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ROI_analysis.py
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from tkinter import *
import os
from tkinter import ttk
import tkinter.filedialog as tkFileDialog
from tkinter import messagebox
import myfunctions
import webbrowser
import time
import threading
import subprocess
import shutil
root = Tk()
root.geometry("500x395+950+250")
root.resizable(False, False)
root.title("ROI analysis")
#___Variables
var_pet_address_input = StringVar(value='Not defined...')
var_pet_address_output = StringVar(value='Not defined...')
var_injected_dose = StringVar(value='1')
var_body_weight = StringVar(value='80')
var_type_of_measurement = StringVar(value='Using body weight')
var_type_of_SUV = StringVar(value='SUV mean')
var_height = StringVar(value='1.8')
var_SUV_reference = StringVar(value='Cerebellum')
var_atlas = StringVar(value='Harvard Oxford atlas')
#___commands
def btn_enter_pet_input_command():
path = myfunctions.get_address_folder()
var_pet_address_input.set(path)
var_pet_address_output.set(var_pet_address_input.get() + '/ROI_analysis')
def btn_enter_pet_output_command():
path = myfunctions.get_address_folder()
var_pet_address_output.set(path)
def btn_open_folder_command():
webbrowser.open(var_pet_address_output.get())
def btn_process_command():
if var_pet_address_input.get() == "Not defined...":
messagebox.showinfo("Error...", "Please fill the PET image address")
elif var_pet_address_input.get() == "":
messagebox.showinfo("Error...", "Please fill the PET image address")
elif var_pet_address_output.get() == "Not defined...":
messagebox.showinfo("Error...", "Please fill the Output address")
elif var_pet_address_output.get() == "":
messagebox.showinfo("Error...", "Please fill the Output address")
elif var_injected_dose.get() == "":
messagebox.showinfo("Error...", "Please fill the injected dose")
elif var_body_weight.get() == "":
messagebox.showinfo("Error...", "Please fill the body weight")
else:
if var_atlas.get() == 'Harvard Oxford atlas':
dirname = var_pet_address_input.get()
os.system('mkdir ' + dirname + '/ROI_analysis')
shutil.rmtree(var_pet_address_output.get()) #remove the folder to delete previous files
os.system('mkdir ' + dirname + '/ROI_analysis')
os.system('mkdir ' + dirname + '/ROI_analysis/Cortical_images')
os.system('mkdir ' + dirname + '/ROI_analysis/Subcortical_images')
os.system('mkdir ' + dirname + '/ROI_analysis/Cortical_normalized_images')
os.system('mkdir ' + dirname + '/ROI_analysis/Subcortical_normalized_images')
file_SUV_cortical_mean = open(var_pet_address_output.get() + '/SUV_cortical_mean.txt', 'w')
file_SUV_cortical_mean.close()
file_SUV_subcortical_mean = open(var_pet_address_output.get() + '/SUV_subcortical_mean.txt', 'w')
file_SUV_subcortical_mean.close()
file_SUVR_cortical_mean = open(var_pet_address_output.get() + '/SUVR_cortical_mean.txt', 'w')
file_SUVR_cortical_mean.close()
file_SUVR_subcortical_mean = open(var_pet_address_output.get() + '/SUVR_subcortical_mean.txt', 'w')
file_SUVR_subcortical_mean.close()
file_SUV_cortical_max = open(var_pet_address_output.get() + '/SUV_cortical_max.txt', 'w')
file_SUV_cortical_max.close()
file_SUV_subcortical_max = open(var_pet_address_output.get() + '/SUV_subcortical_max.txt', 'w')
file_SUV_subcortical_max.close()
file_SUVR_cortical_max = open(var_pet_address_output.get() + '/SUVR_cortical_max.txt', 'w')
file_SUVR_cortical_max.close()
file_SUVR_subcortical_max = open(var_pet_address_output.get() + '/SUVR_subcortical_max.txt', 'w')
file_SUVR_subcortical_max.close()
file_SUV_cortical_min = open(var_pet_address_output.get() + '/SUV_cortical_min.txt', 'w')
file_SUV_cortical_min.close()
file_SUV_subcortical_min = open(var_pet_address_output.get() + '/SUV_subcortical_min.txt', 'w')
file_SUV_subcortical_min.close()
file_SUVR_cortical_min = open(var_pet_address_output.get() + '/SUVR_cortical_min.txt', 'w')
file_SUVR_cortical_min.close()
file_SUVR_subcortical_min = open(var_pet_address_output.get() + '/SUVR_subcortical_min.txt', 'w')
file_SUVR_subcortical_min.close()
file_SUV_cortical_sd = open(var_pet_address_output.get() + '/SUV_cortical_sd.txt', 'w')
file_SUV_cortical_sd.close()
file_SUV_subcortical_sd = open(var_pet_address_output.get() + '/SUV_subcortical_sd.txt', 'w')
file_SUV_subcortical_sd.close()
file_SUVR_cortical_sd = open(var_pet_address_output.get() + '/SUVR_cortical_sd.txt', 'w')
file_SUVR_cortical_sd.close()
file_SUVR_subcortical_sd = open(var_pet_address_output.get() + '/SUVR_subcortical_sd.txt', 'w')
file_SUVR_subcortical_sd.close()
file_volume_cortical = open(var_pet_address_output.get() + '/Cortical_volume.txt', 'w')
file_volume_cortical.close()
file_volume_subcortical = open(var_pet_address_output.get() + '/Subcortical_volume.txt', 'w')
file_volume_subcortical.close()
os.system('cp Areas_Cortical.txt ' + var_pet_address_output.get())
os.system('cp Areas_Subcortical.txt ' + var_pet_address_output.get())
os.system('cp headers_cortical.txt ' + var_pet_address_output.get())
os.system('cp headers_subcortical.txt ' + var_pet_address_output.get())
elif var_atlas.get() == 'CortexID Suite atlas':
dirname = var_pet_address_input.get()
os.system('mkdir ' + dirname + '/ROI_analysis')
shutil.rmtree(var_pet_address_output.get()) #remove the folder to delete previous files
os.system('mkdir ' + dirname + '/ROI_analysis')
os.system('mkdir ' + dirname + '/ROI_analysis/images')
os.system('mkdir ' + dirname + '/ROI_analysis/normalized_images')
file_SUV_cortical_mean = open(var_pet_address_output.get() + '/SUV_mean.txt', 'w')
file_SUV_cortical_mean.close()
file_SUVR_cortical_mean = open(var_pet_address_output.get() + '/SUVR_mean.txt', 'w')
file_SUVR_cortical_mean.close()
file_SUV_cortical_max = open(var_pet_address_output.get() + '/SUV_max.txt', 'w')
file_SUV_cortical_max.close()
file_SUVR_cortical_max = open(var_pet_address_output.get() + '/SUVR_max.txt', 'w')
file_SUVR_cortical_max.close()
file_SUV_cortical_min = open(var_pet_address_output.get() + '/SUV_min.txt', 'w')
file_SUV_cortical_min.close()
file_SUVR_cortical_min = open(var_pet_address_output.get() + '/SUVR_min.txt', 'w')
file_SUVR_cortical_min.close()
file_SUV_cortical_sd = open(var_pet_address_output.get() + '/SUV_sd.txt', 'w')
file_SUV_cortical_sd.close()
file_SUVR_cortical_sd = open(var_pet_address_output.get() + '/SUVR_sd.txt', 'w')
file_SUVR_cortical_sd.close()
file_volume_cortical = open(var_pet_address_output.get() + '/Volume.txt', 'w')
file_volume_cortical.close()
os.system('cp CortexID_areas.txt ' + var_pet_address_output.get())
os.system('cp CortexID_headers.txt ' + var_pet_address_output.get())
if var_SUV_reference.get() == 'Cerebellum':
os.system('fslmaths ' + var_pet_address_input.get() + '/graymatter_mask_std' + ' -mul atlas_cortical/Cerebellum.nii.gz ' + \
var_pet_address_input.get() + '/temp_img.nii.gz')
#os.system('fslmaths ' + var_pet_address_output.get() + '/temp_img.nii.gz -bin ' + var_pet_address_output.get() + \
# '/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz -mul ' + var_pet_address_input.get() + \
'/temp_img.nii.gz ' + var_pet_address_input.get() + '/PET_Cerebellum.nii.gz ')
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_Cerebellum.nii.gz -M')
L = len(x1)
var_RA_refrence_mean = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_Cerebellum.nii.gz -P 0')
L = len(x1)
var_RA_refrence_min = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_Cerebellum.nii.gz -P 100')
L = len(x1)
var_RA_refrence_max = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_Cerebellum.nii.gz -S')
L = len(x1)
var_RA_reference_sd = x1[0:L-1]
elif var_SUV_reference.get() == 'Cerebral cortex':
os.system('fslmaths ' + var_pet_address_input.get() + '/graymatter_mask_std' + ' -mul atlas_subcortical/cerebral_cortex_bin.nii.gz ' + \
var_pet_address_input.get() + '/temp_img.nii.gz')
# os.system('fslmaths ' + var_pet_address_output.get() + '/temp_img.nii.gz -bin ' + var_pet_address_output.get() + \
# '/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz -mul ' + var_pet_address_input.get() + \
'/temp_img.nii.gz ' + var_pet_address_input.get() + '/PET_Cerebral_cortex.nii.gz ')
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_Cerebral_cortex.nii.gz -M')
L = len(x1)
var_RA_refrence_mean = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_Cerebral_cortex.nii.gz -P 0')
L = len(x1)
var_RA_refrence_min = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_Cerebral_cortex.nii.gz -P 100')
L = len(x1)
var_RA_refrence_max = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_Cerebral_cortex.nii.gz -S')
L = len(x1)
var_RA_reference_sd = x1[0:L-1]
elif var_SUV_reference.get() == 'Pons':
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz' + ' -mul atlas_cortical/Pons.nii.gz ' + \
var_pet_address_input.get() + '/PET_Pons.nii.gz')
# os.system('fslmaths ' + var_pet_address_output.get() + '/temp_img.nii.gz -bin ' + var_pet_address_output.get() + \
# '/temp_img.nii.gz')
# os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz -mul ' + var_pet_address_output.get() + \
# '/temp_img.nii.gz ' + var_pet_address_output.get() + '/PET_Pons.nii.gz ')
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_Pons.nii.gz -M')
L = len(x1)
var_RA_refrence_mean = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_Pons.nii.gz -P 0')
L = len(x1)
var_RA_refrence_min = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_Pons.nii.gz -P 100')
L = len(x1)
var_RA_refrence_max = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_Pons.nii.gz -S')
L = len(x1)
var_RA_reference_sd = x1[0:L-1]
elif var_SUV_reference.get() == 'Global gray matter':
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz' + ' -mul atlas_GE/global.nii.gz ' + \
var_pet_address_input.get() + '/PET_global.nii.gz')
# os.system('fslmaths ' + var_pet_address_output.get() + '/temp_img.nii.gz -bin ' + var_pet_address_output.get() + \
# '/temp_img.nii.gz')
# os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz -mul ' + var_pet_address_output.get() + \
# '/temp_img.nii.gz ' + var_pet_address_output.get() + '/PET_global.nii.gz ')
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_global.nii.gz -M')
L = len(x1)
var_RA_refrence_mean = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_global.nii.gz -P 0')
L = len(x1)
var_RA_refrence_min = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_global.nii.gz -P 100')
L = len(x1)
var_RA_refrence_max = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_input.get() + '/PET_global.nii.gz -S')
L = len(x1)
var_RA_reference_sd = x1[0:L-1]
os.system('echo ')
os.system('echo ----------------ROI Analysis----------------')
os.system('echo ')
if var_atlas.get() == 'Harvard Oxford atlas':
os.system('echo ---Calculating and saving SUV, SUVR, and cortical volume for 48 cortical areas according to the Harvard-Oxford atlas')
# Measure SUV coefficient
BMI = float(var_body_weight.get()) / (float(var_height.get()) * float(var_height.get()))
if var_type_of_measurement.get() == 'Using body weight':
x = (float(var_body_weight.get()) * 1000)
elif var_type_of_measurement.get() == 'Using LBM (male)':
x = (9270 * (float(var_body_weight.get()) * 1000)) / (6680 + (216*BMI))
elif var_type_of_measurement.get() == 'Using LBM (female)':
x = (9270 * (float(var_body_weight.get()) * 1000)) / (8780 + (244*BMI))
coefficient = x / float(var_injected_dose.get())
#Save SUV PET image for further visualization
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz -mul ' + str(coefficient) + ' ' + \
var_pet_address_output.get() + '/SUV.nii.gz')
#devide PET image by the reference mean to measure SUVR
var_SUV_refrence_mean = float(var_RA_refrence_mean) / (float(var_injected_dose.get()) / x)
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz -div ' + str(var_SUV_refrence_mean) + ' ' + var_pet_address_input.get() + '/PET_brain_normalized_std.nii.gz')
#Save SUVR PET image for further visualization
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_normalized_std.nii.gz -mul ' + str(coefficient) + ' ' + \
var_pet_address_output.get() + '/SUVR.nii.gz')
for i in range (1,97):
# Measure SUV
os.system('fslmaths ' + var_pet_address_input.get() + '/graymatter_mask_std -mul atlas_cortical/' + str(i) + '.nii.gz ' + \
var_pet_address_output.get() + '/Cortical_images/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz -mul ' + var_pet_address_output.get() + \
'/Cortical_images/temp_img.nii.gz ' + var_pet_address_output.get() + '/Cortical_images/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_output.get() + '/Cortical_images/temp_img.nii.gz -mul ' + str(coefficient) + \
' ' + var_pet_address_output.get() + '/Cortical_images/' + str(i) + '.nii.gz')
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Cortical_images/' + str(i) + '.nii.gz -M')
L = len(x1)
var_SUV_cortical_mean = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Cortical_images/' + str(i) + '.nii.gz -P 0')
L = len(x1)
var_SUV_cortical_min = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Cortical_images/' + str(i) + '.nii.gz -P 100')
L = len(x1)
var_SUV_cortical_max = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Cortical_images/' + str(i) + '.nii.gz -S')
L = len(x1)
var_SUV_cortical_sd = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Cortical_images/' + str(i) + '.nii.gz -V')
L = len(x1)
x2 = x1[0:L-1]
var_ROI_cortical_volume = x2.rsplit(' ', 1)[1]
file_SUV_cortical_mean = open(var_pet_address_output.get() + '/SUV_cortical_mean.txt', 'a')
file_SUV_cortical_mean.write(str(var_SUV_cortical_mean))
if i != 96:
file_SUV_cortical_mean.write('\n')
file_SUV_cortical_mean.close()
file_SUV_cortical_max = open(var_pet_address_output.get() + '/SUV_cortical_max.txt', 'a')
file_SUV_cortical_max.write(str(var_SUV_cortical_max))
if i != 96:
file_SUV_cortical_max.write('\n')
file_SUV_cortical_max.close()
file_SUV_cortical_min = open(var_pet_address_output.get() + '/SUV_cortical_min.txt', 'a')
file_SUV_cortical_min.write(str(var_SUV_cortical_min))
if i != 96:
file_SUV_cortical_min.write('\n')
file_SUV_cortical_min.close()
file_SUV_cortical_sd = open(var_pet_address_output.get() + '/SUV_cortical_sd.txt', 'a')
file_SUV_cortical_sd.write(str(var_SUV_cortical_sd))
if i != 96:
file_SUV_cortical_sd.write('\n')
file_SUV_cortical_sd.close()
file_cortical_volume = open(var_pet_address_output.get() + '/Cortical_volume.txt', 'a')
file_cortical_volume.write(str(var_ROI_cortical_volume))
if i != 96:
file_cortical_volume.write('\n')
file_cortical_volume.close()
# Measure SUVR
os.system('fslmaths ' + var_pet_address_input.get() + '/graymatter_mask_std -mul atlas_cortical/' + str(i) + '.nii.gz ' + \
var_pet_address_output.get() + '/Cortical_normalized_images/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_normalized_std.nii.gz -mul ' + var_pet_address_output.get() + \
'/Cortical_normalized_images/temp_img.nii.gz ' + var_pet_address_output.get() + '/Cortical_normalized_images/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_output.get() + '/Cortical_normalized_images/temp_img.nii.gz -mul ' + str(coefficient) + \
' ' + var_pet_address_output.get() + '/Cortical_normalized_images/' + str(i) + '.nii.gz')
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Cortical_normalized_images/' + str(i) + '.nii.gz -M')
L = len(x1)
var_SUVR_cortical_mean = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Cortical_normalized_images/' + str(i) + '.nii.gz -P 0')
L = len(x1)
var_SUVR_cortical_min = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Cortical_normalized_images/' + str(i) + '.nii.gz -P 100')
L = len(x1)
var_SUVR_cortical_max = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Cortical_normalized_images/' + str(i) + '.nii.gz -S')
L = len(x1)
var_SUVR_cortical_sd = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Cortical_normalized_images/' + str(i) + '.nii.gz -V')
L = len(x1)
x2 = x1[0:L-1]
var_ROI_cortical_volume = x2.rsplit(' ', 1)[1]
# var_SUVR_cortical_mean = float(var_RA_ROI_cortical_mean) / (float(var_injected_dose.get()) / x)
# var_SUVR_cortical_max = float(var_RA_ROI_cortical_max) / (float(var_injected_dose.get()) / x)
# var_SUVR_cortical_min = float(var_RA_ROI_cortical_min) / (float(var_injected_dose.get()) / x)
# var_SUVR_cortical_sd = float(var_RA_ROI_cortical_sd)
file_SUVR_cortical_mean = open(var_pet_address_output.get() + '/SUVR_cortical_mean.txt', 'a')
file_SUVR_cortical_mean.write(str(var_SUVR_cortical_mean))
if i != 96:
file_SUVR_cortical_mean.write('\n')
file_SUVR_cortical_mean.close()
file_SUVR_cortical_max = open(var_pet_address_output.get() + '/SUVR_cortical_max.txt', 'a')
file_SUVR_cortical_max.write(str(var_SUVR_cortical_max))
if i != 96:
file_SUVR_cortical_max.write('\n')
file_SUVR_cortical_max.close()
file_SUVR_cortical_min = open(var_pet_address_output.get() + '/SUVR_cortical_min.txt', 'a')
file_SUVR_cortical_min.write(str(var_SUVR_cortical_min))
if i != 96:
file_SUVR_cortical_min.write('\n')
file_SUVR_cortical_min.close()
file_SUVR_cortical_sd = open(var_pet_address_output.get() + '/SUVR_cortical_sd.txt', 'a')
file_SUVR_cortical_sd.write(str(var_SUVR_cortical_sd))
if i != 96:
file_SUVR_cortical_sd.write('\n')
file_SUVR_cortical_sd.close()
file1 = var_pet_address_output.get() + '/Areas_Cortical.txt'
file2 = var_pet_address_output.get() + '/SUV_cortical_min.txt'
file3 = var_pet_address_output.get() + '/SUV_cortical_max.txt'
file4 = var_pet_address_output.get() + '/SUV_cortical_mean.txt'
file5 = var_pet_address_output.get() + '/SUV_cortical_sd.txt'
file6 = var_pet_address_output.get() + '/SUVR_cortical_min.txt'
file7 = var_pet_address_output.get() + '/SUVR_cortical_max.txt'
file8 = var_pet_address_output.get() + '/SUVR_cortical_mean.txt'
file9 = var_pet_address_output.get() + '/SUVR_cortical_sd.txt'
file10 = var_pet_address_output.get() + '/Cortical_volume.txt'
file11 = var_pet_address_output.get() + '/temp_txt.txt'
os.system('paste -d \' \' ' + file1 + ' ' + file2 + ' ' + file3 + ' ' + file4 + ' ' + file5 + ' '\
+ file6 + ' ' + file7 + ' ' + file8 + ' ' + file9 + ' ' + file10 + ' >> ' + file11)
file12 = var_pet_address_output.get() + '/headers_cortical.txt'
file13 = var_pet_address_output.get() + '/temp_txt.txt'
file14 = var_pet_address_output.get() + '/OPETIA_cortical_all_info.txt'
os.system('cat ' + file12 + ' ' + file13 + ' >> ' + file14)
os.system('echo Done')
os.system('echo ---Calculating and saving SUV, SUVR, and cortical volume for 19 subcortical areas according to the Harvard-Oxford atlas')
BMI = float(var_body_weight.get()) / (float(var_height.get()) * float(var_height.get()))
#devide PET image by the reference mean to measure SUVR
var_SUV_refrence_mean = float(var_RA_refrence_mean) / (float(var_injected_dose.get()) / x)
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz -div ' + str(var_SUV_refrence_mean) + ' ' + var_pet_address_input.get() + '/PET_brain_normalized_std.nii.gz')
for i in range (1,20):
# Measure SUV coefficient
if var_type_of_measurement.get() == 'Using body weight':
x = (float(var_body_weight.get()) * 1000)
elif var_type_of_measurement.get() == 'Using LBM (male)':
x = (9270 * (float(var_body_weight.get()) * 1000)) / (6680 + (216*BMI))
elif var_type_of_measurement.get() == 'Using LBM (female)':
x = (9270 * (float(var_body_weight.get()) * 1000)) / (8780 + (244*BMI))
coefficient = x / float(var_injected_dose.get())
# Measure SUV
os.system('fslmaths ' + var_pet_address_input.get() + '/graymatter_mask_std -mul atlas_subcortical/' + str(i) + '_* ' + \
var_pet_address_output.get() + '/Subcortical_images/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz -mul ' + var_pet_address_output.get() + \
'/Subcortical_images/temp_img.nii.gz ' + var_pet_address_output.get() + '/Subcortical_images/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_output.get() + '/Subcortical_images/temp_img.nii.gz -mul ' + str(coefficient) + \
' ' + var_pet_address_output.get() + '/Subcortical_images/' + str(i) + '.nii.gz')
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Subcortical_images/' + str(i) + '.nii.gz -M')
L = len(x1)
var_SUV_subcortical_mean = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Subcortical_images/' + str(i) + '.nii.gz -P 0')
L = len(x1)
var_SUV_subcortical_min = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Subcortical_images/' + str(i) + '.nii.gz -P 100')
L = len(x1)
var_SUV_subcortical_max = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Subcortical_images/' + str(i) + '.nii.gz -S')
L = len(x1)
var_SUV_subcortical_sd = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Subcortical_images/' + str(i) + '.nii.gz -V')
L = len(x1)
x2 = x1[0:L-1]
var_ROI_subcortical_volume = x2.rsplit(' ', 1)[1]
# var_SUV_subcortical_mean = float(var_RA_ROI_subcortical_mean) / (float(var_injected_dose.get()) / x)
# var_SUV_subcortical_max = float(var_RA_ROI_subcortical_max) / (float(var_injected_dose.get()) / x)
# var_SUV_subcortical_min = float(var_RA_ROI_subcortical_min) / (float(var_injected_dose.get()) / x)
# var_SUV_subcortical_sd = float(var_RA_ROI_subcortical_sd)
file_SUV_subcortical_mean = open(var_pet_address_output.get() + '/SUV_subcortical_mean.txt', 'a')
file_SUV_subcortical_mean.write(str(var_SUV_subcortical_mean))
if i != 19:
file_SUV_subcortical_mean.write('\n')
file_SUV_subcortical_mean.close()
file_SUV_subcortical_max = open(var_pet_address_output.get() + '/SUV_subcortical_max.txt', 'a')
file_SUV_subcortical_max.write(str(var_SUV_subcortical_max))
if i != 19:
file_SUV_subcortical_max.write('\n')
file_SUV_subcortical_max.close()
file_SUV_subcortical_min = open(var_pet_address_output.get() + '/SUV_subcortical_min.txt', 'a')
file_SUV_subcortical_min.write(str(var_SUV_subcortical_min))
if i != 19:
file_SUV_subcortical_min.write('\n')
file_SUV_subcortical_min.close()
file_SUV_subcortical_sd = open(var_pet_address_output.get() + '/SUV_subcortical_sd.txt', 'a')
file_SUV_subcortical_sd.write(str(var_SUV_subcortical_sd))
if i != 19:
file_SUV_subcortical_sd.write('\n')
file_SUV_subcortical_sd.close()
file_subcortical_volume = open(var_pet_address_output.get() + '/Subcortical_volume.txt', 'a')
file_subcortical_volume.write(str(var_ROI_subcortical_volume))
if i != 19:
file_subcortical_volume.write('\n')
file_subcortical_volume.close()
# Measure SUVR
os.system('fslmaths ' + var_pet_address_input.get() + '/graymatter_mask_std -mul atlas_subcortical/' + str(i) + '_* ' + \
var_pet_address_output.get() + '/Subcortical_normalized_images/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_normalized_std.nii.gz -mul ' + var_pet_address_output.get() + \
'/Subcortical_normalized_images/temp_img.nii.gz ' + var_pet_address_output.get() + '/Subcortical_normalized_images/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_output.get() + '/Subcortical_normalized_images/temp_img.nii.gz -mul ' + str(coefficient) + \
' ' + var_pet_address_output.get() + '/Subcortical_normalized_images/' + str(i) + '.nii.gz')
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Subcortical_normalized_images/' + str(i) + '.nii.gz -M')
L = len(x1)
var_SUVR_subcortical_mean = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Subcortical_normalized_images/' + str(i) + '.nii.gz -P 0')
L = len(x1)
var_SUVR_subcortical_min = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Subcortical_normalized_images/' + str(i) + '.nii.gz -P 100')
L = len(x1)
var_SUVR_subcortical_max = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Subcortical_normalized_images/' + str(i) + '.nii.gz -S')
L = len(x1)
var_SUVR_subcortical_sd = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/Subcortical_normalized_images/' + str(i) + '.nii.gz -V')
L = len(x1)
x2 = x1[0:L-1]
var_ROI_subcortical_volume = x2.rsplit(' ', 1)[1]
# var_SUVR_subcortical_mean = float(var_RA_ROI_subcortical_mean) / (float(var_injected_dose.get()) / x)
# var_SUVR_subcortical_max = float(var_RA_ROI_subcortical_max) / (float(var_injected_dose.get()) / x)
# var_SUVR_subcortical_min = float(var_RA_ROI_subcortical_min) / (float(var_injected_dose.get()) / x)
# var_SUVR_subcortical_sd = float(var_RA_ROI_subcortical_sd)
file_SUVR_subcortical_mean = open(var_pet_address_output.get() + '/SUVR_subcortical_mean.txt', 'a')
file_SUVR_subcortical_mean.write(str(var_SUVR_subcortical_mean))
if i != 19:
file_SUVR_subcortical_mean.write('\n')
file_SUVR_subcortical_mean.close()
file_SUVR_subcortical_max = open(var_pet_address_output.get() + '/SUVR_subcortical_max.txt', 'a')
file_SUVR_subcortical_max.write(str(var_SUVR_subcortical_max))
if i != 19:
file_SUVR_subcortical_max.write('\n')
file_SUVR_subcortical_max.close()
file_SUVR_subcortical_min = open(var_pet_address_output.get() + '/SUVR_subcortical_min.txt', 'a')
file_SUVR_subcortical_min.write(str(var_SUVR_subcortical_min))
if i != 19:
file_SUVR_subcortical_min.write('\n')
file_SUVR_subcortical_min.close()
file_SUVR_subcortical_sd = open(var_pet_address_output.get() + '/SUVR_subcortical_sd.txt', 'a')
file_SUVR_subcortical_sd.write(str(var_SUVR_subcortical_sd))
if i != 19:
file_SUVR_subcortical_sd.write('\n')
file_SUVR_subcortical_sd.close()
os.system('rm ' + var_pet_address_output.get() + '/temp_txt.txt')
file1 = var_pet_address_output.get() + '/Areas_Subcortical.txt'
file2 = var_pet_address_output.get() + '/SUV_subcortical_min.txt'
file3 = var_pet_address_output.get() + '/SUV_subcortical_max.txt'
file4 = var_pet_address_output.get() + '/SUV_subcortical_mean.txt'
file5 = var_pet_address_output.get() + '/SUV_subcortical_sd.txt'
file6 = var_pet_address_output.get() + '/SUVR_subcortical_min.txt'
file7 = var_pet_address_output.get() + '/SUVR_subcortical_max.txt'
file8 = var_pet_address_output.get() + '/SUVR_subcortical_mean.txt'
file9 = var_pet_address_output.get() + '/SUVR_subcortical_sd.txt'
file10 = var_pet_address_output.get() + '/Subcortical_volume.txt'
file11 = var_pet_address_output.get() + '/temp_txt.txt'
os.system('paste -d \' \' ' + file1 + ' ' + file2 + ' ' + file3 + ' ' + file4 + ' ' + file5 + ' '\
+ file6 + ' ' + file7 + ' ' + file8 + ' ' + file9 + ' ' + file10 + ' >> ' + file11)
file12 = var_pet_address_output.get() + '/headers_subcortical.txt'
file13 = var_pet_address_output.get() + '/temp_txt.txt'
file14 = var_pet_address_output.get() + '/OPETIA_subcortical_all_info.txt'
os.system('cat ' + file12 + ' ' + file13 + ' >> ' + file14)
os.system('echo Done')
#--------------------------------------------------------------GE atlas
elif var_atlas.get() == 'CortexID Suite atlas':
os.system('echo ---Calculating and saving SUV, SUVR, and cortical volume for 26 areas according to the CortexID Suite atlas')
# Measure SUV coefficient
BMI = float(var_body_weight.get()) / (float(var_height.get()) * float(var_height.get()))
if var_type_of_measurement.get() == 'Using body weight':
x = (float(var_body_weight.get()) * 1000)
elif var_type_of_measurement.get() == 'Using LBM (male)':
x = (9270 * (float(var_body_weight.get()) * 1000)) / (6680 + (216*BMI))
elif var_type_of_measurement.get() == 'Using LBM (female)':
x = (9270 * (float(var_body_weight.get()) * 1000)) / (8780 + (244*BMI))
coefficient = x / float(var_injected_dose.get())
#Save SUV PET image for further visualization
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz -mul ' + str(coefficient) + ' ' + \
var_pet_address_output.get() + '/SUV.nii.gz')
#devide PET image by the reference mean to measure SUVR
var_SUV_refrence_mean = float(var_RA_refrence_mean) / (float(var_injected_dose.get()) / x)
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz -div ' + str(var_SUV_refrence_mean) + ' ' + var_pet_address_input.get() + '/PET_brain_normalized_std.nii.gz')
#Save SUV PET image for further visualization
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_normalized_std.nii.gz -mul ' + str(coefficient) + ' ' + \
var_pet_address_output.get() + '/SUVR.nii.gz')
for i in range (1,27):
# Measure SUV
os.system('fslmaths ' + var_pet_address_input.get() + '/graymatter_mask_std.nii.gz -mul atlas_GE/' + str(i) + '.nii.gz ' + \
var_pet_address_output.get() + '/images/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_std.nii.gz -mul ' + var_pet_address_output.get() + \
'/images/temp_img.nii.gz ' + var_pet_address_output.get() + '/images/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_output.get() + '/images/temp_img.nii.gz -mul ' + str(coefficient) + \
' ' + var_pet_address_output.get() + '/images/' + str(i) + '.nii.gz')
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/images/' + str(i) + '.nii.gz -M')
L = len(x1)
var_SUV_cortical_mean = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/images/' + str(i) + '.nii.gz -P 0')
L = len(x1)
var_SUV_cortical_min = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/images/' + str(i) + '.nii.gz -P 100')
L = len(x1)
var_SUV_cortical_max = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/images/' + str(i) + '.nii.gz -S')
L = len(x1)
var_SUV_cortical_sd = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/images/' + str(i) + '.nii.gz -V')
L = len(x1)
x2 = x1[0:L-1]
var_ROI_cortical_volume = x2.rsplit(' ', 1)[1]
# var_SUV_cortical_mean = float(var_RA_ROI_cortical_mean) / (float(var_injected_dose.get()) / x)
# var_SUV_cortical_max = float(var_RA_ROI_cortical_max) / (float(var_injected_dose.get()) / x)
# var_SUV_cortical_min = float(var_RA_ROI_cortical_min) / (float(var_injected_dose.get()) / x)
# var_SUV_cortical_sd = float(var_RA_ROI_cortical_sd)
file_SUV_cortical_mean = open(var_pet_address_output.get() + '/SUV_mean.txt', 'a')
file_SUV_cortical_mean.write(str(var_SUV_cortical_mean))
if i != 26:
file_SUV_cortical_mean.write('\n')
file_SUV_cortical_mean.close()
file_SUV_cortical_max = open(var_pet_address_output.get() + '/SUV_max.txt', 'a')
file_SUV_cortical_max.write(str(var_SUV_cortical_max))
if i != 26:
file_SUV_cortical_max.write('\n')
file_SUV_cortical_max.close()
file_SUV_cortical_min = open(var_pet_address_output.get() + '/SUV_min.txt', 'a')
file_SUV_cortical_min.write(str(var_SUV_cortical_min))
if i != 26:
file_SUV_cortical_min.write('\n')
file_SUV_cortical_min.close()
file_SUV_cortical_sd = open(var_pet_address_output.get() + '/SUV_sd.txt', 'a')
file_SUV_cortical_sd.write(str(var_SUV_cortical_sd))
if i != 26:
file_SUV_cortical_sd.write('\n')
file_SUV_cortical_sd.close()
file_cortical_volume = open(var_pet_address_output.get() + '/volume.txt', 'a')
file_cortical_volume.write(str(var_ROI_cortical_volume))
if i != 26:
file_cortical_volume.write('\n')
file_cortical_volume.close()
# Measure SUVR
os.system('fslmaths ' + var_pet_address_input.get() + '/graymatter_mask_std -mul atlas_GE/' + str(i) + '.nii.gz ' + \
var_pet_address_output.get() + '/normalized_images/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_input.get() + '/PET_brain_normalized_std.nii.gz -mul ' + var_pet_address_output.get() + \
'/normalized_images/temp_img.nii.gz ' + var_pet_address_output.get() + '/normalized_images/temp_img.nii.gz')
os.system('fslmaths ' + var_pet_address_output.get() + '/normalized_images/temp_img.nii.gz -mul ' + str(coefficient) + \
' ' + var_pet_address_output.get() + '/normalized_images/' + str(i) + '.nii.gz')
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/normalized_images/' + str(i) + '.nii.gz -M')
L = len(x1)
var_SUVR_cortical_mean = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/normalized_images/' + str(i) + '.nii.gz -P 0')
L = len(x1)
var_SUVR_cortical_min = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/normalized_images/' + str(i) + '.nii.gz -P 100')
L = len(x1)
var_SUVR_cortical_max = x1[0:L-1]
status, x1 = subprocess.getstatusoutput('fslstats ' + var_pet_address_output.get() + '/normalized_images/' + str(i) + '.nii.gz -S')
L = len(x1)
var_SUVR_cortical_sd = x1[0:L-1]
BMI = float(var_body_weight.get()) / (float(var_height.get()) * float(var_height.get()))
# var_SUVR_cortical_mean = float(var_RA_ROI_cortical_mean) / (float(var_injected_dose.get()) / x)
# var_SUVR_cortical_max = float(var_RA_ROI_cortical_max) / (float(var_injected_dose.get()) / x)
# var_SUVR_cortical_min = float(var_RA_ROI_cortical_min) / (float(var_injected_dose.get()) / x)
# var_SUVR_cortical_sd = float(var_RA_ROI_cortical_sd)
file_SUVR_cortical_mean = open(var_pet_address_output.get() + '/SUVR_mean.txt', 'a')
file_SUVR_cortical_mean.write(str(var_SUVR_cortical_mean))
if i != 26:
file_SUVR_cortical_mean.write('\n')
file_SUVR_cortical_mean.close()
file_SUVR_cortical_max = open(var_pet_address_output.get() + '/SUVR_max.txt', 'a')
file_SUVR_cortical_max.write(str(var_SUVR_cortical_max))
if i != 26:
file_SUVR_cortical_max.write('\n')
file_SUVR_cortical_max.close()
file_SUVR_cortical_min = open(var_pet_address_output.get() + '/SUVR_min.txt', 'a')
file_SUVR_cortical_min.write(str(var_SUVR_cortical_min))
if i != 26:
file_SUVR_cortical_min.write('\n')
file_SUVR_cortical_min.close()
file_SUVR_cortical_sd = open(var_pet_address_output.get() + '/SUVR_sd.txt', 'a')
file_SUVR_cortical_sd.write(str(var_SUVR_cortical_sd))
if i != 26:
file_SUVR_cortical_sd.write('\n')
file_SUVR_cortical_sd.close()
file1 = var_pet_address_output.get() + '/CortexID_areas.txt'
file2 = var_pet_address_output.get() + '/SUV_min.txt'
file3 = var_pet_address_output.get() + '/SUV_max.txt'
file4 = var_pet_address_output.get() + '/SUV_mean.txt'
file5 = var_pet_address_output.get() + '/SUV_sd.txt'
file6 = var_pet_address_output.get() + '/SUVR_min.txt'
file7 = var_pet_address_output.get() + '/SUVR_max.txt'
file8 = var_pet_address_output.get() + '/SUVR_mean.txt'
file9 = var_pet_address_output.get() + '/SUVR_sd.txt'
file10 = var_pet_address_output.get() + '/volume.txt'
file11 = var_pet_address_output.get() + '/temp_txt.txt'
os.system('paste -d \' \' ' + file1 + ' ' + file2 + ' ' + file3 + ' ' + file4 + ' ' + file5 + ' '\
+ file6 + ' ' + file7 + ' ' + file8 + ' ' + file9 + ' ' + file10 + ' >> ' + file11)
file12 = var_pet_address_output.get() + '/CortexID_headers.txt'
file13 = var_pet_address_output.get() + '/temp_txt.txt'
file14 = var_pet_address_output.get() + '/OPETIA_all_info.txt'
os.system('cat ' + file12 + ' ' + file13 + ' >> ' + file14)
os.system('echo Done')
os.system('echo ')
os.system('echo ----------Finish!')
os.system('echo --------------------------------------------')
os.system('echo ')
#___GUI
frame1 = LabelFrame(root, text='Input parameters', relief=SUNKEN, bd=2)
frame1.place(x=5, y=5, width=490, height=315)
Label(frame1, text="Folder including pre-processed data (OPETIA_output):").place(x=5, y=5)
Button(frame1, text="Browse", command=btn_enter_pet_input_command).place(x=5, y=30)
Entry(frame1, textvariable=var_pet_address_input).place(x=100, y=35, width=365)
Label(frame1, text="Output folder (name it ROI_analysis):").place(x=5, y=70)
Button(frame1, text="Browse", command=btn_enter_pet_output_command).place(x=5, y=95)
Entry(frame1, textvariable=var_pet_address_output).place(x=100, y=100, width=365)
Label(frame1, text="Tracer radioactivity (Bq):").place(x=5, y=140)
Entry(frame1, textvariable=var_injected_dose).place(x=365, y=140, width=100)
Label(frame1, text="Body weight (kg):").place(x=5, y=165)
Entry(frame1, textvariable=var_body_weight).place(x=365, y=165, width=100)
Label(frame1, text="Body height (m):").place(x=5, y=190)
Entry(frame1, textvariable=var_height).place(x=365, y=190, width=100)
Label(frame1, text='Type of measurement:').place(x=5, y=215)
ttk.Combobox(frame1, textvariable=var_type_of_measurement, values=['Using body weight', 'Using LBM (male)', 'Using LBM (female)'], state='readonly').place(x=265, y=220, width=200)
Label(frame1, text='SUV reference:').place(x=5, y=240)
ttk.Combobox(frame1, textvariable=var_SUV_reference, values=['Cerebellum', 'Cerebral cortex', 'Pons', 'Global gray matter'], state='readonly').place(x=265, y=245, width=200)
Label(frame1, text='Brain atlas:').place(x=5, y=265)
ttk.Combobox(frame1, textvariable=var_atlas, values=['Harvard Oxford atlas', 'CortexID Suite atlas'], state='readonly').place(x=265, y=270, width=200)
Button(root, text="Process", command=btn_process_command).place(x=5, y=325, width = 490)
Button(root, text="Open output folder", command=btn_open_folder_command).place(x=5, y=360, width = 490)
root.mainloop()