GitHub YouTube Wiki QC Sample

• Python ≥ 3.8
• Python packages: numpy, nibabel, SimpleITK, matplotlib, tqdm, templateflow, pandas
• SynthStrip via one of:
  • Singularity image: freesurfer/synthstrip:latest (recommended)
  • Docker image: freesurfer/synthstrip:latest
  • FreeSurfer installation with mri_synthstrip in $FREESURFER_HOME/bin
• (Optional) TemplateFlow cache (auto-downloads MNI152NLin2009cAsym)
• (Optional) conda for environment management

Example env setup

conda create -y -n smri python=3.10
conda activate smri
pip install numpy nibabel SimpleITK matplotlib tqdm templateflow pandas

The pipeline is config-driven. Edit config.py once and drive all scripts consistently. Below is a concise map of the most important keys, with usage tips and sensible defaults.

Paths

• INPUT_DIR: Root directory for input NIfTI files.
• OUTPUT_DIR: Root for all outputs (skullstrip / registration / postprocess / metadata).
• LOG_DIR: Log directory (automatically placed inside OUTPUT_DIR).
• FREESURFER_HOME: Path to FreeSurfer installation directory.
• SYNTHSTRIP_BIN: Full path to mri_synthstrip binary.
• SYNTHSTRIP_SIF_PATH: Singularity image path for SynthStrip (if used in container mode).

Structure

• STRUCTURE: Path pattern template (e.g., {root}/{subject}/{session}/anat).
• STAGE_ROOTS: Dictionary with stage-specific output subfolders (e.g., skullstrip, registration, qc).
• Legacy aliases: FORCE_REPROCESS, QC_DIR, SKULLSTRIP_DIR, etc. are auto-derived.

Scope

• DATASET_NAME: Name of the dataset being processed (e.g., "OpenNeuro ds004215").
• MODALITIES: List of modalities to process (usually ["T1w", "T2w"]).
• SUBJECTS, SESSIONS: Can be set to None or a filtered list.
• FORCE_REPROCESSING: If True, overwrites previous outputs.

Parallel Processing

• CPU_CORES: Number of CPU cores available on the system.
• ENABLE_PARALLEL: Whether to run stages in parallel.
• MAX_WORKERS: Number of parallel workers to spawn.

Logging

• LOG_LEVEL: Logging verbosity (e.g., "INFO", "DEBUG").
• LOG_FORMAT: Format string for log messages.

File patterns

• SKULLSTRIP_T1_FILE_PATTERN: Pattern to locate T1-weighted input NIfTI files.
• SKULLSTRIP_T2_FILE_PATTERN: Pattern to locate T2-weighted input NIfTI files.

QC configuration

• ENABLE_QC: Whether to enable QC checks.
• QC_GENERATE_IMAGES: Whether to save image previews for QC.
• QC_SUMMARY_FILE: Filename of the summary CSV (e.g., "qc_summary.csv").
• QC_BRAIN_VOLUME_MIN_ML, QC_BRAIN_VOLUME_MAX_ML: Min/max bounds for acceptable brain volume.
• DICE_PASS_THRESHOLD: Threshold for Dice overlap between masks.

Registration

• MNI_TEMPLATE_VERSION: e.g., "MNI152NLin2009cAsym".
• MNI_TEMPLATE_RESOLUTION: e.g., 1 (for 1mm resolution).

Post-process

• POSTPROCESS_APPLY_N4: Apply N4 bias field correction before normalization.
• POSTPROCESS_CROP_MARGIN: Crop margin in voxels after Z-score normalization.

CSV metadata

• GENERATE_CSV_METADATA: Whether to generate output CSV files.
• CSV_METADATA_DIR: Path to store CSV files.
• T1_CSV_FILENAME, T2_CSV_FILENAME: Output file names for metadata.
• PARTICIPANTS_TSV_PATH: Path to BIDS participants.tsv.
• CSV_COLUMNS: Ordered list of fields to appear in the metadata files.
• T1_PROCESSED_PATTERNS, T2_PROCESSED_PATTERNS: Patterns to locate output files.
• SEX_ENCODING, HANDEDNESS_ENCODING: Encoding dictionaries.
• MISSING_DATA_VALUE: Value used for missing entries (e.g., "n/a").
• REQUIRE_BOTH_MODALITIES: Whether both T1w and T2w are mandatory.
• VALIDATE_FILE_EXISTENCE: Whether to check for input/output file presence before processing.

# ============ GENERAL CONFIGURATION ============
DATASET_NAME = "OpenNeuro ds004215"
INPUT_DIR = "/path/to/REDACTED"
OUTPUT_DIR = "/path/to/REDACTED"
LOG_DIR = os.path.join(OUTPUT_DIR, "logs")

# ============ HARDWARE & PARALLEL ============
CPU_CORES = 64
ENABLE_PARALLEL = True
MAX_WORKERS = 8

# ============ LOGGING ============
LOG_LEVEL = "INFO"
LOG_FORMAT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"

# ============ FREESURFER ============
FREESURFER_HOME = "/path/to/REDACTED"
SYNTHSTRIP_BIN = os.path.join(FREESURFER_HOME, "bin", "mri_synthstrip")
SYNTHSTRIP_SIF_PATH = "/path/to/REDACTED"

# ============ FILE PATTERNS ============
SKULLSTRIP_T1_FILE_PATTERN = "*acq-MPRAGE_T1w.nii.gz"
SKULLSTRIP_T2_FILE_PATTERN = "*acq-CUBE_T2w.nii.gz"

# ============ PROCESSING SCOPE ============
MODALITIES = ["T1w", "T2w"]
SUBJECTS = None
SESSIONS = None
FORCE_REPROCESSING = True

# ============ QUALITY CONTROL ============
ENABLE_QC = True
QC_GENERATE_IMAGES = True
QC_SUMMARY_FILE = "qc_summary.csv"
QC_BRAIN_VOLUME_MIN_ML = 800
QC_BRAIN_VOLUME_MAX_ML = 2000
DICE_PASS_THRESHOLD = 0.8

# ============ REGISTRATION ============
MNI_TEMPLATE_VERSION = "MNI152NLin2009cAsym"
MNI_TEMPLATE_RESOLUTION = 1

# ============ POSTPROCESS ============
POSTPROCESS_APPLY_N4 = True
POSTPROCESS_CROP_MARGIN = 1

# ============ METADATA ============
GENERATE_CSV_METADATA = True
CSV_METADATA_DIR = os.path.join(OUTPUT_DIR, "metadata")
T1_CSV_FILENAME = "T1_metadata.csv"
T2_CSV_FILENAME = "T2_metadata.csv"
PARTICIPANTS_TSV_PATH = "/path/to/REDACTED"

CSV_COLUMNS = [
    "subjectId", "session", "MNI_Warped", "MNI_ZSCORE",
    "MNI_Z_Cropped", "age", "sex", "handedness"
]

T1_PROCESSED_PATTERNS = {
    "MNI_Warped": "*T1w*_mni_warped.nii.gz",
    "MNI_ZSCORE": "T1w_mni_zscore_fixed.nii.gz",
    "MNI_Z_Cropped": "T1w_mni_zscore_fixed_cropped.nii.gz"
}

T2_PROCESSED_PATTERNS = {
    "MNI_Warped": "*T2w*_mni_warped.nii.gz",
    "MNI_ZSCORE": "T2w_mni_zscore_fixed.nii.gz",
    "MNI_Z_Cropped": "T2w_mni_zscore_fixed_cropped.nii.gz"
}

SEX_ENCODING = {
    "male": 1,
    "female": 2,
    "n/a": 0
}

HANDEDNESS_ENCODING = {
    "right": 1,
    "left": 2,
    "ambidextrous": 3,
    "n/a": 0
}

MISSING_DATA_VALUE = "n/a"
REQUIRE_BOTH_MODALITIES = False
VALIDATE_FILE_EXISTENCE = True

# ============ STRUCTURE ============
STAGE_ROOTS = {
    "skullstrip":   os.path.join(OUTPUT_DIR, "skullstrip"),
    "registration": os.path.join(OUTPUT_DIR, "registration"), 
    "qc":           os.path.join(OUTPUT_DIR, "QC"),
}

STRUCTURE = "{root}/{subject}/{session}/anat"

# Legacy aliases
FORCE_REPROCESS = FORCE_REPROCESSING
QC_DIR = STAGE_ROOTS["qc"]
SKULLSTRIP_DIR = STAGE_ROOTS["skullstrip"] 
REGISTRATION_DIR = STAGE_ROOTS["registration"]
POSTPROCESS_MODALITIES = ["T1", "T2"]
POSTPROCESS_SUBJECTS = SUBJECTS

If your dataset is already in NIfTI, skip this step. Otherwise, this script converts DICOM folders into gzip-compressed NIfTI (BIDS-style layout) using dcm2niix.

Inputs

• DICOM series or a root directory containing series

Outputs

• {subject}/{session}/anat/*.nii.gz (+ JSON sidecars)

CLI

python dicom_to_nifti.py --dicom_dir /path/to/DICOM --output_dir /path/to/nifti --subject sub-0001 --session ses-01

Removes non-brain tissue using SynthStrip. The script attempts Singularity → Docker → FreeSurfer binary (in that order). Generates brain, mask, and QC images.

Inputs

• NIfTI files discovered via STRUCTURE and patterns:
  • *acq-MPRAGE_T1w.nii.gz
  • *acq-CUBE_T2w.nii.gz

Outputs (per subject/session in STAGE_ROOTS["skullstrip"])

• *_brain.nii.gz, *_brain_mask.nii.gz
• *_desc-qc.png montage overlay
• Global CSV: QC/qc_summary.csv (brain volume, PASS/FAIL, paths, message)

CLI

python skullstrip.py                  # process all
python skullstrip.py --subject sub-ON12345

Registers T1w to MNI152NLin2009cAsym (1mm) with SimpleITK (Mattes MI, multi-resolution), then applies the same transforms to T2w in the same session.

Method

• Rigid: Euler3D, shrink [4,2,1], linear
• Affine: 12-DOF, shrink [2,1], linear
• Final resampling: composite transform (rigid+affine) for single interpolation
• Saves *.mat transforms

Outputs (per subject/session in STAGE_ROOTS["registration"])

• *_mni_rigid_warped.nii.gz (QC/ref)
• *_mni_warped.nii.gz (final)
• other/*_rigid.mat, other/*_affine.mat

CLI

python reg.py
python reg.py --subject sub-ON12345

Warp masks to MNI, run optional N4 bias correction, compute masked Z-score, crop tightly around the brain, and save a multi-slice visualization.

Steps

1. Warp native masks → MNI with nearest-neighbor using the composite transform.
2. N4 bias correction (if POSTPROCESS_APPLY_N4 = True).
3. Masked Z-score: μ/σ computed within mask; background set to 0.
4. Crop: tight bounding box (+ POSTPROCESS_CROP_MARGIN), affine updated (qform/sform set).
5. Save multi-slice PNG montage.

Outputs (per subject/session; under the same registration anat/)

• T1w_mni_mask.nii.gz
• T1w_mni_warped_n4.nii.gz (if N4 enabled)
• T1w_mni_zscore_fixed.nii.gz
• T1w_mni_zscore_fixed_cropped.nii.gz
• multislice_visualization_<subj>.png
• (T2 equivalents when available; falls back to T1 mask if T2 mask missing)

CLI

python postprocess_mni_mask_zscore_crop.py
python postprocess_mni_mask_zscore_crop.py --subject sub-ON12345

• skullstrip.py writes per-subject QC montages and a global QC CSV with brain volume and status.
• structural_qc.py (optional helpers) can support extended validations (e.g., Dice vs reference) if needed.

QC CSV Columns

• input_file, output_file, modality, qc_status, brain_volume_ml, qc_image_path, error_message

Scans registration outputs and builds CSVs listing MNI warped, Z-score, and cropped paths, with optional demographics (participants.tsv) and encodings (sex, handedness).

Outputs

• metadata/T1_metadata.csv
• metadata/T2_metadata.csv

CLI

python generate_csv_metadata.py

# 0) (optional) environment
conda create -y -n smri python=3.10
conda activate smri
pip install numpy nibabel SimpleITK matplotlib tqdm templateflow pandas

# 1) configure
# edit config.py (private paths, thresholds)

# 2) brain extraction
python skullstrip.py

# 3) registration
python reg.py

# 4) post-process
python postprocess_mni_mask_zscore_crop.py

# 5) metadata tables
python generate_csv_metadata.py

• Skullstrip → STAGE_ROOTS["skullstrip"]
*_brain.nii.gz, *_brain_mask.nii.gz, *_desc-qc.png, global QC/qc_summary.csv
• Registration → STAGE_ROOTS["registration"]
*_mni_rigid_warped.nii.gz, *_mni_warped.nii.gz, other/*_rigid.mat, other/*_affine.mat
• Post-process → same anat/ under registration
T1w_mni_mask.nii.gz, T1w_mni_zscore_fixed.nii.gz, T1w_mni_zscore_fixed_cropped.nii.gz, multislice_visualization_<subj>.png (and T2 analogs)
• CSV metadata → CSV_METADATA_DIR
T1_metadata.csv, T2_metadata.csv

• Keep config.py under version control; avoid hardcoding private paths in scripts.
• Use Singularity/Docker for SynthStrip consistency across systems.
• Visually inspect outliers flagged by QC (extreme brain volumes, failed masks/registration).
• Pin TemplateFlow template (MNI152NLin2009cAsym, 1mm) to ensure reproducibility.
• Log CPU threads and memory; large cohorts benefit from batched runs.

• SynthStrip not found: ensure one of Singularity/Docker/FreeSurfer is available in PATH; try freesurfer/synthstrip:latest.
• Strange intensity after N4: disable N4 (POSTPROCESS_APPLY_N4 = False) and compare before/after Z-score.
• Registration misalignment: check *_mni_rigid_warped.nii.gz overlay; verify correct template resolution.
• CSV empty: confirm STRUCTURE and file patterns in config.py match your dataset.

• Author: Mohammad H. Abbasi (mabbasi [at] stanford.edu)
• Lab: Stanford University, STAI Lab (https://stai.stanford.edu)
• Created: 2025 | Version: 1.0.0 | Last update: Sep 18, 2025

Thanks to the open-source neuroimaging community (FreeSurfer/SynthStrip, TemplateFlow, SimpleITK, nibabel) and dataset providers.

If you use this pipeline in your research, please cite it as follows:

Abbasi, M.H., & Adeli, E. (2025). sMRI Processing Pipeline: A lightweight, end-to-end framework for BIDS-compatible structural MRI preprocessing and quality control.
Stanford Translational AI Lab, Stanford University. Zenodo.
https://doi.org/10.5281/zenodo.17503175

@software{abbasi2025smri,
  author       = {Mohammad Hassan Abbasi and Ehsan Adeli},
  title = {s{MRI} {P}rocessing {P}ipeline: A lightweight, end-to-end workflow for structural brain {MRI} preprocessing and quality control},
  year         = {2025},
  institution  = {Stanford Translational AI Lab, Stanford University},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17503175},
  url          = {https://doi.org/10.5281/zenodo.17503175},
  note = {Zenodo, doi: 10.5281/zenodo.17503175},
 }

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