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funcs_gh.py
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funcs_gh.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import nibabel as nib
import os, sys
import numpy as np
import pandas as pd
from scipy import signal
from scipy.ndimage import zoom
from scipy.interpolate import interp1d
#import matplotlib.pyplot as plt
#import SimpleITK as sitk
#from matplotlib.widgets import Slider, Button, RadioButtons
def readData4(patientName,subjectInfo,reconMethod,genBoundBox):
seqNum=subjectInfo['numSeq'][patientName];
# reconstruction Method
# type1 ---> 'SCAN'
# type2 ---> 'GRASP'
if reconMethod=='GRASP':
dataAddress0='path-to-folder-containing-4D-data-nii/GraspRecons/reconResults'+reconMethod+'/method2/';
elif reconMethod=='SCAN':
dataAddress0='path-to-folder-containing-4D-data-nii/ScanRecons/reconResults'+reconMethod+'/';
if seqNum==1 or reconMethod=='GRASP':
dataAddress=dataAddress0+patientName+'/recon'+reconMethod+'_4D.nii';
img= nib.load(dataAddress)
im=img.get_data()
maskAddress = 'path-to-folder-containing-4D-data-nii/GraspRecons/reconResults/'+patientName+'/';
if os.path.isfile(maskAddress+'aortaMask.nii'):
am1= nib.load(maskAddress+'aortaMask.nii');am=am1.get_data()
if os.path.isfile(maskAddress+'leftKidneyMask.nii.gz'):
lkm1= nib.load(maskAddress+'leftKidneyMask.nii.gz');lkm=2*lkm1.get_data();
lkm[lkm>2]=2;
elif os.path.isfile(maskAddress+'leftKidneyMaskUpper.nii.gz'):
lkm1_upper= nib.load(maskAddress+'leftKidneyMaskUpper.nii.gz');lkmUpper=2*lkm1_upper.get_data();
lkmUpper[lkmUpper>2]=2;
lkm1_lower= nib.load(maskAddress+'leftKidneyMaskLower.nii.gz');lkmLower=2*lkm1_lower.get_data();
lkmLower[lkmUpper>2]=2;
lkm=lkmUpper+lkmLower;
else:
lkm=np.zeros(np.shape(am));
if os.path.isfile(maskAddress+'rightKidneyMask.nii.gz'):
rkm1= nib.load(maskAddress+'rightKidneyMask.nii.gz');rkm=rkm1.get_data();
rkm[rkm>1]=1;
elif os.path.isfile(maskAddress+'rightKidneyMaskUpper.nii.gz'):
rkm1_upper= nib.load(maskAddress+'rightKidneyMaskUpper.nii.gz');rkmUpper=rkm1_upper.get_data();
rkm1_lower= nib.load(maskAddress+'rightKidneyMaskLower.nii.gz');rkmLower=rkm1_lower.get_data();
rkmUpper[rkmUpper>1]=1;
rkmLower[rkmLower>1]=1;
rkm=rkmUpper+rkmLower;
else:
rkm=np.zeros(np.shape(am));
else:
lkm=0;rkm=0;am=0;
elif seqNum==2:
dataAddress=dataAddress0+patientName+'_seq1/recon'+reconMethod+'_4D.nii';
img1= nib.load(dataAddress)
im1=img1.get_data()
dataAddress=dataAddress0+patientName+'_seq2/recon'+reconMethod+'_4D.nii';
img= nib.load(dataAddress)
im2=img.get_data()
x=subjectInfo['timeRes'][patientName];
seq1tres=float(x.split("[")[1].split(",")[0]);
seq2tres=float(x.split(",")[1].split("]")[0]);
if seq2tres>seq1tres:
#resample second to first
num2=seq2tres/seq1tres;
im3=zoom(im2,(1,1,1,num2),order=0);
im=np.concatenate((im1, im3), axis=3);
else:
im=np.concatenate((im1, im2), axis=3);
maskAddress='path-to-folder-to-containining-4D-nii-files/ScanRecons/reconResults/'+patientName+'_seq1/';
if os.path.isfile(maskAddress+'aortaMask.nii.gz'):
am1= nib.load(maskAddress+'aortaMask.nii.gz');am=am1.get_data()
if os.path.isfile(maskAddress+'leftKidneyMask.nii.gz'):
lkm1= nib.load(maskAddress+'leftKidneyMask.nii.gz');lkm=2*lkm1.get_data();
lkm[lkm>2]=2;
elif os.path.isfile(maskAddress+'leftKidneyMaskUpper.nii.gz'):
lkm1_upper= nib.load(maskAddress+'leftKidneyMaskUpper.nii.gz');lkmUpper=2*lkm1_upper.get_data();
lkmUpper[lkmUpper>2]=2;
lkm1_lower= nib.load(maskAddress+'leftKidneyMaskLower.nii.gz');lkmLower=2*lkm1_lower.get_data();
lkmLower[lkmLower>2]=2;
lkm=lkmUpper+lkmLower;
else:
lkm=np.zeros(np.shape(am));
if os.path.isfile(maskAddress+'rightKidneyMask.nii.gz'):
rkm1= nib.load(maskAddress+'rightKidneyMask.nii.gz');rkm=rkm1.get_data();
rkm[rkm>1]=1;
elif os.path.isfile(maskAddress+'rightKidneyMaskUpper.nii.gz'):
rkm1_upper= nib.load(maskAddress+'rightKidneyMaskUpper.nii.gz');rkmUpper=rkm1_upper.get_data();
rkm1_lower= nib.load(maskAddress+'rightKidneyMaskLower.nii.gz');rkmLower=rkm1_lower.get_data();
rkmUpper[rkmUpper>1]=1;
rkmLower[rkmLower>1]=1;
rkm=rkmUpper+rkmLower;
else:
rkm=np.zeros(np.shape(am));
else:
lkm=0;rkm=0;am=0;
boxes=[];
if genBoundBox:
aL=np.nonzero(lkm==2);
aR=np.nonzero(rkm==1);
if aL[0].size!=0:
boxL=np.array([int((min(aL[0])+max(aL[0]))/2),int((min(aL[1])+max(aL[1]))/2),int((min(aL[2])+max(aL[2]))/2),\
(max(aL[0])-min(aL[0])),(max(aL[1])-min(aL[1])),(max(aL[2])-min(aL[2]))])
else:
boxL=np.zeros((6,));
if aR[0].size!=0:
boxR=np.array([int((min(aR[0])+max(aR[0]))/2),int((min(aR[1])+max(aR[1]))/2),int((min(aR[2])+max(aR[2]))/2),\
(max(aR[0])-min(aR[0])),(max(aR[1])-min(aR[1])),(max(aR[2])-min(aR[2]))])
else:
boxR=np.zeros((6,));
boxes=np.vstack([np.array(boxR),np.array(boxL)]);
oriKM = rkm+lkm;
im=(im/np.amax(im))*100;
return im, oriKM, boxes, rkm,lkm
def readDetect(patientName,subjectInfo,reconMethod,genBoundBox):
primaryAddress = 'path-to-folder-to-contain-segmented-image-files/detected/';
seqNum=subjectInfo['numSeq'][patientName];
if reconMethod=='GRASP':
dataAddress0='path-to-folder-to-containining-4D-nii-files/GraspRecons/reconResults'+reconMethod+'/method2/';
elif reconMethod=='SCAN':
dataAddress0='path-to-folder-to-containining-4D-nii-files/ScanRecons/reconResults'+reconMethod+'/';
if seqNum==1 or reconMethod=='GRASP':
dataAddress=dataAddress0+patientName+'/recon'+reconMethod+'_4D.nii';
img= nib.load(dataAddress)
im=img.get_data()
maskAddress='path-to-folder-to-containining-4D-nii-files/GraspRecons/reconResults/'+patientName+'/';
if os.path.isfile(maskAddress+'aortaMask.nii'):
am1= nib.load(maskAddress+'aortaMask.nii');am=am1.get_data();
maskAddressDetect=primaryAddress+patientName+'_seq1'+'/';
if os.path.isfile(maskAddressDetect+'leftKidneyMask_detected.nii.gz'):
lkm1= nib.load(maskAddressDetect+'leftKidneyMask_detected.nii.gz');lkm=2*lkm1.get_data();
lkm[lkm>2]=2;
else:
lkm=np.zeros(np.shape(am));
if os.path.isfile(maskAddressDetect+'rightKidneyMask_detected.nii.gz'):
rkm1= nib.load(maskAddressDetect+'rightKidneyMask_detected.nii.gz');rkm=rkm1.get_data();
rkm[rkm>1]=1;
else:
rkm=np.zeros(np.shape(am));
else:
lkm=0;rkm=0;am=0;
elif seqNum==2:
dataAddress=dataAddress0+patientName+'_seq1/recon'+reconMethod+'_4D.nii';
img1= nib.load(dataAddress)
im1=img1.get_data()
dataAddress=dataAddress0+patientName+'_seq2/recon'+reconMethod+'_4D.nii';
img= nib.load(dataAddress)
im2=img.get_data()
#im=np.concatenate((im1, im2), axis=3);
x=subjectInfo['timeRes'][patientName];
seq1tres=float(x.split("[")[1].split(",")[0]);
seq2tres=float(x.split(",")[1].split("]")[0]);
if seq2tres>seq1tres:
#resample second to first
num2=seq2tres/seq1tres;
im3=zoom(im2,(1,1,1,num2),order=0);
#num=int(np.round(im2.shape[3]*seq2tres/seq1tres))
#im3=signal.resample(im2, num, t=None, axis=3)
im=np.concatenate((im1, im3), axis=3);
else:
im=np.concatenate((im1, im2), axis=3);
maskAddress='path-to-folder-containing-4D-data-nii/ScanRecons/reconResults/'+patientName+'_seq1/';
if os.path.isfile(maskAddress+'aortaMask.nii.gz'):
am1= nib.load(maskAddress+'aortaMask.nii.gz');am=am1.get_data()
maskAddressDetect = primaryAddress + patientName+'_seq1'+'/';
if os.path.isfile(maskAddressDetect+'leftKidneyMask_detected.nii.gz'):
lkm1= nib.load(maskAddressDetect+'leftKidneyMask_detected.nii.gz');lkm=2*lkm1.get_data();
lkm[lkm>2]=2;
else:
lkm=np.zeros(np.shape(am));
if os.path.isfile(maskAddressDetect+'rightKidneyMask_detected.nii.gz'):
rkm1= nib.load(maskAddressDetect+'rightKidneyMask_detected.nii.gz');rkm=rkm1.get_data();
rkm[rkm>1]=1;
else:
rkm=np.zeros(np.shape(am));
else:
lkm=0;rkm=0;am=0;
boxes=[];
if genBoundBox:
aL=np.nonzero(lkm==2);
aR=np.nonzero(rkm==1);
if aL[0].size!=0:
boxL=np.array([int((min(aL[0])+max(aL[0]))/2),int((min(aL[1])+max(aL[1]))/2),int((min(aL[2])+max(aL[2]))/2),\
(max(aL[0])-min(aL[0])),(max(aL[1])-min(aL[1])),(max(aL[2])-min(aL[2]))])
else:
boxL=np.zeros((6,));
if aR[0].size!=0:
boxR=np.array([int((min(aR[0])+max(aR[0]))/2),int((min(aR[1])+max(aR[1]))/2),int((min(aR[2])+max(aR[2]))/2),\
(max(aR[0])-min(aR[0])),(max(aR[1])-min(aR[1])),(max(aR[2])-min(aR[2]))])
else:
boxR=np.zeros((6,));
boxes=np.vstack([np.array(boxR),np.array(boxL)]);
# add extra margins to minimise impact of false-negative predictions
mask = rkm+lkm; mask[mask>1]=1;
xSafeMagin=10;ySafeMagin=10;zSafeMagin=3;
if boxes[0,2]+boxes[0,5]+3 >= mask.shape[2] or boxes[0,2]+boxes[0,5]-3 <0:
boxes[:,[3,4,5]]=boxes[:,[3,4,5]]+[xSafeMagin,ySafeMagin,0];
else:
boxes[:,[3,4,5]]=boxes[:,[3,4,5]]+[xSafeMagin,ySafeMagin,zSafeMagin];
# add extra margins to minimise impact of false-negative predictions
#xSafeMagin=12;ySafeMagin=12;zSafeMagin=3;
#Box[:,[3,4,5]]=Box[:,[3,4,5]]+[xSafeMagin,ySafeMagin,zSafeMagin];
KM=rkm+lkm;
im=(im/np.amax(im))*100;
return im, KM, boxes, rkm,lkm
def writeMasksDetect(patientName,subjectInfo,reconMethod,Masks2Save,overwrite):
primaryAddress = 'path-to-folder-to-contain-detection-image-files/detected/';
seqNum=subjectInfo['numSeq'][patientName];
# reconstruction Method
# type 1 ---> 'SCAN'
# type 2 ---> 'GRASP'
if reconMethod=='GRASP':
dataAddress0='path-to-folder-containing-4D-data-nii/GraspRecons/reconResults'+reconMethod+'/method2/';
elif reconMethod=='SCAN':
dataAddress0='path-to-folder-containing-4D-data-nii/ScanRecons/reconResults'+reconMethod+'/';
if seqNum==1 or reconMethod=='GRASP':
dataAddress=dataAddress0+patientName+'/recon'+reconMethod+'_T0.nii';
img= nib.load(dataAddress)
#im=img.get_data()
maskAddress = primaryAddress + patientName+'_seq1/';
if os.path.isfile(maskAddress+'leftKidneyMask_detected.nii') and not overwrite:
print('Mask is already existant!');
else:
Masks2SaveR=Masks2Save['R'];Masks2SaveL=Masks2Save['L'];
Masks2Save1R = nib.Nifti1Image(Masks2SaveR, img.affine)
Masks2Save1L = nib.Nifti1Image(Masks2SaveL, img.affine)
nib.save(Masks2Save1L,maskAddress+'leftKidneyMask_detected.nii.gz');
nib.save(Masks2Save1R,maskAddress+'rightKidneyMask_detected.nii.gz');
elif seqNum==2:
dataAddress=dataAddress0+patientName+'_seq1/recon'+reconMethod+'_T0.nii';
img1= nib.load(dataAddress)
maskAddress = primaryAddress + patientName+'_seq1/';
if os.path.isfile(maskAddress+'leftKidneyMask_detected.nii') and not overwrite:
print('Mask is already existant!');
else:
Masks2SaveR=Masks2Save['R'];Masks2SaveL=Masks2Save['L'];
summationL = np.sum(Masks2SaveL)
if summationL == 0.0:
#Masks2SaveL = Masks2SaveL.astype('int');
Masks2SaveL[112,112,16] = 1.0;
summationR = np.sum(Masks2SaveR)
if summationR == 0.0:
#Masks2SaveR = Masks2SaveR.astype('int');
Masks2SaveR[112,112,16] = 1.0;
Masks2Save1R = nib.Nifti1Image(Masks2SaveR, img1.affine)
Masks2Save1L = nib.Nifti1Image(Masks2SaveL, img1.affine)
nib.save(Masks2Save1L,maskAddress+'leftKidneyMask_detected.nii.gz');
nib.save(Masks2Save1R,maskAddress+'rightKidneyMask_detected.nii.gz');
oscommand='chmod -R 777 '+maskAddress;
os.system(oscommand);
return
def writeMasks(patientName,subjectInfo,reconMethod,Masks2Save,overwrite):
primaryAddress = 'path-to-folder-to-contain-segmented-image-files/segmented/';
seqNum=subjectInfo['numSeq'][patientName];
# reconstruction Method
# scanner ---> 'SCAN'
# grasp ---> 'GRASP'
if reconMethod=='GRASP':
dataAddress0='path-to-folder-to-containining-4D-nii-files/GraspRecons/reconResults'+reconMethod+'/method2/';
elif reconMethod=='SCAN':
dataAddress0='path-to-folder-to-containining-4D-nii-files/ScanRecons/reconResults'+reconMethod+'/';
if seqNum==1 or reconMethod=='GRASP':
dataAddress=dataAddress0+patientName+'/recon'+reconMethod+'_T0.nii';
img= nib.load(dataAddress)
#im=img.get_data()
maskAddress=primaryAddress+patientName+'_seq1/';
if os.path.isfile(maskAddress+'leftKidneyMask_automatic.nii') and not overwrite:
print('Mask is already existant!');
else:
Masks2SaveR=Masks2Save['R'];Masks2SaveL=Masks2Save['L'];
Masks2Save1R = nib.Nifti1Image(Masks2SaveR, img.affine)
Masks2Save1L = nib.Nifti1Image(Masks2SaveL, img.affine)
nib.save(Masks2Save1L,maskAddress+'leftKidneyMask_automatic.nii.gz');
nib.save(Masks2Save1R,maskAddress+'rightKidneyMask_automatic.nii.gz');
elif seqNum==2:
dataAddress=dataAddress0+patientName+'_seq1/recon'+reconMethod+'_T0.nii';
img1= nib.load(dataAddress);
maskAddress=primaryAddress+patientName+'_seq1/';
if os.path.isfile(maskAddress+'leftKidneyMask_automatic.nii') and not overwrite:
print('Mask is already existant!');
else:
Masks2SaveR=Masks2Save['R'];Masks2SaveL=Masks2Save['L'];
summationL = np.sum(Masks2SaveL)
if summationL == 0.0:
#Masks2SaveL = Masks2SaveL.astype('int');
Masks2SaveL[112,112,16] = 1.0;
summationR = np.sum(Masks2SaveR)
if summationR == 0.0:
#Masks2SaveR = Masks2SaveR.astype('int');
Masks2SaveR[112,112,16] = 1.0;
Masks2Save1R = nib.Nifti1Image(Masks2SaveR, img1.affine)
Masks2Save1L = nib.Nifti1Image(Masks2SaveL, img1.affine)
nib.save(Masks2Save1L,maskAddress+'leftKidneyMask_automatic.nii.gz');
nib.save(Masks2Save1R,maskAddress+'rightKidneyMask_automatic.nii.gz');
oscommand='chmod -R 777 '+maskAddress;
os.system(oscommand);
return
def baselineFinder(im):
aortaPotentialTimesIM=im[75:150,:,0:20,0:50]; # keep first 15 dataPoints
#aortaPotentialTimesIM=im[:,:,:,0:1];
x=(aortaPotentialTimesIM>.8*np.max(aortaPotentialTimesIM)).nonzero()
x=(np.max(aortaPotentialTimesIM,axis=3)-np.min(aortaPotentialTimesIM,axis=3)>.6*np.max(aortaPotentialTimesIM)).nonzero()
#b = Counter(x[2]);
#mostOccInZ=b.most_common(1)[0][0];
medianOfValsInXaxisNdx=(abs(x[0]-np.median(x[0]))<10).nonzero()[0];
medianOfValsInYaxisNdx=(abs(x[1]-np.median(x[1]))<10).nonzero()[0];
medianOfValsInZaxisNdx=(abs(x[2]-np.median(x[2]))<10).nonzero()[0];
commonXYconstraint=np.intersect1d(medianOfValsInXaxisNdx,medianOfValsInYaxisNdx);
commonXYconstraint=np.intersect1d(medianOfValsInZaxisNdx,commonXYconstraint)
allAortaPotentials=aortaPotentialTimesIM[x[0][commonXYconstraint],x[1][commonXYconstraint],x[2][commonXYconstraint],:]
v,timeNdx = allAortaPotentials.max(1),allAortaPotentials.argmax(1)
maxA=np.median(timeNdx)
y=np.mean(allAortaPotentials[:,0:int(maxA)+1],0);
#plt.figure();plt.plot(y,'-*');
#plt.figure();plt.plot(np.mean(allAortaPotentials,0),'-*');
y2=(y*400)/y.max();y3=y2-y2.min()+50;
baseLine=(y3<0.5*(max(y3)-min(y3))).nonzero()[0][-1];
#baseLine=(y<0.5*(max(y)-min(y))).nonzero()[0][-1];
#print('Automatically detected injection timePoint: '+str(baseLine))
return baseLine
def computeNew4D(im):
vol4D = np.array(im);
xl = vol4D.shape[0];
yl = vol4D.shape[1];
sl = vol4D.shape[2];
tl = vol4D.shape[3];
reshapedVol4D = np.reshape(vol4D,[xl*yl*sl,tl]);
fid = np.mean(reshapedVol4D,axis=0)
diffFid = np.diff(fid,n=1,axis=0)
absDiffFid = np.absolute(diffFid)
maxAbsDiffFid = max(absDiffFid)
jumpTimeSample, = np.where(absDiffFid == maxAbsDiffFid)
jumpTimeSample = max(jumpTimeSample)
difference = fid[jumpTimeSample+1]-fid[jumpTimeSample];
newVol4D = np.copy(vol4D);
oldVol4D = np.copy(vol4D);
newVol4D[:,:,:,jumpTimeSample+1:] = np.subtract(oldVol4D[:,:,:,jumpTimeSample+1:],difference)
return newVol4D
def baselineFinder2(patientName,subjectInfo):
maskAddress='path-to-folder-to-containining-4D-nii-files/GraspRecons/reconResults/'+patientName;
#maskAddress='path-to-folder-to-containining-4D-nii-files/SraspRecons/reconResults/'+patientName;
path1 = maskAddress+'/aortaMask.nii.gz';
path2 = maskAddress+'_seq1/aortaMask.nii.gz';
if os.path.isfile(path1):
am1= nib.load(path1);am=am1.get_data()
elif os.path.isfile(path2):
am1= nib.load(path2);am=am1.get_data()
im, _,_,_,_= readData4(patientName,subjectInfo,'SCAN',0);
im = computeNew4D(im);
time = im.shape[3];
aortaMask = np.zeros(np.shape(im));
aortaInt = im;
for xx in range (time):
aortaMask[:,:,:,xx] = am;
aortaInt[aortaMask==0]=0
aortaMask = np.copy(aortaInt);
xs,ys,zs,as1 = np.where(aortaMask!=0)
aortaMask2 = aortaMask[min(xs):max(xs)+1,min(ys):max(ys)+1,min(zs):max(zs)+1,min(as1):max(as1)+1]
aortaMask2 = np.array(aortaMask2)
aortaMask3 = np.reshape(aortaMask2,[aortaMask2.shape[0]*aortaMask2.shape[1]*aortaMask2.shape[2],time]);
xs1,ys1 = np.where(aortaMask3 !=0)
aortaMask4 = aortaMask3[min(xs1):max(xs1)+1,min(ys1):max(ys1)+1]
aortaMask4 = np.array(aortaMask4)
whereIs = np.amax(aortaMask4, axis=1)
indicesWhereIs = np.where(whereIs !=0)
aortaMask4 = aortaMask4[indicesWhereIs, :]
aortaMask4 = np.reshape(aortaMask4,[aortaMask4.shape[1],time])
midWay = int(time/2)
tWay = int(time/3)
lookAtM = aortaMask4[:,1:midWay]
lookAtT = aortaMask4[:,1:tWay]
ranMax = np.amax(lookAtM, axis=1) # Maxima along the second axis
ranMin = np.amin(lookAtT, axis=1) # Minima along the second axis
rangeA = ranMax - ranMin
maxKeepPercent = 40;
condition = rangeA > (1-maxKeepPercent/100)*max(rangeA)
valuesOfInterestINDICES = np.where(condition)
aortaMask5 = aortaMask4[valuesOfInterestINDICES,:]
aortaMask5 = np.reshape(aortaMask5,[aortaMask5.shape[1],time])
checkLength = aortaMask5.shape[0];
powerOfHF = []
for xx in range(checkLength):
arrayVI = np.array(aortaMask5[xx,:]);
maximumVI = max(arrayVI)
peak, = np.where(arrayVI == maximumVI)
peak = max(peak)
diffS = np.diff(aortaMask5[xx,peak:],n=1,axis=0)
squaredDiffs = np.square(diffS);
sumSquaredDiffs = np.sum(squaredDiffs)
powerOfHF.append(sumSquaredDiffs)
powerOfHF = np.array(powerOfHF)
lenHF = powerOfHF.shape[0]
sortedSmoothestVoxels = sorted(range(lenHF), key=lambda k: powerOfHF[k])
smoothestVoxels = sortedSmoothestVoxels[1:int(len(sortedSmoothestVoxels)/2)];
selectedVoxels = np.array(aortaMask5[smoothestVoxels,:]);
aif = np.median(selectedVoxels,axis=0);
aifMAX = max(aif)
aifMndx, = np.where(aif == aifMAX)
aifMndx = max(aifMndx)
aifDif = np.diff(aif[1:aifMndx-1],n=1,axis=0)
aifDif = np.array(aifDif.tolist());
baseLine2 = [index for index, item in enumerate(aifDif) if item != 0][-1]
### constraint (?)
if baseLine2 > 15:
baseLine2 = 10;
return baseLine2