Feature Requests #88
Replies: 14 comments 12 replies
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Below is my list of the items to be supported at leap package for the usual usage for tomography.
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I am interested in the following topics and would appreciate if they could be considered for inclusion in the LEAP toolkit:
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Thanks for the list. I have some questions/ comments.
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@ProjX0, I could easily do 3-energy decomposition and multi-material BHC for any number of materials. What is the purpose of the Katsevich algorithm? It is theoretically exact, but in practice WFBP (the algorithm implemented in LEAP) produces better results and is more "dose efficient". By dose efficient, the WFBP algorithm always uses data from all detector rows, but the Katsevich algorithm throws out some of the data. |
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@hws203, what do you mean by HU information? Note that if you know something about the x-ray spectra you can convert your reconstructions into HU using the XrayPhysics package. |
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The Future Releases section has been modified. @hws203, I still don't understand you comment about converting to HU. You can do this with the XrayPhysics packages like this: |
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@kylechampley |
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@kylechampley , Did you go back to old future release list at your readme file ? Now it is updated,,,, |
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A beta version of the GUI has been released. |
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Ball phantom calibration function has been released in v1.22. |
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Hi Kyle, it would be great if you could add more documentation to the leaptorch section. Does leaptorch support curvedetector, shift_detector, set_rampFilter, etc. ? |
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They are already supported because the tomographicModels class (the class in leapctype.py) is a member variable of the Projector class. For example, you can do:
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@kylechampley As you know, current leapct use the float type projection data as like float *g. But most x-ray detector device makes unsigned short type data, so I have to convert it into float data which consumes more memory and cpu resource. |
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Yes, most x-ray detectors have 14 to 16 bits of accuracy which, as you say, are usually stored as unsigned 16-bit integers. For CT algorithms to work, you must use floating point numbers which can be 16, 32, or 64-bit. LEAP uses 32-bit float. Switching to 16-bit float would be a HUGE task that I am not up for. Moreover, using 16-bit floats will incur a lot of roundoff error and in my opinion is only appropriate for small data sizes. Recently releases of LEAP are much more memory efficient; have you tried this? In addition, LEAP does support saving data as 8 or 16 bits. See the set_fileIO_parameters function. |
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I maintain a list the things I am working on in the Future Releases section on the main page.
But what sorts of features do our users want? Some example topics are: improve the build process, improve speed, new algorithms, improvements to the PyTorch interface, file I/O features to process data from various vendor systems, etc.? What is preventing you from using LEAP for your applications?
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