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Hi FreeSurfer Developers,

I'm attempting to register two T2 images using mri_easyreg (FreeSurfer 7.4.1). This is what my bash code looks like:

#!/bin/bash

for subject in ./sub-?????.nii.gz
do
        mri_easyreg --ref ./mni152_t2_strip.nii.gz --flo $subject \
                    --ref_seg ./mni152_t2_strip_parc.nii.gz \
                    --flo_seg "${subject/.nii.gz/_parc.nii.gz}" \
                    --flo_reg "${subject/.nii.gz/_moved.nii.gz}" \
                    --fwd_field "${subject/.nii.gz/_fwd_fld.nii.gz}" \
                    --thread 24
        subject+=" Done!"
        echo $subject
done

This is what the output looks like when I execute the bash script:

2023-10-02 11:15:58.965124: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-10-02 11:15:58.988396: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-10-02 11:15:59.313149: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
using 24 threads
Segmentation of reference image already exists; reading from disk
Segmentation of floating image already exists; reading from disk
Linear registration
  Computing centroids and estimating affine transform
  Reading reference image
  Deforming reference image to reference space
  Deforming reference segmentation to reference space
  Normalizing intensities of reference image
  Reading floating image
  Deforming floating image to reference space
  Deforming floating segmentation to reference space
  Normalizing intensities of floating image
2023-10-02 11:16:08.118592: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2023-10-02 11:16:08.118640: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: kaibo-Alienware-Aurora-R13
2023-10-02 11:16:08.118647: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: kaibo-Alienware-Aurora-R13
2023-10-02 11:16:08.118766: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 510.108.3
2023-10-02 11:16:08.118794: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 510.108.3
2023-10-02 11:16:08.118801: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:309] kernel version seems to match DSO: 510.108.3
UserWarning: int_downsize is deprecated, use the int_resolution parameter.
UserWarning: unet_half_res is deprecated, use the svf_resolution parameter.
./easyreg.sh: line 3: 1588985 Killed                  mri_easyreg --ref ./mni152_t2_strip.nii.gz --flo $subject --ref_seg ./mni152_t2_strip_parc.nii.gz --flo_seg "${subject/.nii.gz/_parc.nii.gz}" --flo_reg "${subject/.nii.gz/_moved.nii.gz}" --fwd_field "${subject/.nii.gz/_fwd_fld.nii.gz}" --thread 24
./sub-00001.nii.gz Done!

When I checked the folder, I saw only the parcellation results. Neither the moved floating image nor the forward field were generated.

I initially thought this might be a memory problem and I set --thread 1 but I ended up getting the same result (Killed).

I've searched the list and there was another error (MailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg75001.html) similar to mine. But I don't think mine was related. Can anyone provide any insight into this?

FreeSurfer version: freesurfer-linux-ubuntu22_x86_64-7.4.1-20230614-7eb8460
Platform: Ubuntu 22.04.2 LTS
I am running the script on 12th Gen Intel® Core™ i9-12900KF × 24 with a 64.0 GiB memory and NVIDIA Corporation GA102 [GeForce RTX 3090].

Thanks,
Kaibo