roflmaostc / radonka.jl Goto Github PK

This package already looks impressive, so I was wondering your goals for this sort of package? I work with CT imaging physics for my Ph.D. and if this is something you plan on spending a lot of time on, I would be interesting in collaborating wherever possible.

I am very interested in having something like KomaMRI.jl for CT and this seems like a good starting point - and maybe something like https://github.com/xcist/main could be useful for that goal.

Do you have any larger goals like that for this package?

We loose about a factor of 4 compared to torch-radon in the forward pass

https://github.com/carterbox/torch-radon/blob/master/src/forward.cu

https://roflmaostc.github.io/RadonKA.jl/dev/benchmark/

Would be interesting to find out why exactly.

Please consider publishing this repo on your github homepage too, since it's a quite usefule repo I think.

Hi,

Is there a way to obtain the "Radom matrix" ?

I will try to be more clear.

Based on the direction the signal (the electromagnetic wave), we could define a matrix
of zeros and ones where each row would represent where the signal traveled through the image.

So that we could write something like A * f, where f would be representative of the image, say vec(Image).
Here we can think of the first row of A[1, :] as a row-vector whose components that are 1 represent where the signal
traveled through the image.

More precisely, check this self-consistent material slide 38.

I been to developing some Gaussian process regression approach computing things in a recursive way without
any matrix inversion and would be of great help if I could recover such matrix A from this package.

Any help on this ?

Thank you.

Current issues:

• restricted to in-circle
• even number of input, odd number of output.

Roadmap:

• allow different samplings of rays, not only integer steps
• allow arbitrary directions of rays
• sample the whole array, not only the in-circle

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