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View Code? Open in Web Editor NEWDeep Cascade of Convolutional Neural Networks for MR Image Reconstruction: Implementation & Demo
License: Other
Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction: Implementation & Demo
License: Other
Hi,
I want to test with single mri image,which script should I use?
I have a question regarding the expression of the output of the data consistency layer in the noisy case.
We have out = (x + v * x_sampled) / (1 + v)
both here and here.
But this is done without consideration of whether the sampled point is in sampled region or not. I.e., the formal expression represented by this code would be, instead of eq. 6 of your paper:
x_rec(k) = x_cnn(k) / (1 + v) if k \notin Omega; (x_cnn(k) + v * x_u(k)) / (1 + v) else
I removed the \hat
to make it more readable and replaced lambda by v
.
I think the correct implementation of eq. 6 should be:
out = x + (v * (x_sampled - mask * x)) / (1 + v)
What do you think?
Hi @js3611 ,
In the paper, you mentioned that "our data is consisting of 10 fully sampled short-axis cardiac cine MR scans. Each scan contains a single slice SSFP acquisition with 30 temporal frames with a 320 ร 320
mm field of view and 10 mm slice thickness".
But in the data repo, there are only 256x256 x 30 frames.
I assume that this is one of 10 scans. Is it possible to release the rest of 9 scans so that I can train your model from scratch?
Best,
I noticed in the pytorch
models this StochasticDnCn
model where you stochastically drop blocks of conv + data consistency
(I guess during training).
I wanted to know if there was a paper where the rationale behind this model is detailed (with potentially results).
can't execute ''from lasagne.layers import prelu''
in your requirement txt, you said lasagne==v0.2, but I can just find v0.1
Is there the code of generating radial sampling mask?
I noticed that in main_crnn.py
, line 167 :
validate_err += err
as err
is a Varaible on GPU, such usage will take extra GPU memory (with PyTorch 1.0.1), maybe you should use err.item()
instead like line 147.
line 188 has the same problem as well
dummy_mask = cs.cartesian_mask((500, Nx, Ny), gauss_ivar,
sample_high_freq=True,
sample_centre=True, sample_n=8)
cartesian_mask(shape, acc, sample_n=10, centred=False)
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