Comments (8)
Uh, ya, there are a few methods in LEAP you could try. I guess the main method you could try is iterative reconstruction.
If you know the overall shape of your object, you could make a mask of it and then blend you CT reconstruction and this mask with the method described in the paper posted below.
https://www.sciencedirect.com/science/article/am/pii/S0963869521001997
I talk more about this in issue #34. See here
#34 (comment)
from leap.
I added a new sample script that demonstrates a cone-beam artifact method. Its currently on a development branch here: d33_reducingConeBeamArtifacts.py
Here is an example of the results:
from leap.
@scf819 did you try the above demo? Did you have any other questions about reducing cone-beam artifacts?
from leap.
I think that this method costs too much resource and time for real usage. As you know it needs three more recons both single and iterative ones.
from leap.
Those iterative reconstruction steps are an overkill just to make the method super robust. You can get results that are almost as good by quantizing the FBP result and then performing the filtering step with the cone-shaped filter.
from leap.
When I did test such iterative recon, I could see that target sample has to have rounding edge, if it has any sharp edge which will be removed too even it is real necessary one.
from leap.
It depends on what you are trying to do. If you are actually trying to recover this sharp edge I think you just need to acquire the data differently, but if you have a sharp edge and you are just interested in getting a better quality reconstruction in slices that are just inside this sharp edge then you can just estimate the location of this edge and manually set all the voxels outside this edge to zero and then apply this filtering method. This will usually work well for sharp edges. You may feel that this is "cheating", but it is not really cheating because the cone-shaped filtering process only includes a very small amount of data from this artificially cut off reconstruction, and, again, this is only appropriate if recovering the sharp edge is not of interest.
from leap.
Yes, your advice could be useful at some case, But at my case, the sharp edge needs to be cleaned too.
from leap.
Related Issues (20)
- big data, small RAM HOT 1
- The NaN problem in Iteration Reconstruction HOT 4
- issue: cone-beam with truncatedscan HOT 4
- Installing error HOT 14
- Can we intinialize the CT image when do iterative reconstruction methods on sinogram? HOT 5
- 3D reconstruction on LEAP HOT 6
- Forward projection bug related to the voxel spacing HOT 3
- Issue with LEAP Library Support on MacOS HOT 5
- Remove the limitation of 5 degree aligned to z-axis HOT 5
- Issue with Helical Reconstruction Geometry Parameters in Leap HOT 9
- laminography zero recon slice issue HOT 3
- Incorrect number of voxels in helical geometry when angular range is bigger than 360 degrees HOT 1
- Laminography multiply factor to align the projection image to slice image HOT 9
- Out of Memory Error for 4000x4000x2096 VOLUME HOT 23
- Out boundary brightness issue HOT 2
- batch mode in projection and its adjoint HOT 6
- intensity shift of helical fbp HOT 12
- Projection image's intensity slope reverse issue HOT 12
- fail to backpropagate after proj.fbp HOT 9
- Phantom jig calibration supporting HOT 6
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from leap.