I have been analysing hippocampus PRESS data from GE scanner (TE / TR: 30 / 3000, dimensions: 20 x 20 x 15 mm = 6 ml). For one participant in the dataset I am having an issue with the OspreyProcess step as the spectra does not appear to be correctly aligned after the averaging and alignment steps (see image attached).
I was wondering if there is a reason for this and if this is something that can be corrected?
Yep, that’s a little concerning - I think we’ve made a bunch of changes to the frequency referencing recently, so it’s worth trying a newer version than 2.5
Also:
I will try installing the new version of Osprey and rerunning.
I have attached the water reference plot below.
The job file is set to perform eddy-current correction.
Yeah, there’s a big eddy-current artefact in the water signal (which just looks really odd in many ways). Can you try de-activating the ECC correction flag? (I always thought that ECC is not already applied to the P-file data - checking with @noeskera for confirmation).
Yes. The pfiles contain unprocessed data. If this is our default PRESS protocol (PROBE-P) with NEX = 8 then it should contain 2 reference frames in the beginning and then CV4/NEX data frames. Doing ECC with this kind of reference signal should work fine. I don’t see any sideband-artefact signal in the data that could affect the alignment. How did the product recon look like?
Need to correct me. If the pre-alignment water signal is showing the 2 reference frames then you do have a massive shift in-between those 2 (motion?). But post-alignment looks like one of those so seemed to work.
I tried re-running with Osprey 2.9.0 and this did not change the alignment.
I have rerun for this participant with ECC de-activated and that does seem to have fixed the alignment (see attached).
For the rest of the participants we did not have this issue with ECC set. Do you know why it has caused an issue for this participant?
Would you recommend running the other participants without ECC?
That is impossible to answer without seeing the data. The pre-alignment data for this particular dataset you showed suggest that something was going on (maybe crass movement during the collection of the water reference transients). If you tell me that this particular dataset is an outlier and all the other ones look different (better), then I certainly would not recommend running all of them with a different setting.
