Understanding MRI scan outputs and viewing them with FSL

An MRI scan often produces many .nii (NIfTI) files because of the complexity and richness of MRI data. To follow this post, one can download the BraTS dataset on Kaggle. Here’s a breakdown of why this happens:

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🧠 1. Multiple Imaging Sequences / Contrasts

MRI scans are typically acquired using different sequences, each capturing different tissue properties:

• T1-weighted (anatomical detail)
• T2-weighted (fluid-sensitive)
• FLAIR (suppresses CSF to highlight lesions)
• DWI (diffusion-weighted)
• fMRI (functional time-series)
• SWI, MPRAGE, etc.

Each of these becomes a separate .nii file.

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🧬 2. Multi-volume or Time-series Data

Some MRI types (like fMRI or DWI) capture many volumes over time or across directions:

• A single fMRI scan might produce a 4D .nii file: 3 spatial + 1 time dimension.
• DWI can include many directions (e.g. 64 or 128) → saved as a 4D .nii.

Sometimes these are split into individual 3D .nii files for each volume.

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🧰 3. Post-processing Outputs

Analysis tools generate many derivative .nii files:

• Brain extraction → brain-only mask: brain_mask.nii.gz
• Segmentation → gray matter, white matter, CSF maps
• Registration/alignment → resliced or transformed versions
• Functional analyses → statistical maps, activation masks, etc.

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🏗 4. Multiple Slices / Series Saved Separately

Some systems or conversion tools (e.g. dcm2niix) may save:

• Each slice or series as a separate .nii file.
• You might get one .nii per anatomical region, run, or repetition.

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🛠 5. Conversion from DICOM to NIfTI

DICOM → NIfTI conversion tools (like dcm2niix) often split by:

• Series
• Echo time / phase
• Coil
• Scan repetition

Result: dozens of .nii files for one study.

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📁 Example: fMRI Scan Session Might Generate

T1.nii.gz
T2.nii.gz
fMRI_run1.nii.gz
fMRI_run2.nii.gz
brain_mask.nii.gz
gm_segmentation.nii.gz
motion_params.txt

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🧩 *_seg.nii — Segmentation File

• seg stands for segmentation.
• This file contains labeled regions of the brain (or other structures).
• Common in structural MRI processing, like:
  • Tissue segmentation (gray matter, white matter, CSF)
  • Lesion segmentation
  • Tumor regions (in brain tumor studies)
• Voxel values are usually integers representing different classes: 0 = background 1 = gray matter 2 = white matter 3 = CSF ...

🔧 Used in tools like:

• SPM, FSL’s FAST, FreeSurfer
• BraTS datasets: seg.nii.gz for brain tumor masks

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💉 *_ce.nii — Contrast-Enhanced Image

• ce usually stands for contrast-enhanced.
• This is typically a T1-weighted image acquired after injection of contrast agent (e.g., Gadolinium).
• Helps highlight:
  • Tumors
  • Blood-brain barrier breakdown
  • Vascular structures
• Used heavily in oncology, neuroinflammation, and angiography.

🧠 Example:

• T1ce.nii.gz = T1-weighted contrast-enhanced scan (commonly used in BraTS and glioma datasets).

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📂 Example File Set (Brain Tumor Imaging)

T1.nii.gz         # T1-weighted scan
T1ce.nii.gz       # T1 with contrast enhancement
T2.nii.gz         # T2-weighted scan
FLAIR.nii.gz      # FLAIR scan
seg.nii.gz        # Tumor segmentation mask

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Suffix	Meaning	Purpose
_seg	Segmentation	Labeled brain regions or pathology
_ce	Contrast-Enhanced	T1 MRI taken after contrast agent

✅ Summary

You get many .nii files from an MRI session because each file represents:

• A different imaging modality, volume, or processing step,
• Or is a result of data conversion practices.

How to view MRI scan outputs with FSL

To install FSL for viewing MRI scan outputs, go to

https://fsl.fmrib.ox.ac.uk/fsl/docs/#/install/index

and follow the instructions.

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✅ Step-by-Step: View .nii File with FSLeyes

1. Open Terminal (Linux/macOS) or FSL Shell (Windows via WSL or FSL virtual machine).
2. Run FSLeyes with your NIfTI file:
   fsleyes your_file.nii.gz
   If your file is not compressed:
   fsleyes your_file.nii fsleyes

One can also load multiple overlays, e.g. an anatomical underlay and a mask:
fsleyes anatomical.nii.gz mask.nii.gz

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💡 Tips

• If fsleyes doesn’t open, check that FSL is installed and properly sourced: source $FSLDIR/etc/fslconf/fsl.sh
• If you’re using a remote machine (e.g. SSH), you may need X11 forwarding enabled: ssh -X user@remote
• If using VSCode, and conda is automatically initialized when the terminal starts, use conda deactivate before opening the file(s) with FSL.

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More:

Some interesting videos can be found in this youtube playlist.