Below is my list of the items to be supported at leap package for the usual usage for tomography.

1. Geometry calibration algorithm for auto finding parameters of cone beam. I am using the sweep method of leap currently. But I hope a little more robust and fast one for this purpose.
2. motion correction(jitter correction) of projection image feature for the imperfect scanner H/W.
3. PyQT GUI for the visual and direct understanding of current geometry, parameters and projection simulation.
4. A little more independency of python library(like scipy) and more c++ or cuda code for faster running.
5. Ball phantom feature for the detail calibration.
6. Sinogram order recon supporting for the faster recon.
7. Offscan recon at concurrent recon from both left and right projection image.
8. Selectable bad pixel correction algorithm not by all area filtering.
9. Projection simulation feature from stl file and HU information.
10. beam hardening correction as you listed up for future release.

I am interested in the following topics and would appreciate if they could be considered for inclusion in the LEAP toolkit:

• Multi-energy CT reconstruction
• Three (or more) material decomposition (e.g., bone, fat, water)
• Katsevich algorithm for spiral CT reconstruction

Thanks for the list. I have some questions/ comments.

1. I have some ideas about what to add here.
2. This is not something I want to do. Hopefully someone else will step up and add this to LEAP.
3. Yep, this is my current priority. It's a lot of work, so it is taking more time than I first expected.
4. I'm not sure what "fact running" means. I am trying to keep Python dependencies to a minimum and trying to keep enough functionality in the C++ side so that people can use LEAP without Python, but Python is easier to write some algorithms, so I am trying to make the best compromise.
5. I don't know anyone who uses the term "jig". Most people use a ball phantom for calibration, and this is often just called "ball phantom calibration" or something similar to this. Anyway, if you check the list on the LEAP main page, this is on that list.
6. I gave a lot of thought of whether to use projection or sinogram order in LEAP. I still think I made the right decision by going with projection order, and most people use this order. Still, I do agree that sinogram order has some advantages. Adding this feature would be a huge undertaking and would mean that every time I add a new algorithm to LEAP, I'd have to make sure it supports projection AND sinogram ordering which would slow down future development. So, I'd say I might add this feature, but it is not very likely.
7. I don't understand this request.
8. Yes, I like this one and it would be easy to do. This would be similar to outlier correction, but done only on specified detector pixels. I recently added a low signal correction algorithm which is another flavor of this.
9. In my opinion this is beyond the scope of LEAP. There are other packages that do this, such as aRTist.
10. Yep, I'll do this after I complete the GUI.

@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.

@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.

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:
f = f * 1000.0 / physics.mu('water',effectiveEnergy) - 1000.0
where "effectiveEnergy" is the effective energy of your total system spectral response in keV.

@kylechampley
May be this suggestion is not Overall useful but in My Application(Single material with some inclusions and cracks only) 8 bit image/slice output is more than enough and desirable.. right now output images are in 32bit float ..

@kylechampley , Did you go back to old future release list at your readme file ? Now it is updated,,,,

A beta version of the GUI has been released.

Ball phantom calibration function has been released in v1.22.

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. ?
Thanks!

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:

proj = Projector(...)
proj.leapct.set_curvedDetector()
proj.leapct.shift_detector(...)
proj.leapct.set_rampFilter(...)

@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.
So I hope that leacpct can support 16bit type projection data directly on cuda smearing function(SF or eSF) which can save cuda memory too(float output = float weight x short PrjVal).

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.

@kylechampley Ring artifact removing algorithm upgrading.

I know leapct supports some ring artifact removing features, and I tested these ones and I could see some needs too much time and some is showing too weak removed result. So I hope that leapct supports more robust and fast ring removing algorithm. Please check the reference site as like https://github.com/nghia-vo/sarepy. I hope that you can adapt some feature into your leapct's kernel codes.

@kylechampley, Are you busy in recent? There has been no more update in leapct package. I hope to see your new algorithm update in leapct.

Yes, I have been extremely busy lately and have taken a pause from adding new features to LEAP. There will be a new big release sometime in the next month or two when we add support for AMD GPUs and some other new features.

Linear motion based CT reconstruction supports.

I hope that LEAPCT can supports more flexible custom base geometry support without any limitation as like aligned to z-axis.

Linear ct