BIDS JSON question for ASLprep

Software Support
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1

Hi there,

I have a question about processing some data with ASL prep and hoping someone could provide some help!

I have images from a PCASL sequence ran on a Siemens Prisma scanner. To match BIDS specifications I had to add these fields based on what I could determine and calling to Siemens app support.

JSON sidecar

"RepetitionTimePreparation": 4,
"ArterialSpinLabelingType": "PCASL",
"PostLabelingDelay": 1.8,
"BackgroundSuppression": true,
"M0Type": "Absent",
"TotalAcquiredPairs": 11,
"LabelingDuration": 2.2

I am setting Background suppression based on this field in my JSON-
“ScanOptions”: “FS”

M0

And to my knowledge I do not have an M0 calibration image. I initially thought my 1st volume was the M0 image based on it being much brighter than the rest of the images, but Siemens tells me thats due to fat suppression (in addition to needing an even number of label/control images, in my case 22 total/ 11 pairs).

The error

The issue is that when I run ASLprep I get this error:

Traceback: Traceback (most recent call last): File "/opt/conda/envs/aslprep/lib/python3.11/site-packages/nipype/interfaces/base/core.py", line 397, in run runtime = self._run_interface(runtime) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/envs/aslprep/lib/python3.11/site-packages/aslprep/interfaces/cbf.py", line 199, in _run_interface raise ValueError( ValueError: Background-suppressed control volumes cannot be used for calibration.

Am I misunderstanding what “background suppression” refers to? Am I setting my JSON up incorrectly?

Any help would be much appreciated, thank you!

Best,
Caleb Haynes

2

That is based on the BASIL documentation (emphasis mine):

Now we need to know some more information about our data.

  • Background suppression was ON.

This should have improved our perfusion contrast by removing as much static tissue signal as possible. However, we with this cannot use the control images to estimate equilibrium magnetization values. Instead there is a separate dataset, called calib, which is a series of control images with BGS OFF. We also have calib_body data in which control images were acquired with the same parameters but using the body coil. We will assume that the latter has a relatively flat sensitivity and use this to correct for sensitivity variation in the coil we used for the main acquisition. At this stage we also supply a structural image - this will be used to segment out the ventricles which will form the basis of our magnetization estimation, plus the perfusion image will also be transformed into the same resolution as the structural image via registration:

Background suppression is not the same a fat suppression, so I wouldn’t set BackgroundSuppression to True on the basis of your ScanOptions field, but I don’t know what DICOM fields might indicate if background suppression is enabled or not.

3

Thank you for taking the time to respond. I set this based on the conversation with the Siemens rep but if background suppression != fat supression I will rerun with that change in mind.