Closed JinDJsuper closed 3 months ago
This is going to be a tough problem.
My suggestion is to
Which are more "aggressive" in terms of large-scale deformations by travelling the image pyramid at larger scales and stepping down with smaller steps.
I have used these on a menagerie of animals already, would love to see how they do with dog brains!
I really appreciate your suggestions and the methods you provided. I'm a bit of a beginner in this area, so I'm having trouble understanding the correct usage of your script and how to adjust the parameters properly. For example,
bash antsRegistration_affine_SyN.sh dogbrain_brain.nii.gz template.nii.gz test_
,error is UTC [ failure] Required program ants_generate_iterations.py not found!
"This might be a very basic question, but I'm still learning.
You need to have a copy of the script, it needs to be chmod +x
and it needs to be on the PATH.
I suggest you git clone the repo and then export PATH=/path/to/minc-toolkit-extras:$PATH
That will allow you to run the command without "bash" and fix the "can't find required command"
thank you very much, i try this code,and maybe not some changs for my image. I think there might be some issues with the commands.This is my command:
bash /home/jindj/download/minc-toolkit-extras-master/antsRegistration_affine_SyN.sh --syn-metric CC[3] --syn-control 0.25,3,0 --syn-shrink-factors 8x4x2x1 --syn-smoothing-sigmas 3x2x1x0vox --syn-convergence 100x70x50x20 --linear-shrink-factors 6x4x2x1 --linear-smoothing-sigmas 4x2x1x0vox --linear-convergence 1000x500x250x100 dogbrain46_brain.nii.gz begle_Deformable_template0_rigid.nii.gzWarped.nii.gz testCC30.25
antsApplyTransforms -3 -r dogbrain46_brain.nii.gz -i begle_Deformable_template0_rigid.nii.gzWarped.nii.gz -o regcc3_0.25.nii.gz -t [ testcc3_0.25_0GenericAffine.mat, 1 ] -t testcc3_0.25_1InverseWarp.nii.gz
This is my command:
You are overrriding all the automatic features. Just use:
antsRegistration_affine_SyN.sh --syn-control 0.25,3,0 dogbrain46_brain.nii.gz begle_Deformable_template0_rigid.nii.gzWarped.nii.gz testCC3_0.25_
hi: thank you very much for your help
this is my command and its image is No.1 and No .2 is case image bash /home/jindj/download/minc-toolkit-extras-master/antsRegistration_affine_SyN.sh --syn-control 0.25,3,0 dogbrain46_brain.nii.gz begle_Deformable_template0_rigid.nii.gzWarped.nii.gz testCC30.25
its image is No. 3 bash /home/jindj/download/minc-toolkit-extras-master/antsRegistration_affine_SyN.sh --syn-control 0.25,2,0 dogbrain46_brain.nii.gz begle_Deformable_template0_rigid.nii.gzWarped.nii.gz testCC3_0.252
No. 4 image is default command
i think that there are no more changes in these image ,so What should I do next ?
Hi,
First, I think you may be running into violating the fundamental assumptions of image registration, that the anatomy present in each image has a 1:1 correspondence, allowing for in-theory alignment.
But in general here, I think I would like to know a little more about the problem you're trying to solve, not the specific step, in particular I'm worried about https://xyproblem.info/
Can you give a full overview of what problem you're trying to solve, rather than this specific registration step?
I would also be interested in looking at the actual data you're using, I'm not a fan of a single slice-screenshot as any sort of representation of the data.
Operating system and version
Ubuntu 20.04
CPU architecture
x86_64 (PC, Intel Mac, other Intel/AMD)
ANTs code version
ants-2.5.0
ANTs installation type
Compiled from source
Summary of the problem
Hi I have some issues with image registration, particularly with the registration of samples showing wide-ranging brain developmental abnormalities, which is not going as ideally as I'd hoped. I'm working on an analysis of animal‘s brain structure, and my preliminary work involved using some animal‘s MRI images with antsMultivariateTemplateConstruction2.sh to build individual templates for our project. Here's the command I used in command1.
My T1 image has a voxel size of 0.50.50.5. I used antsRegistrationSyN.sh to register the T1 image of a case to an individual template, but the results were not satisfactory. So, I revisited the answers in the issue and subsequently modified the parameters in antsRegistration, changing --transform SyN[ 0.1,3,0 ] \ --metric CC[ "$subject_template", "$input",1,4 ] \ to --transform SyN[ 0.25,3,0 ] \ --metric CC[ "$subject_template", "$input",1,3 ] \ . This adjustment yielded reasonably good registration results. Here is the specific command I used in command2.
The issues I'm facing are with the registration of partially missing brain tissue. The registration doesn't appropriately account for the missing parts; instead, residual tissues remain. Additionally, there are halo effects around the hippocampus and some deep nuclei, indicating significant changes in those areas. Is this the limit of what ANTs can achieve? Could adjusting certain parameters improve the registration outcome?
Thanks very much,
DJ
This is t1 image of case,left image is ${input_name}2sub_syn0.25_3_InverseWarped.nii.gz ,which is template inverse registration to case and right image is original case image
This is another more extreme case.
Commands to reproduce the problem.
command1: antsMultivariateTemplateConstruction2.sh -d 3 -i 5 -k 1 -f 6x4x2x1 -s 4x2x1x0vox -q 50x100x70x20 -t SyN -m CC -c 0 -r 0 -z ./template_result/dog_Affine_template0.nii.gz -o ./template_result/dog_Deformable_dogbrain*_brain.nii.gz
command2: antsRegistration --verbose 1 --dimensionality 3 --float 0 \ --collapse-output-transforms 1 \ --output [ "$reg_output_dir"/${input_name}2sub_syn0.253,"$reg_output_dir"/${input_name}2sub_syn0.25_3_Warped.nii.gz, "$reg_output_dir"/${input_name}2sub_syn0.25_3_InverseWarped.nii.gz ] \ --interpolation Linear \ --use-histogram-matching 0 \ --winsorize-image-intensities [ 0.005,0.995 ] \ --initial-moving-transform [ "$subject_template", "$input",1 ] \ --transform Rigid[ 0.1 ] \ --metric MI[ "$subject_template", "$input",1,32,Regular,0.25 ] \ --convergence [ 1000x500x250x100,1e-6,10 ] \ --shrink-factors 8x4x2x1 \ --smoothing-sigmas 3x2x1x0vox \ --transform Affine[ 0.1 ] \ --metric MI[ "$subject_template", "$input",1,32,Regular,0.25 ] \ --convergence [ 1000x500x250x100,1e-6,10 ] \ --shrink-factors 8x4x2x1 \ --smoothing-sigmas 3x2x1x0vox \ --transform SyN[ 0.25,3,0 ] \ --metric CC[ "$subject_template", "$input",1,3 ] \ --convergence [ 100x70x50x20,1e-6,10 ] \ --shrink-factors 8x4x2x1 \ --smoothing-sigmas 3x2x1x0vox
Output of the command with verbose output.
#
Data to reproduce the problem
case1.zip case2.zip