def Normalization(reconspace, backprojector, sinogram):
sinseg = sinogram.get_segment_by_sinogram(0)
filler = np.ones(np.shape(stirextra.to_numpy(sinseg)))
fillStirSpace(sinseg, filler)
sinogram.set_segment(sinseg)
backprojector.back_project(reconspace, sinogram)
npReconSpace = stirextra.to_numpy(reconspace)
pyvpx.numpy2vpx(npReconSpace, 'NormMap.vpx')
return reconspace
countIt = iIt + 1
plt.figure(), plt.axis('off'), plt.title(
'Guess after {0} iteration(s)'.format(iIt + 1)), plt.imshow(
guess,
interpolation=None,
vmin=0,
vmax=np.max(image2D),
cmap=plt.cm.Greys_r), plt.savefig(
figSaveDir +
'Fig{}_finalImage.png'.format(numFigures, trueShiftAmplitude)
), plt.close()
numFigures += 1
image2DTMP = np.zeros((1, ) + np.shape(guess))
image2DTMP[0, :, :] = guess
pyvpx.numpy2vpx(image2DTMP,
figSaveDir + 'guess_Iteration{}.vpx'.format(iIt))
plt.figure(), plt.axis('off'), plt.subplot(
1, 2, 1), plt.title('Original Image'), plt.imshow(image2D,
interpolation=None,
vmin=0,
vmax=np.max(image2D),
cmap=plt.cm.Greys_r)
plt.subplot(1, 2, 2), plt.title('Reconstructed Image'), plt.imshow(
guess,
interpolation=None,
vmin=0,
vmax=np.max(image2D),
cmap=plt.cm.Greys_r), plt.savefig(
figSaveDir +
'Fig{}_originalAndRecon.png'.format(numFigures, trueShiftAmplitude)
# Target volumes
largeTarget = guess[125:185, 100:170]
smallTarget = guess[115:175, 150:220]
plt.figure(), plt.axis('off'), plt.title(
'Target volume (large lesion)'), plt.imshow(
largeTarget,
interpolation=None,
vmin=0,
vmax=np.max(largeTarget),
cmap=plt.cm.Greys_r), plt.savefig(
figSaveDir + 'QFig{}_largeTarget'.format(numFigures)), plt.close()
numFigures += 1
image2DTMP = np.zeros((1, ) + np.shape(largeTarget))
image2DTMP[0, :, :] = largeTarget
pyvpx.numpy2vpx(image2DTMP, figSaveDir + 'largeTarget.vpx')
plt.figure(), plt.axis('off'), plt.title(
'Target volume (snall lesion)'), plt.imshow(
smallTarget,
interpolation=None,
vmin=0,
vmax=np.max(smallTarget),
cmap=plt.cm.Greys_r), plt.savefig(
figSaveDir + 'QFig{}_smallTarget'.format(numFigures)), plt.close()
numFigures += 1
image2DTMP = np.zeros((1, ) + np.shape(smallTarget))
image2DTMP[0, :, :] = smallTarget
pyvpx.numpy2vpx(image2DTMP, figSaveDir + 'smallTarget.vpx')
# SUV max
import numpy as np
import matplotlib.pyplot as plt
import ManonsFunctions as mf
import pyvpx
nGates = 5
experiment = 'Sinus2D/'
filePath = "E:/Manon/Resultaten_Experimenten/" + experiment + "Gating/"
gatedImagesReg = []
for iGate in range(1, nGates+1):
gatedImagesReg.append(mf.load_itk(filePath + "Coregistered_gates/Gate{}.mhd".format(iGate, iGate)))
meanImageRegistered = np.mean(gatedImagesReg, axis = 0)
pyvpx.numpy2vpx(meanImageRegistered, filePath + "MeanRegistered.vpx")
mf.writeMhdFile(meanImageRegistered, filePath + "MeanRegistered.mhd")
for j in range(np.shape(sinoMu)[1]):
expSinoMu[i, j] = math.exp(-sinoMu[i, j])
# Plot phantom and store as vpx image
plt.figure(), plt.axis('off'), plt.title('Original image'), plt.imshow(
image2D,
interpolation=None,
vmin=0,
vmax=np.max(image2D),
cmap=plt.cm.Greys_r), plt.savefig(
figSaveDir + 'Fig{}_phantom.png'.format(numFigures)), plt.close()
numFigures += 1
image2DTMP = np.zeros((1, ) + np.shape(image2D))
image2DTMP[0, :, :] = image2D
pyvpx.numpy2vpx(image2DTMP, figSaveDir + 'OriginalImage.vpx')
#_________________________ADD MOTION_______________________________
# Create surrogate signal and add motion to the phantom
phantomList, surSignal, shiftList, shiftXList = mf.move_Phantom(
motion, nFrames, trueShiftAmplitude, trueSlope, trueSlopeX, image2D,
stationary, modelBroken)
originalImage = phantomList[0]
# Plot surrogate signal and internal motion
# y-axis
plt.figure(), plt.axis('off')
plt.plot(range(nFrames), surSignal, label='Surrogate signal'), plt.title(
'Motion (y-axis)'), plt.xlabel('Time frame'), plt.ylabel('Shift')
plt.plot(range(nFrames), shiftList, label='True motion y-axis')
plt.legend(
#_________________________MAKE PHANTOM_______________________________
image2D = mf.make_Phantom(phantom, noiseLevel)
plt.figure(), plt.title('Original image'), plt.imshow(
image2D,
interpolation=None,
vmin=0,
vmax=np.max(image2D),
cmap=plt.cm.Greys_r), plt.savefig(
figSaveDir +
'Fig{}_TrueShift{}_phantom.png'.format(numFigures, trueShiftAmplitude)
), plt.close()
numFigures += 1
image2DTMP = np.zeros((1, ) + np.shape(image2D))
image2DTMP[0, :, :] = image2D
pyvpx.numpy2vpx(image2DTMP, figSaveDir + 'image2D.vpx')
#_________________________ADD MOTION_______________________________
phantomList, surSignal, shiftList = mf.move_Phantom(motion, nFrames,
trueShiftAmplitude,
trueSlope, image2D,
stationary)
originalImage = phantomList[0]
imageTMP = np.zeros((1, ) + np.shape(image2D))
imageTMP[0, :, :] = phantomList[0]
pyvpx.numpy2vpx(imageTMP, figSaveDir + 'image.vpx')
for iFrame in range(nFrames):
plt.subplot(2, nFrames / 2 + 1, iFrame + 1), plt.title(
'Time frame {0}'.format(iFrame)), plt.imshow(