ASL Analysis Tab¶
The analysis tab contains options for the model fitting part of the pipeline.
Model fitting options¶
A custom ROI in which to perform the model fitting can be provided - normally this is generated by brain extraction of the structural data (or the ASL data if no structural data is given).
This option will smooth the output data using an adaptive method which depends on the degree of variation in the data. If there is sufficient information in the data to justify fine grained spatial detail, it will be preserved, however if the data is not sufficient this will be smoothed.
The effect is similar to what you would get by applying a smoothing algorithm to the output, however in this case the degree of smoothing is determined by the the variation in the data itself.
An example perfusion map without spatial regularization might look like this:
With spatial regularization turned on, the same data set produced the following perfusion map:
Fix label duration¶
The label duration (bolus duration) can be allowed some variation to better fit the data. If this option is selected this will not occur. Label duration is fixed by default.
Fix arterial transit time¶
Similarly to the above, this controls whether the arterial transit time (also known as bolus arrival time) is allowed to vary to fit the data. However, in contrast to the label duration, this is allowed to vary by default with multi-PLD data.
Arterial transit time cannot be accurately estimated with single-delay data.
T1 value uncertainty¶
This is analagous to the above options but controls whether the T1 value is allowed to vary. By default it is kept constant.
Macro vascular component¶
Some of the signal in the ASL data will come from labelled blood in arteries as opposed to perfused tissue. This may be a significant contribution in voxels containing a major artery. By adding a macro vascular component this signal can be estimated and separated from the tissue perfusion contribution during the fitting process.
Partial volume correction¶
If enabled, this will use the GM/WM segmentation to perform an additional modelling step in which the GM and WM contributes will be modelled separately and based on the GM/WM partial volume within each voxel (which will also be modelled as part of the fitting process).
Partial volume correction adds considerably to the pipeline run time!
The values given for arterial transit time, T1 and T1b are from the literature, but can be customized if required.
White paper mode¶
‘White paper mode’ selects defaults and analysis methods to match the recommendations in Alsop et al (2014) . Specifically this selects:
- Voxelwise calibration
- Arterial transit time of zero (fixed)
- T1 and T1b of 1.65s
- Fixed label duration
- No macrovascular component
|||Alsop, D. C., Detre, J. A., Golay, X. , Günther, M. , Hendrikse, J. , Hernandez‐Garcia, L. , Lu, H. , MacIntosh, B. J., Parkes, L. M., Smits, M. , Osch, M. J., Wang, D. J., Wong, E. C. and Zaharchuk, G. (2015), Recommended implementation of arterial spin‐labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn. Reson. Med., 73: 102-116. doi:10.1002/mrm.25197|