preproc_t1
Subworkflow for T1 image pre-processing, from denoising to brain extraction and cropping. It requires three input channels, one with a raw T1 image you want to process (ch_image), one with a T1 template (ch_template) and one with a brain probability mask (ch_probability_map). The aim of the workflow is to carry out the first stages of pre-processing, through denoising, N4 correction, resampling and brain extraction, right up to the cropping stage. The resulting T1 is corrected, resampled, brain extracted and cropped.
You can retrieve the file after each step if you don’t want to run the entire subworkflow. The next steps would be to register the resulting T1-corrected image with the DWI-corrected image with, for example, the REGISTRATION subworkflow. IMPORTANT : the subworkflow is only reproducible with when running ANTs BET using a single thread.
----------- Steps -----------
1. Denoising (nlmeans, scil).
Used to remove the noise induced by the MRI acquisition, enhance the signal to noise ratio and improve the image quality and following metrics. The denoising is performed in the original spatial resolution and uses the nlmeans method.
2. N4 Image Intensities Bias Correction (N4BiasFieldCorrection, ANTs).
Used to normalize the image intensities and reduce biases (e.g. signal loss with distance in multi-channel head coils), while preserving contrasts between tissues.
3. Resample (DIPY).
Resamples the T1 to an isotropic spatial resolution. The default is 1mm, a standard in humans which usually facilitate registration with corrected DWI images. This spatial resolution is modifiable in the configuration file.
4. Brain Extraction (ANTs - default, freesurfer).
Isolates the brain tissue voxels from the remaining image. Also creates a binary brain mask. This brain extraction is required for the T1 to DWI Registration. IMPORTANT : when using ANTs, brain extraction is reproducible only when run using a single thread.
5. Cropping (scil).
Crops the empty planes around the brain to optimize the next processing steps.
Subworkflow based on Tractoflow : https://www.sciencedirect.com/science/article/pii/S105381192030375X?via%3Dihub
Keywords : T1, preprocessing, nlmeans, n4, resample, bet, crop
Components : denoising/nlmeans, preproc/n4, image/resample, betcrop/antsbet, betcrop/synthstrip, image/cropvolume, utils_options
Inputs
Section titled “Inputs”| Type | Description | Mandatory | Pattern | |
|---|---|---|---|---|
| ch_image | file | The input channel containing the anatomical image. Structure : tuple val(meta), path(image)
| True | *.\{nii,nii.gz\} |
| ch_template | file | The input channel containing the anatomical template for antsBET. Structure : tuple val(meta), path(image)
| False | *.\{nii,nii.gz\} |
| ch_probability_map | file | The input channel containing the brain probability mask for antsBET, with intensity range 1 (definitely brain) to 0 (definitely background). Structure : tuple val(meta), path(image)
| False | *.\{nii,nii.gz\} |
| ch_template_mask | file | Brain mask (in template space) used to restrict metric computation when performing registration. | False | *.\{nii,nii.gz\} |
| ch_initial_affine | file | Affine transform from T1w space to DWI space, used as initialization for registration algorithms. | False | *.\{mat/txt\} |
| ch_ref_mask | file | The input channel containing a reference brain mask for all processing. Optional Structure : tuple val(meta), path(mask)
| False | *.\{nii,nii.gz\} |
| ch_ref_n4 | file | The input channel containing the reference and the reference mask for the n4 correction. Optional Structure : tuple val(meta), path(ref)
| False | *.\{nii,nii.gz\} |
| ch_ref_resample | file | The input channel containing the reference for the resampling. Optional Structure : tuple val(meta), path(ref)
| False | *.\{nii,nii.gz\} |
| ch_weights | file | The input channel containing an alternative model weights for synthstrip. Optional Structure : tuple val(meta), path(weights)
| False | *.pt |
| options | map | Map of options for the preproc_t1 subworkflow.
| False |
Outputs
Section titled “Outputs”| Type | Description | Mandatory | Pattern | |
|---|---|---|---|---|
| image_nlmeans | file | Channel containing the anatomical image after the nlmeans denoising. Structure : tuple val(meta), path(image)
| True | *.\{nii,nii.gz\} |
| image_N4 | file | Channel containing the anatomical image after the N4 correction. Structure : tuple val(meta), path(image)
| True | *.\{nii,nii.gz\} |
| image_resample | file | Channel containing the anatomical image after the resampling. Structure : tuple val(meta), path(image)
| True | *.\{nii,nii.gz\} |
| image_bet | file | Channel containing the anatomical image after the brain-extraction. Structure : tuple val(meta), path(t1)
| True | *.\{nii,nii.gz\} |
| mask_bet | file | Channel containing the binary mask after the brain-extraction. Structure : tuple val(meta), path(mask)
| True | *.\{nii,nii.gz\} |
| crop_box | file | Channel containing the bouding box defining the limits of the crop. Structure : tuple val(meta), path(image)
| True | *.\{pkl\} |
| mask_final | file | Channel containing the binary mask after the all preprocessing. Structure : tuple val(meta), path(mask)
| True | *.\{nii,nii.gz\} |
| t1_final | file | Channel containing the anatomical image after the all preprocessing. Structure : tuple val(meta), path(image)
| True | *.\{nii,nii.gz\} |
| versions | file | File containing software versions Structure : tuple path(versions)
| True | versions.yml |
Authors
Section titled “Authors”Last updated : 2026-03-17