> 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/diffusion-qc.md).
# Diffusion QC
Diffusion data includes DTI, DKI, FBI, and other similar sequences. All sequences' QC notes should be recorded in independent fields in a given study's REDCap project.
There are a variety of possible diffusion QC issues to be aware of, such as signal dropouts and other movement artifacts.
However, due to our preprocessing procedures' ability to compensate for many known artifacts, the only artifact type that we note in our study documentation is ghosting.
Ghosting is, in short, the effect of blurred versions of images being repeated over the central image. [You can read more about ghosting here](https://mriquestions.com/ghosting.html). Since assessing artifacts is a subjective process, we attempt to quantify the level of ghosting via the criteria below.
All QC documentation is kept in REDCap. So once you decide on the level of ghosting in a particular dataset, you will document it in a module that looks like this:
