# 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="/files/cwnK0siZJLPKS7FrleXH" 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.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://bridge-lab.gitbook.io/docs/imaging/image-analysis/general-concepts/image-qc/raw-data-qc/t1-t2-qc.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.