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Add support for multi-coil acquisitions #150

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cncastillo opened this issue Jan 19, 2023 · 7 comments
Open

Add support for multi-coil acquisitions #150

cncastillo opened this issue Jan 19, 2023 · 7 comments
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@cncastillo
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cncastillo commented Jan 19, 2023

Support for multiple coils, with their corresponding sensitivities, to enable parallel imaging. As a second step, we can start thinking in pTx.

@cncastillo
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cncastillo commented Jun 22, 2023

After scanning a phantom with the Siemens MAGNETOM Free.Max (software XA50), and trying it with Koma, I have seen some problems:

  • plot_signal plots the raw data incorrectly, the coil noise measurements there, and the coils are concatenated.
  • When I read the .mrd file below, I can not reconstruct the image, even if I read the corresponding Pulseq file for the k-space coordinates.

PulseqEPITest.zip

The second bullet point could be caused by not using extensions, and the dimension of the data is not interpreted correctly in the .dat (Siemens raw data format). To get from the .dat to .mrd I used: https://github.com/ismrmrd/siemens_to_ismrmrd.

image

@aTrotier
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Hi, I have an SMS simulation and I need to validate a reconstruction algorithm (with multiple-coils)

Can I use the PR #548 for that ?

@cncastillo
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cncastillo commented Mar 17, 2025

Hi @aTrotier. Yes, that PR should generate multi-coil raw data!

We also included an example on how to use it (lit-07-Coils.jl). But it is kind of experimental, so let us know if it works. For the moment, we know that the simulation speed is 2x slower, but we are working on it.

Image

Slides:
https://docs.google.com/presentation/d/1eIkg4zcDQMYfMJgTXUVqHIFoQzNdhP9kiaYp_ZqAPUE/edit#slide=id.g33fe438c6a4_0_1218

@aTrotier
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Thanks @cncastillo and @Stockless for the PR.

I am able to generate a SMS with multi-coil sensitivities :

Image

and the sensitivity maps from ecalib

Image

@aTrotier
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With a naive sense reconstruction

img_unfold = zeros(ComplexF32,size(im_)[1:2]...,2)
# loop over the  the image
for y = 1:size(im_,2)
    for x = 1:size(im_,1)
        # pick out the sub-problem sensitivities
        s = (sens[x,y,2:3,:])'
        img_unfold[x,y,:] = pinv(s) * im_[x,y,1,1,:,1].data
    end
end

I get :

Image

I suppose I have an issue with my calibration slice n°2

@cncastillo
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Cool! Is that kind of what you expected? or there could be something that we are not still simulating correctly?

@aTrotier
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aTrotier commented Mar 19, 2025

The folded images are really close. So I guess it is an issue with my second calibration image.

I don't think it is an issue with Koma. I will check the .seq file

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