Comments (1)
This auto calibration method is based on the zeroth order consistency conditions, so it is an absolute necessity that the projections not be truncated. If you know of a calibration method that works with truncated data, please send me a reference.
Yes, you are correct, that is a typo and should be convert_to_modularbeam(). I just fixed this on the development branch.
Your updated code looks fine to me. The authors state that the cost function is only locally convex, so if your initial guess isn't close to the true solution, it may just converge to a local minimum.
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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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