Spatial Normalization: Is it Good Enough?

✅ Scroll to the Spatial normalization of the anatomical image section.

MRIQC Visual report: Dynamic panel showing spatial normalization

✅ In the upper left corner is a label called moving. Click it to see the quality of registration. The fixed image is the MNI template and the moving image is sub-CAM003 registered into MNI space. As expected, the structures are roughly aligned:

✅ Look at the ventricles in the axial slices.

✅ Look at the corpus callosum, brainstem, and cerebellum in the sagittal views. This is pretty typical of normalization and looks fine. Generally, when normalization goes bad, it goes REALLY bad (e.g., the brain is upside-down, squished, stretched, or stuck in one corner of the image). That is all you are looking for, some kind of shockingly bad alignment.

Note

Compared to the pristine MNI brain, the participant's images are messy because the brain has not been perfectly extracted. There is non-brain tissue around the outside of the brain. This is all perfectly normal. Scroll down to the sections Brain mask and Brain tissue segmentation. The algorithm has done a good job of identifying the brain and distinguishing tissue types.

✅ Scroll through the rest of the sections, again, nothing is shockingly bad.

✅ Select each section under Other.

At the bottom of the report, the Extracted Image quality metrics section (red underline) contains the actual values of each image quality metric. To see the metrics, expand the section (red arrow).

MRIQC visual report: Bottom Dropdown showing IQMs

However, these metrics are also available as a text file (group_T1w.tsv), which is more useful for analysis.

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