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LLNL / LEAP

comprehensive library of 3D transmission Computed Tomography (CT) algorithms with Python and C++ APIs, a PyQt GUI, and fully integrated with PyTorch
https://leapct.readthedocs.io
MIT License
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FBPscalar #108

Closed zhangr05 closed 1 month ago

zhangr05 commented 1 month ago

Hi, Kyle

Thank you, this is a great contribution to the CT community.

I have a question regarding the FBP reconstruction. Based on the document: "FBP function can be achieved by running the following functions: filterProjections(f) weightedBackproject(g,f) f *= get_FBPscalar()"

However, when I replace the FBP with the above (e.g., in d01_standard_geometries.py), the intensity values are off by a lot. I wonder if there is a bug somewhere.

kylechampley commented 1 month ago

Oh, sorry about that. That is not quite correct. The code below should be used. Would you tell me where I wrote that so I can fix it? Thanks.

leapct.filterProjections(g)
leapct.weightedBackproject(g,f)

Note that the argument to filterProjections is "g" and NOT "f".

zhangr05 commented 1 month ago

Kyle, thanks for the quick response!

There are a few places on this page: https://leapct.readthedocs.io/en/latest/fbp.html#leapctype.tomographicModels.weightedBackproject

For example: leapctype.tomographicModels.filterProjections leapctype.tomographicModels.weightedBackproject (which notes: "Don’t forget to scale your reconstruction by get_FBPscalar() to obtain a quantitatively accurate result.")

kylechampley commented 1 month ago

Thanks, I revised those statements which will go out in the next release.

I'm going to close this issue, but feel free to re-open it if something else comes up.