# simply does not have any way of storing the information (Zeiss Xradia # does not store anything but the projecions) # if this is the case, the general way of tackling the problem is: # Step 1: define your geometry and angles geo=tigre.geometry() angles= # Step 2: Load projections: #store in NP array. Use whichever python lirbary will help you get the data loaded. # Step 3: validate tigre.plotproj(proj,angles) # you need to make sure that: # 1) white=metal/bone/high density and black=air. # If this is not the case proj=-np.log(proj/(np.max(proj+1)); # Beer-Lanbert law # 2) rotation happens left-right instead of top-bottom. # If its top bottom permute your data as required # Step 4: test imgfdk=algs.fdk(proj,geo,angles) tigre.plotimg(imgFDK,dim='z') # If this does not look good, possible things to check: # - Are the angles in the right direction? maybe they need to be inverted. # - Is your geometry properly defined? mistake on geometry will be # detrimental to image quality # - if microCT: halos around the features? COR correction needed.
# sample the voxels at a given sample rate.
#%% Main difference
head = sample_loader.load_head_phantom(geo.nVoxel)
start_time = time.time()
projInterp = tigre.Ax(head, geo, angles, "interpolated")
interptime = time.time() - start_time
start_time = time.time()
projray = tigre.Ax(head, geo, angles, "Siddon")
raytime = time.time() - start_time
# It is relatively clear that discretization artefacts appear with the
# ray-voxel approach (middle one)
tigre.plotproj(
np.concatenate([np.abs(projInterp - projray), projray, projInterp],
axis=1), angles)
# But also the ray voxel approach is faster (more obvious at bigger sizes)
print("Time interpolated: " + str(interptime))
print("Time Siddon : " + str(raytime))
#%% With small voxel the errors are more obvious
geo.nVoxel = np.array([32, 32, 32]) # number of voxels (vx)
geo.sVoxel = np.array([256, 256, 256]) # total size of the image (mm)
geo.dVoxel = geo.sVoxel / geo.nVoxel # size of each voxel (mm)
head = sample_loader.load_head_phantom(geo.nVoxel)
geo.accuracy = 3
projInterp3 = tigre.Ax(head, geo, angles, "interpolated")
geo.accuracy = 1
projInterp1 = tigre.Ax(head, geo, angles, "interpolated")
roll = angles
pitch = 0.7 * np.linspace(0, 1, len(angles))
yaw = 0.7 * np.linspace(0, 1, len(angles))
geo.rotDetector = np.array(np.vstack([roll, pitch, yaw])).T
#%%
# Load thorax phatom data
head = sample_loader.load_head_phantom(geo.nVoxel)
# generate projections
projections = tigre.Ax(head, geo, angles)
# add noise
noise_projections = CTnoise.add(projections,
Poisson=1e5,
Gaussian=np.array([0, 10]))
tigre.plotproj(projections, angles)
#%% recon
imgRotDet = algs.ossart(projections, geo, angles, 50)
#% No rotation
geo.rotDetector = np.array([0, 0, 0])
projections2 = tigre.Ax(head, geo, angles)
imgnoRot = algs.ossart(projections2, geo, angles, 50)
# %% Plot to show that indeed the reconstruction is right
tigre.plotimg(np.concatenate([imgRotDet, imgnoRot], axis=1), dim="z")
geo = tigre.geometry_default(high_resolution=False)
#%% Define angles of projection and load phatom image
# define projection angles (in radians)
angles = np.linspace(0, 2 * np.pi, 50)
# load phatnom image
head = sample_loader.load_head_phantom(geo.nVoxel)
# Simulate forward projection.
# To match with mathematical notation, the projection operation is called Ax
projections = tigre.Ax(head, geo, angles)
# Add realistic noise. Adds photon scattering noise ('Poisson') and
# electronic noise of the detector ('Gaussian').
#
# 'Poisson' is related to the maximum photon count in the detector. 1e5 is
# a standard clinical nuber, reduce it for more noise
# 'Gaussian' is related to possible electronic noise in the detector. mean
# of 0 and std of 10 is common in clinical scenario. Increase std for more
# noise.
noise_projections = CTnoise.add(projections,
Poisson=1e5,
Gaussian=np.array([0, 10]))
#%% Plot Projections
tigre.plotproj(projections)
# plot noise
tigre.plotproj(projections - noise_projections)
clims = [0, 200]
# 'Savegif': allows to save the plotted figure as an animated gif,
# specified by the given filename.
giffilename = "demo5projections.gif"
# 'Slice': allows to plot a single projection .Will overwrite the behaviour
# of 'Step'
slice = 5
# Lets try out.
tigre.plotproj(noise_projections,
angles,
step=step,
colormap=colormap,
clims=clims,
savegif=giffilename) # not using 'Step'
# Remember you can also plot errors, for example the added noise by:
noise = np.abs(noise_projections -
projections) # abs is what we are interested in plotting
tigre.plotproj(noise, angles, clims=[0, 2])
#%% PlotImg
# plotImg plots the image slice by slice.
#
# List of optional parameters:
#%% Load data and generate projections
# define angles
angles = np.linspace(0, 2 * np.pi, 100)
## Define angles
numProjs = 100
anglesY = np.linspace(0, 2 * np.pi, numProjs)
anglesZ2 = anglesY
anglesZ1 = np.pi * np.sin(np.linspace(0, 2 * np.pi, numProjs))
angles = np.vstack([anglesZ1, anglesY, anglesZ2]).T
## Get Image
head = sample_loader.load_head_phantom(geo.nVoxel)
## Project
projections = tigre.Ax(head, geo, angles)
tigre.plotproj(projections,
anglesY) # angle information not right in the title
## Reconstruct:
# Note, FDK will not work.
imgSIRT = algs.sirt(projections, geo, angles, 50)
imgCGLS = algs.cgls(projections, geo, angles, 10)
tigre.plotimg(np.concatenate([head, imgSIRT, imgCGLS], axis=1), dim="z")