Output normalized images have a shape different from the template

Summary of what happened:

I’m new to fmriprep, and have been trying to use it to process my T1 data.
I used MNI152NLin2009cAsym as the template with a shape of 193x229x193. However, I found some of my output normalized images have a shape different from this.

Command used (and if a helper script was used, a link to the helper script or the command generated):

    data = getPandas('pat_data')
    conf = getConfig('data')
    train_inds = conf['indices']['pat']['train']
    test_inds = conf['indices']['pat']['test']
    data = data.loc[train_inds + test_inds].reset_index(drop=True)
    keys = data['KEY'].values
    for i, key in enumerate(keys):
        print('Processing {}'.format(key))
        cmd = 'fmriprep-docker data/bids/pat_raw data/bids/pat_fmriprep -i nipreps/fmriprep:latest --mem 8192 --output-space MNI152NLin2009cAsym --fs-no-reconall --anat-only --skip_bids_validation'
        cmd += ' --participant-label {}'.format(key)
        if not os.path.exists(os.path.join('data', 'bids', 'pat_fmriprep', 'sub-{}'.format(key))):


Environment (Docker, Singularity, custom installation):


Data formatted according to a validatable standard? Please provide the output of the validator:

Relevant log outputs (up to 20 lines):

Screenshots / relevant information:

This is because I missed res-01 after the template…


Out of curiosity: what is the voxel size of your functional images? Isn’t choosing a resolution of 1mm isotropic (res-01 for MNI152NLin2009cAsym template) upsampling your functional images? I didn’t hear much about upsampling with functional MRI processing (more common with diffusion MRI processing). The drawback with upsampling your functional images is that the data size can get very big.

But I may be wrong, you may have already a resolution close to 1mm isotropic of your functional images acquired at 7T!

Actually I’m using --anat-only flag to process T1 scans only.