return tmax_seq.reshape((256, 256)), cbf_seq.reshape(
(256, 256)), comb_seq.reshape((256, 256))
def get_tissue_signal(self):
output = np.zeros((256, 256, len(self.img_lst)))
for ii in tqdm(range(256 * 256)):
y_t_i = int(ii // 256)
x_t_i = int(ii % 256)
if self.brain_mask[y_t_i, x_t_i] == 0:
continue
length = len(self.img_lst)
tissue_lst = [0] * length
for idx, img in enumerate(self.images):
tissue_lst[idx] = get_ct_value_neighbor_avg(
img, x_t_i, y_t_i, self.brain_mask, self.img_mask, g_d)
array_ct_value_tissue = np.array(tissue_lst)
array_ct_value_tissue_bl = baseline_process(
array_ct_value_tissue, g_std_t)
self.signal_taken[y_t_i,
x_t_i, :] = array_ct_value_tissue_bl.copy()
# array_ct_value_tissue_bl_f = filter_lp(array_ct_value_tissue_bl, cutoff_l=None, cutoff_h=0.41,
# ftype="lowpass")
# try:
# final_signal_tissue, base_tissue = baseline_correction(array_ct_value_tissue_bl_f)
# output[y_t_i, x_t_i, :] = final_signal_tissue
# except:
# pass
return output
def compute_irf_time(self, index):
'''
Input:
index:the index of pixel in flat image
isPad: expand time axis for extensive distribution of Tmax
output: Tmax of irf
Describe:
if the time of peak in tissue is after the time of peak in aif, tmax = tissue_peak_time - aif_peak_time
else tmax = total_time - (aif_peak_time - tissue_peak_time)
'''
y_t_i = int(index // 256)
x_t_i = int(index % 256)
if self.brain_mask[y_t_i, x_t_i] == 0:
return
length = len(self.final_signal_aif)
tissue_lst = [0] * length
for idx, img in enumerate(self.images):
tissue_lst[idx] = get_ct_value_neighbor_avg(
img, x_t_i, y_t_i, self.brain_mask, self.img_mask, g_d)
array_ct_value_tissue = np.array(tissue_lst)
array_ct_value_tissue_bl = baseline_process(array_ct_value_tissue,
g_std_t)
array_ct_value_tissue_bl_f = filter_lp(array_ct_value_tissue,
cutoff_l=None,
cutoff_h=0.41,
ftype="lowpass")
final_signal_tissue, base_tissue = baseline_correction(
array_ct_value_tissue_bl_f)
aif_delay = np.min(np.where(self.final_signal_aif > 0))
tissue_delay = np.min(np.where(final_signal_tissue > 0))
irf_delay = abs(tissue_delay - aif_delay)
aif_peak = np.argmax(self.final_signal_aif)
tissue_peak = np.argmax(final_signal_tissue)
max_aif = np.max(self.final_signal_aif)
self.cbf_img[y_t_i,
x_t_i] = (1 / max_aif) * np.max(final_signal_tissue)
self.cbv_img[y_t_i, x_t_i] = trapz(
final_signal_tissue, np.arange(len(final_signal_tissue)),
dx=1) / trapz(self.final_signal_aif,
np.arange(len(self.final_signal_aif)),
dx=1)
if tissue_peak >= aif_peak:
self.irf_img[y_t_i, x_t_i] += (tissue_peak - aif_peak)
def compute_irf(self, index):
'''
Input:
index:the index of pixel in flat image
isPad: expand time axis for extensive distribution of Tmax
Output: Tmax of irf
Describe:
Compute irf using Tikhonov svd
'''
y_t_i = int(index // 256)
x_t_i = int(index % 256)
if self.brain_mask[y_t_i, x_t_i] == 0:
return
tissue_lst = [0] * len(self.img_lst)
for idx, img in enumerate(self.images):
tissue_lst[idx] = get_ct_value_neighbor_avg(
img, x_t_i, y_t_i, self.brain_mask, self.img_mask, g_d)
array_ct_value_tissue = np.array(tissue_lst)
array_ct_value_tissue_bl = baseline_process(array_ct_value_tissue,
g_std_t)
array_ct_value_tissue_bl_f = filter_lp(array_ct_value_tissue,
cutoff_l=None,
cutoff_h=0.41,
ftype="lowpass")
final_signal_tissue, base_tissue = baseline_correction(
array_ct_value_tissue_bl_f)
residual_func = deconvolution(self.final_signal_aif,
final_signal_tissue)
t_idx = np.argmax(residual_func)
self.irf_img[y_t_i, x_t_i] += t_idx
self.cbf_img[y_t_i, x_t_i] += np.max(residual_func)