Comments (5)
Yes, res_s
is the in-plane resolution of the simulated images, so it should be greater than the resolution of the source volume and the reconstructed volume..
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Hi @Edenzzzz, did you try to reconstruct using the default parameters, like this?
nesvor reconstruct \
--input-stacks /incoming/stack-1.nii.gz ... /incoming/stack-N.nii.gz \
--thicknesses <thick-1> ... <thick-N> \
--output-volume /outgoing/volume.nii.gz
The segmentation model (MONAIfbs) is used to remove the maternal tissue in the images and the model was trained on data with maternal tissue. Therefore, If you apply it to an image without background. It might remove some of the brain.
Did you check the simulated stack data? Could you share the simulated files with me?
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feta_stack3_gap=3.nii.gz
feta_stack2_gap=3.nii.gz
feta_stack1_gap=3.nii.gz
Thanks for your reply. Here are my stack files sliced from FeTA and I'm looking into the options you mentioned.
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Hi, you may try to save each stack as follows.
from nesvor.image import Stack
Stack(
slices,
transformation=transform,
resolution_x=res_s,
resolution_y=res_s,
thickness=s_thick,
gap=gap,
).get_volume().save('stack.nii')
You may find more information about the Stack class here.
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Hi, you may try to save each stack as follows.
from nesvor.image import Stack Stack( slices, transformation=transform, resolution_x=res_s, resolution_y=res_s, thickness=s_thick, gap=gap, ).get_volume().save('stack.nii')
You may find more information about the Stack class here.
Thanks for the reply! The result now looks closer to the input.
However just to be sure, based on my code is res_s always 1.5 or the resolution for the source volume (0.5 mm for FeTA) and res is the desired output stack resolution (0.8 according to the paper)? I've found only setting res_s to 1.5 produces reasonable results.
This repo is well-structured and promising, but I think users who are not expert in MRI processing may benefit from a bit more instruction on processing stacks and specifying resolutions. Thanks for your contribution!
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Related Issues (17)
- Understanding n-level-bias parameter defaults value HOT 5
- Efficiency with deepspeed HOT 1
- Three facets of data HOT 4
- Wrong Phantom in tests/slice_acquisition HOT 1
- --device option not passed down to tiny-cuda-nn HOT 2
- "SVD did not converge" error when using nesvor segment-stack HOT 2
- TypeError: 'ABCMeta' object is not subscriptable HOT 1
- What parameters I should set to improve the separation of the fetal brain regions and eliminate the unwanted areas?
- Computation of PSNR score : Low values
- nesvor segment-stack issue when using it standalone
- Can the overall method be applied to different medical images? HOT 1
- Can't get the full volume using actual thickness. HOT 1
- Visualizing learnt hash grid at different level HOT 2
- TCNN was not compiled with half-precision support! HOT 2
- ROI in the data is too large for SVoRT HOT 4
- Wrong checkpoint for DynUNet HOT 2
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