> For the complete documentation index, see [llms.txt](https://bridge-lab.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://bridge-lab.gitbook.io/docs/imaging/image-analysis/general-concepts/image-qc/raw-data-qc/t1-t2-qc.md).

# T1/T2 QC

For QCing structural images, we use a method developed by Dr. Lea Backhausen and her team at the University of Dresden. This method relies on assessing three features of structural images: image sharpness, ringing, contrast-to-noise ratio. Each of these features is classified on a three-level scale, similar in principle to assessing the level of ghosting in diffusion data.

This process is thoroughly documented by Dr. Backhausen and her team in this PDF.

{% file src="/files/VH7HzRFpm55aRoxdWnd2" %}

After you have assessed each structural image, your QC notes should be documented in the appropriate module:

<figure><img src="https://2091072347-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FiDgWwNd2W0ZWe2XnzLuq%2Fuploads%2FvKr6g9v8kiVNHS08bK3L%2Fimage.png?alt=media&amp;token=6aeb2b73-80e1-4e35-97c0-bd64b0944d04" alt=""><figcaption></figcaption></figure>

Note that this module outputs a final single number to indicate whether an image has passed QC or not, or whether further examination is necessary.