Hi Michael,
Is it still possible to use the separated control image for calibration if we do not have much more info about the background suppression? Our MRI technician did not think BS was used so unfortunately I am
doubtful he will have any specific parameter information pertaining this, but if there is anything helpful I can get from the dicom header, I could definitely get that info.
Lastly, we were running Region Analysis to obtain perfusion in a custom ROI, and noticed
the grey matter perfusion reported by perfusion_gm_mean.txt and the 80%+ GM figure from the region_anaysis_gm.csv were very inconsistent with one another (one was ~5x larger than the other). Do you think background
suppression is related to this discrepancy or there is another issue at play here?
Relative perfusion would definitely work for us, but when I run without calibration, I do not generate the region analysis outputs, so I am unsure what the best path forward is to get a reliable
relative perfusion value.
Thank you so much for taking the time to help us figure this out!
Emily,
From these images - given they derive from the same original data - I would assume that background suppression has been applied. I would expect a much larger difference in magnitude between the perfusion image
and a true calibration (equilibrium magnetisation) image.
Michael Chappell MEng DPhil SFHEA
Professor of Biomedical Imaging
Sir Peter Mansfield Imaging Centre
School of Medicine
University of Nottingham
Precision Imaging Beacon Hub
Books for biomedical engineering (physiology and imaging):
Thank you for following up with the helpful feedback! I attached the perfusion weighted image and calibration image here (the control image which was separated out from the label-control pair). Please take
a look and let me know your thoughts, I am not seeing much of a difference between the two and am not sure if this indicates background suppression was probably used and we cannot get our perfusion values in absolute units.
I agree that these values are very high. It would be good to look at the relative scale of your calibration image (which in your case is the control image) and the perfusion weighted image (the perfusion.nii.gz
output from oxford_asl would be fine for this). I would expect there to be a large difference, around two orders of magnitude difference, between the two if there is no background suppression applied.
Michael Chappell MEng DPhil SFHEA
Professor of Biomedical Imaging
Radiological Sciences, Mental Health & Clinical Neurosciences, School of Medicine
Sir Peter Mansfield Centre, School of Medicine
University of Nottingham
Texts on biomedical engineering
(physiology and imaging):
PhysiologyforEngineers.org
Texts on neuroimaging:
NeuroimagingPrimers.org
I would like to follow up on my previous question about our perfusion values which seemed very high (re-pasting these below) after using our separated control image from the ASL sequence as the calibration
input along with its TR value using this command: oxford_asl -i PREV_053_S1_ASL_real.nii.gz -c SingleSplitVol_even.nii.gz --tr=4.854 --mc --iaf=tc --wp --casl --tis 3.475 --bolus 1.45 --fslanat=PREV_053_S1_T1_sag.anat --sbrain PREV_053_S1_T1_sag_brain.nii.gz
--senscorr –pvcorr.
I would be extremely appreciative for any feedback on this, thank you so much!
Mean perfusion in gm is 1706.541063
Mean perfusion in cortical gm is 1782.053283
Mean arrival in gm is 0.001143
Mean arrival in cortical gm is 0.001067
Mean perfusion_wm in wm is 3384.354213
Mean perfusion_wm in cerebral wm is 3410.658000
Mean arrival_wm in wm is 0.343629
Mean arrival_wm in cerebral wm is 0.347908
Mean perfusion_calib in gm is 8169.734142
Mean perfusion_calib in cortical gm is 8619.137136
Mean perfusion_wm_calib in wm is 15115.901513
Mean perfusion_wm_calib in cerebral wm is 15187.194326
Thank you so much for getting back to me with the helpful walkthrough for separating the control volume and then using that for calibration. Our ASL data only contains 2 volumes (one label and one control) so I do not think taking the mean or motion correcting
applies in our case, please let me know if this is correct and if using a single control volume is still valid for calibration.
I used the following command to first separate out the control image: asl_file --data=PREV_053_S1_ASL_real.nii.gz
--ntis=1 --iaf=tc --spairs --out=SingleSplitVol, and then used the even output file (because our data is in label-control pairs) as the calibration input file to oxford asl along with the TR value using the following command: oxford_asl
-i PREV_053_S1_ASL_real.nii.gz -c SingleSplitVol_even.nii.gz --tr=4.854 --mc --iaf=tc --wp --casl --tis 3.475 --bolus 1.45 --fslanat=PREV_053_S1_T1_sag.anat --sbrain PREV_053_S1_T1_sag_brain.nii.gz --senscorr –pvcorr.
However, the perfusion values that were produced seem extremely high, so I am wondering if anything stands out to you that may be offsetting the values?
I am pasting the values below:
Mean perfusion in gm is 1706.541063
Mean perfusion in cortical gm is 1782.053283
Mean arrival in gm is 0.001143
Mean arrival in cortical gm is 0.001067
Mean perfusion_wm in wm is 3384.354213
Mean perfusion_wm in cerebral wm is 3410.658000
Mean arrival_wm in wm is 0.343629
Mean arrival_wm in cerebral wm is 0.347908
Mean perfusion_calib in gm is 8169.734142
Mean perfusion_calib in cortical gm is 8619.137136
Mean perfusion_wm_calib in wm is 15115.901513
Mean perfusion_wm_calib in cerebral wm is 15187.194326
Thank you so much for your help with this!
Given that you did not acquire your label-control data with background suppression it should be possible to use the control images in place of an M0 image in oxford_asl.
As a first attempt, I would separate out just one control image from the label-control time series and just use that as the input to the “-c <M0_image>” option. Normally M0 images are acquired
with a longer TR than the label-control data, so it is worth inputting the TR for the label-control images into oxford_asl with the “--tr=<TR value>” option.
If that looks like it is producing sensible perfusion values, then I think I would try again but this time use the mean of all of the control images. So, separate all of the control images out
from the label-control time series, motion correct them so that they are aligned, then take the mean. Then use the mean control image as the input to “-c <M0_image>” option (and still use the “--tr=<TR value>” option to let it know the control images TR value).
Let us know if you have any problems with this.
My team is currently trying to run a single PLD/TI pcASL analysis with oxford_asl; however, we did not acquire a separate M0 image and none of our structural images have the same readout parameters as the
ASL data. Do you know if our ASL control images (they are in control-label pairs) can be used by any of the BASIL tools to estimate the M0 of arterial blood? I was looking into using asl_calib to compute this value, but I did not feel confident that this was
supported. We did not use presaturation or background suppression if this is helpful.
Thank you so much for your help.
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<perfusion.nii.gz><SingleSplitVol_even.nii.gz>
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