This project is the MNI 7T DICOM to BIDS converter, which is used at the Montreal Neurological Institute-Hospital to convert 7 Tesla DICOM scans to BIDS.
If you want to contribute to the MNI 7T DICOM to BIDS converter, read the contribution guide here.
You can download the latest version of the converter as an executable here. This executable should work on all Debian-based machines (including Ubuntu) from Debian 11 or newer.
To run the executable, use the following command:
mni7t_dcm2bids <dicom_study_path> <bids_dataset_path> --subject <subject_label> --session <session_label>The input DICOM directory must contain the DICOM files of a single session. The output BIDS directory can either be an empty directory (which can be created by the script) or be an existing BIDS directory (in which case the converted session is added to the existing BIDS).
You can run the latest version of the converter as a Docker image using the following command:
docker run ghcr.io/bic-mni/mni-7t-dicom-to-bids <dicom_study_path> <bids_dataset_path> --subject <subject_label> --session <session_label>You can find more information about the converter Docker image here. You might need to add bind mounts (--mount option) to link the input and output directories of the converter to the host.
You can also use the aforementioned Docker image of the converter with Apptainer using the following command:
apptainer run docker://ghcr.io/bic-mni/mni-7t-dicom-to-bids <dicom_study_path> <bids_dataset_path> --subject <subject_label> --session <session_label>Similarly, you might need to use bind paths (--bind option) to link the input and output directories of the converter to the host.
Finally, you can also install the converter as a Python package. To do so, run the following command in the relevant Python environment:
pip install git+https://github.com/bic-mni/mni-7t-dicom-to-bidsNote that you must also have dcm2niix (preferably a more recent version) installed on your machine.
| N | 7T Terra Siemens acquisition | BIDS | Directory |
|---|---|---|---|
| 1 | anat-T1w_acq_mprage_0.8mm_CSptx | T1w | anat |
| 2 | anat-T1w_acq-mp2rage_0.7mm_CSptx_INV1 | inv-1_MP2RAGE | anat |
| 3 | anat-T1w_acq-mp2rage_0.7mm_CSptx_INV2 | inv-2_MP2RAGE | anat |
| 4 | anat-T1w_acq-mp2rage_0.7mm_CSptx_T1_Images | T1map | anat |
| 5 | anat-T1w_acq-mp2rage_0.7mm_CSptx_UNI_Images | UNIT1 | anat |
| 6 | anat-T1w_acq-mp2rage_0.7mm_CSptx_UNI-DEN | desc-denoised_UNIT1 | anat |
| 7 | anat-flair_acq-0p7iso_UPAdia | FLAIR | anat |
| 8 | CLEAR-SWI_anat-T2star_acq-me_gre_0*7iso_ASPIRE | acq-SWI_T2starw | anat |
| 9 | Romeo_P_anat-T2star_acq-me_gre_0*7iso_ASPIRE | acq-romeo_T2starw | anat |
| 10 | Romeo_Mask_anat-T2star_acq-me_gre_0*7iso_ASPIRE | acq-romeo_desc-mask_T2starw | anat |
| 11 | Romeo_B0_anat-T2star_acq-me_gre_0*7iso_ASPIRE | acq-romeo_desc-unwrapped_T2starw | anat |
| 12 | Aspire_M_anat-T2star_acq-me_gre_0*7iso_ASPIRE | acq-aspire_part-mag_T2starw | anat |
| 13 | Aspire_P_anat-T2star_acq-me_gre_0*7iso_ASPIRE | acq-aspire_part-phase_T2starw | anat |
| 14 | EchoCombined_anat-T2star_acq-me_gre_0*7iso_ASPIRE | acq-aspire_desc-echoCombined_T2starw | anat |
| 15 | sensitivity_corrected_mag_anat-T2star_acq-me_gre_0*7iso_ASPIRE | acq-aspire_desc-echoCombinedSensitivityCorrected_T2starw | anat |
| 16 | T2star_anat-T2star_acq-me_gre_0*7iso_ASPIRE | acq-aspire_[T2starw,T2starmap] | anat |
| 17 | anat-mtw_acq-MTON_07mm | acq-mtw_mt-on_MTR | anat |
| 18 | anat-mtw_acq-MTOFF_07mm | acq-mtw_mt-off_MTR | anat |
| 19 | anat-mtw_acq-T1w_07mm | acq-mtw_T1w | anat |
| 20 | anat-nm_acq-MTboost_sag_0.55mm | acq-neuromelaninMTw_T1w | anat |
| 21 | anat-angio_acq-tof_03mm_inplane | angio | anat |
| 22 | anat-angio_acq-tof_03mm_inplane_MIP_SAG | acq-sag_angio | anat |
| 23 | anat-angio_acq-tof_03mm_inplane_MIP_COR | acq-cor_angio | anat |
| 24 | anat-angio_acq-tof_03mm_inplane_MIP_TRA | acq-tra_angio | anat |
The acquisitions
acq-romeo_part-phase_T2starw,acq-aspire_part-mag_T2starw, andacq-aspire_part-phase_T2starweach have five echoes. The final string will include the identifierecho-followed by the echo number. For example:acq-aspire_echo-1_part-mag_T2starw.
| N | 7T Terra Siemens acquisition | BIDS | Directory |
|---|---|---|---|
| 1 | fmap-b1_tra_p2 | acq-[anat,sfam]_TB1TFL | fmap |
| 2 | fmap-b1_acq-sag_p2 | acq-[anat,sfam]_TB1TFL | fmap |
| 3 | fmap-fmri_acq-mbep2d_SE_19mm_dir-AP | acq-fmri_dir-AP_epi | fmap |
| 4 | fmap-fmri_acq-mbep2d_SE_19mm_dir-PA | acq-fmri_dir-PA_epi | fmap |
| N | 7T Terra Siemens acquisition | BIDS | Directory |
|---|---|---|---|
| 1 | func-cross_acq-ep2d_MJC_19mm | task-rest_bold | func |
| 2 | func-cloudy_acq-ep2d_MJC_19mm | task-cloudy_bold | func |
| 3 | func-present_acq-mbep2d_ME_19mm | task-present_bold | func |
Each functional MRI acquisition includes three echoes and a phase. The final string will contain the identifier
echo-followed by the echo number (e.g.,task-rest_echo-1_bold). Additionally, the stringpart-phasewill be included to identify the phase (e.g.,task-rest_echo-1_part-phase_bold).
| N | 7T Terra Siemens acquisition | BIDS | Directory |
|---|---|---|---|
| 1 | *dwi_acq_b0_PA | acq-b0_dir-PA_dwi | dwi |
| 2 | *dwi_acq_b0_PA_SBRef | acq-b0_dir-PA_sbref | dwi |
| 3 | *dwi_acq_multib_38dir_AP_acc9 | acq-multib38_dir-AP_dwi | dwi |
| 4 | *dwi_acq_multib_38dir_AP_acc9_SBRef | acq-multib38_dir-AP_sbref | dwi |
| 5 | *dwi_acq_multib_70dir_AP_acc9 | acq-multib70_dir-AP_dwi | dwi |
| 6 | *dwi_acq_multib_70dir_AP_acc9_SBRef | acq-multib70_dir-AP_sbref | dwi |
The string
part-phasewill be included to identify the phase acquisitions (e.g.,acq-multib38_dir-AP_part-phase_dwi).
| Abbreviation | Description |
|---|---|
| AP | Anterio-Posterior |
| PA | Postero-anterior |
| mtw | Magnetic transfer weighted |
| sfmap | Scaled flip angle map |
| tof | Time of flight |
| multib | Multi shell N directions |
| semphon | Semantic-phonetic |
| romeo | Rapid opensource minimum spanning tree algorithm |
| aspire | Combination of multi-channel phase data from multi-echo acquisitions |
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Eckstein K, Dymerska B, Bachrata B, Bogner W, Poljanc K, Trattnig S, Robinson SD. Computationally efficient combination of multi‐channel phase data from multi‐echo acquisitions (ASPIRE). Magnetic resonance in medicine. 2018 Jun;79(6):2996-3006. https://doi.org/10.1002/mrm.26963
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Dymerska B, Eckstein K, Bachrata B, Siow B, Trattnig S, Shmueli K, Robinson SD. Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO). Magnetic resonance in medicine. 2021 Apr;85(4):2294-308. https://doi.org/10.1002/mrm.28563
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Sasaki M, Shibata E, Tohyama K, Takahashi J, Otsuka K, Tsuchiya K, Takahashi S, Ehara S, Terayama Y, Sakai A. Neuromelanin magnetic resonance imaging of locus ceruleus and substantia nigra in Parkinson's disease. Neuroreport. 2006 Jul 31;17(11):1215-8. https://doi.org/10.1097/01.wnr.0000227984.84927.a7
This project can be compiled and distributed as an executable using PyInstaller, the compilation process is described in the COMPILATION.md file.
This DICOM to BIDS converter is a rewrite of Raul Cruces' MPN 7T pipeline DICOM to BIDS converter. The present converter aims to provide a similar behavior (up to versions) with enhanced safety checks, portability, performance, and maintainability.