About ZeroFilled2DSequence and Masked2DSequence in Unet

[zaccharieramzi]

About ZeroFilled2DSequence and Masked2DSequence in Unet

[zaccharieramzi]

Hi @fharman ,

Can you show me the code that you used to generate these outputs?

Mainly, are you using only one contrast?

[fharman]

Hi Zaccharie,

test_path = '/content/drive/My Drive/UNET_Colab/knee_singlecoil_test_v2/singlecoil_test_v2/'
test_gen_zero = ZeroFilled2DSequence(test_path, af=AF, norm=True, mode='testing', mask_seed=0)
test_gen_scaled = Masked2DSequence(test_path, mode='testing', af=AF, scale_factor=1e6, mask_seed=0)

len(test_gen_scaled)
50

import os
path, dirs, files = next(os.walk("/content/drive/My Drive/UNET_Colab/knee_singlecoil_test_v2/singlecoil_test_v2/"))
file_count = len(files)
print(file_count)
108

I don't overcome this problem. Yes, i just only used Fastmri Single Coil Knee Dataset. (PDFS)

Best regards,

[zaccharieramzi]

With this setting, you are actually considering all contrasts.
However, you are only considering the acceleration factor AF (I don't to what it is set).
So you are only getting the test files that have been retrospectively under-sampled with and acceleration factor of AF, which is roughly half of them.

[fharman]

AF=4,
What is your recommendation for full set path? Because with these commands, path files are skipped by 2 files. Consequently, path file numbers are decreasing the half.

Thank you for your return,

Best regards,

[zaccharieramzi]

I recommend treating both acceleration factors separately.
You would do the same thing just with AF=4 and AF=8.

With the current implementation it's not possible to have a a generator for both acceleration factors but I intend to keep it that way since I think it's important to separate both. You can still have a model that reconstructs agnostically the 2 acceleration factors, you would just need to run it twice.