def ec_select(mrs_data):
"""
"""
try:
## work out whether eddy current correction is necessary based on TE
if mrs_data.te < 288:
root = Tkinter.Tk()
root.geometry('0x0+0+0')
root.attributes("-topmost", 1)
root.withdraw()
mrs_data.eddy = tkMessageBox.askyesno(
'ECC', 'Perform an eddy current correction?', parent=root)
if mrs_data.eddy == True:
try:
# get path to water directory for eddy current correction
mrs_data.h2o_dir = tkFileDialog.askopenfilename(
parent=root, initialdir=mrs_data.dcm_dir)
water_data = mrs_struct(dicom.read_file(mrs_data.h2o_dir))
# mrs_data.ecc_cx=mrs_data.raw_cx*np.exp(-1.0j*np.angle(water_data.raw_cx))
except:
mrs_data.eddy = 0
else:
mrs_data.eddy = 0
root.attributes("-topmost", 0)
root.destroy()
else:
mrs_data.eddy = 0
return (mrs_data, water_data)
except:
print("Error selecting water file for eddy current correction!")
def water_t2_corr_csf():
# Make a top-level instance and hide in top left corner, get filepath
root = Tkinter.Tk()
root.geometry('0x0+0+0')
root.attributes("-topmost", 1)
root.withdraw()
water_list = tkFileDialog.askopenfilenames(parent=root)
water_dir = os.path.split(water_list[0])[0]
os.chdir(water_dir)
root.attributes("-topmost", 0)
root.destroy()
water_data = {}
tr_list = np.zeros(shape=len(water_list))
te_list = np.zeros(shape=len(water_list))
amp_list = np.zeros(shape=len(water_list))
fig1, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)
for w in range(0, len(water_list)):
water_data[w] = mrs_struct(dicom.read_file(water_list[w]))
tr_list[w] = water_data[w].tr
te_list[w] = water_data[w].te
amp_list[w] = np.mean(
np.abs(
hlsvdquant(water_data[w], nc_w,
np.arange(0, water_data[w].nsa))[0]))
ax1.plot(np.arange(0, water_data[w].pts),
np.abs(water_data[w].raw_cx[0, :]))
ax1.set_ylim(bottom=0)
ax1.set_xlim(left=0)
# fig2, ax2 = plt.subplots()
ax2.scatter(te_list, amp_list)
m0_1_est, t2_1_est, m0_2_est = np.square(
curve_fit(t2_curve_biexp,
te_list,
amp_list,
p0=[
np.sqrt(np.max(amp_list)), 10.0,
np.sqrt(np.max(amp_list / 100.0))
])[0])
ax2.plot(
np.arange(0, 1500),
t2_curve_biexp(np.arange(0, 1500), np.sqrt(m0_1_est),
np.sqrt(t2_1_est), np.sqrt(m0_2_est)))
ax2.set_ylim(bottom=0)
ax2.set_xlim(left=0)
return (m0_1_est, t2_1_est, m0_2_est)
def water_t2_corr_phantom():
# Make a top-level instance and hide in top left corner, get filepath
root = Tkinter.Tk()
root.geometry('0x0+0+0')
root.attributes("-topmost", 1)
root.withdraw()
water_list = tkFileDialog.askopenfilenames(parent=root)
water_dir = os.path.split(water_list[0])[0]
os.chdir(water_dir)
root.attributes("-topmost", 0)
root.destroy()
water_data = {}
tr_list = np.zeros(shape=len(water_list))
te_list = np.zeros(shape=len(water_list))
amp_list = np.zeros(shape=len(water_list))
for w in range(0, len(water_list)):
water_data[w] = mrs_struct(dicom.read_file(water_list[w]))
tr_list[w] = water_data[w].tr
te_list[w] = water_data[w].te
amp_list[w] = np.mean(
np.abs(
hlsvdquant(water_data[w], nc_w,
np.arange(0, water_data[w].nsa))[0]))
plt.scatter(te_list, amp_list)
m0_est, t2_est = np.square(
curve_fit(t2_curve_mono,
te_list,
amp_list,
p0=[np.sqrt(np.max(amp_list)), 50])[0])
plt.plot(
np.arange(0, 1500),
t2_curve_mono(np.arange(0, 1500), np.sqrt(m0_est), np.sqrt(t2_est)))
axes = plt.gca()
axes.set_xlim(0, np.max(te_list))
return (m0_est, t2_est)
def mrs_save(mrs_data):
""" SAVE SELECTED RAW SPECTRA TO A NEW DICOM & PROCESSED (FILTERED) DATA TO .RAW FILES
Both of these are placed in a folder as a 'sister directory' i.e. the
_MC_PROCESSED folder is in the parent directory of the DICOM file
"""
try:
## work out whether eddy current correction is necessary based on TE
if mrs_data.te < 288:
mrs_data.eddy = tkMessageBox.askyesno(
'ECC', 'Perform an eddy current correction?')
if mrs_data.eddy == True:
try:
# get path to water directory for eddy current correction
mrs_data.h2o_dir = tkFileDialog.askopenfilename(
initialdir=mrs_data.dcm_dir)
water_data = mrs_struct(mrs_data.h2o_dir)
##Create a new window to display the effects of water filtering
frame = Tkinter.Toplevel()
ecc_select_GUI(frame, water_data)
### EDDY DIALOGUE BOX TO SELECT AVERAGES ###
### EDDY DIALOGUE BOX TO SELECT AVERAGES ###
### EDDY DIALOGUE BOX TO SELECT AVERAGES ###
### EDDY DIALOGUE BOX TO SELECT AVERAGES ###
mrs_data.ecc_cx = mrs_data.raw_cx * np.exp(
-1.0j * np.angle(water_data.raw_cx))
except:
mrs_data.eddy = 0
else:
mrs_data.eddy = 0
else:
mrs_data.eddy = 0
## save data as raw files
except AttributeError:
print('PROBLEM SAVING FILE!')
def dicom_master(self):
##Hide any old plots and clear them from memory
for sub in range(0, numpy.size(self.plotlist)):
self.plotlist[sub].set_visible(
False) #otherwise 'burned' onto canvas
self.plotlist = []
print('---CLEARED PREVIOUS CASES FROM ANALYSIS WINDOW---')
##Read the new DICOM file dataset
dcm_dir = tkFileDialog.askopenfilename(parent=self.master)
dcm = dicom.read_file(dcm_dir, force=True)
os.chdir(os.path.split(dcm_dir)[0])
print('CURRENTLY ANALYSING -----> ' + os.path.split(dcm_dir)[1])
##Create an mrs data structure as defined in mrs_format module
self.mrs_data = mrs_struct(dcm)
self.mrs_data.dcm_dir = dcm_dir
##Repopulate the list of plots with the new dataset
for sub in range(0, self.mrs_data.nsa):
l, = self.splt.plot(
self.mrs_data.ppmscale,
numpy.real(
numpy.fft.fftshift(
numpy.fft.fft(
self.mrs_data.raw_cx[sub, :] *
numpy.exp(1j * self.stheta.get() -
self.sapod.get() * self.mrs_data.fscale /
self.mrs_data.bw)))),
lw=2,
visible=False)
self.plotlist.insert(sub, l)
##Repopulate the listbox with the subspectra labels
self.listbox.delete(0, Tkinter.END)
for item in range(0, self.mrs_data.nsa):
self.listbox.insert(Tkinter.END, str(item))
##Clear the current canvas to blank
self.fig.canvas.draw()
def water_t1_corr():
# Make a top-level instance and hide in top left corner, get filepath
root = Tkinter.Tk()
root.geometry('0x0+0+0')
root.attributes("-topmost", 1)
root.withdraw()
water1 = tkFileDialog.askopenfilename(
parent=root, title='CHOOSE WATER DICOM WITH TR=1500')
water_dir = os.path.split(water1)[0]
os.chdir(water_dir)
res_dir = water_dir + '_Results'
water2 = tkFileDialog.askopenfilename(
parent=root, title='CHOOSE WATER DICOM WITH TR=5000')
root.attributes("-topmost", 0)
root.destroy()
if not os.path.exists(res_dir):
os.makedirs(res_dir)
water_data = {}
td_list = np.zeros(shape=2)
amp_list = np.zeros(shape=2)
water_data[0] = mrs_struct(dicom.read_file(water1))
water_data[1] = mrs_struct(dicom.read_file(water2))
frame1 = Tkinter.Toplevel()
water_select_GUI(frame1, water_data[0])
frame1.mainloop()
frame2 = Tkinter.Toplevel()
water_select_GUI(frame2, water_data[1])
frame2.mainloop()
for w in range(0, 2):
amp_list[w] = np.sum(
np.abs(hlsvdquant(water_data[w], nc_w,
water_data[w].vals)[0])) / np.size(
water_data[w].vals)
# print(amp_list[w])
td_list = [1470, 4970]
plt.scatter(td_list, amp_list)
m0_est, t1_est = np.square(
curve_fit(t1_curve,
td_list,
amp_list,
p0=[np.max(amp_list), np.sqrt(1500)])[0])
plt.plot(np.arange(0, 10000),
t1_curve(np.arange(0, 10000), np.sqrt(m0_est), np.sqrt(t1_est)))
axes = plt.gca()
axes.set_xlim(0, 10000)
axes.set_ylim(0, m0_est)
axes.set_ylabel('Water Signal Intensity')
axes.set_xlabel('Repetition Time (ms)')
axes.set_title('Water Relaxation Curve')
resfile = os.path.abspath(res_dir + '/water_table.txt')
with open(resfile, 'w') as f:
f.write('Water Fit Results\n')
f.write('Water@TR1500, ' + str(amp_list[0]) + '\n')
f.write('Water@TR5000, ' + str(amp_list[1]) + '\n')
f.write('Water@TRinf, ' + str(m0_est) + '\n')
f.write('Water T1, ' + str(t1_est) + '\n')
f.write('\n')