asl_calib - calibration of ASL perfusion¶
ASL tag-control difference data can be used to quantify perfusion. However, the values obtained are not by default provided in conventional units. To get absolute CBF quantification it is also necessary to estimate the equilibrium magnetization (M0) of arterial blood.
A popular option is to correct for the equilibrium magnetization of arterial blood using a proton density (PD) weighted image (using a relatively long TR) and dividing the ASL perfusion image (the output of
basil) by the PD image voxel-by-voxel. Alternatively, the M0 value for arterial blood can be estimated indirectly from a measurement in a reference ‘tissue’, such as the CSF or white matter, either:
- LongTR: From a separate calibration image that uses the same acquisition as the ASL data, but contains no inversion (i.e. a ‘control’ image) and no background suppression. Ideally the images would be acquired with a very long TR. However, it is possible to account for shorter TR values, for example matching that the of ASL sequence, with an estimate of the T1 of the reference ‘tissue’.
- SatRecov: From the saturation recovery of the control images in the ASL data sequence, if a presaturation has been applied in the imaging region.
asl_calib performs the necessary steps to obtain the M0 of arterial blood value from such a calibration images. It can also:
- LongTR method: produce a spatial sensitivity estiamte for the coil used for aquisition, if another calibration image is supplied that was acquired using some other coil (assumed to have a flat spatial sensitivity) as a reference (e.g. the body coil).
- SatRecov method: produce an estimated T1 of tissue image for use in kinetic curve model fitting.
Using M0 and sensitivity images to calculate absolute CBF¶
asl_calib can be instructed to save the M0 value and the sensitivity image (if calcuated) for subsequent use to calculate absolute CBF. Given an estimated perfusion image, e.g. from
basil, absolute CBF in ml/100g/min can be obtained using fslmaths:
With M0 only:
fslmaths [perfusion_image] -div cat [M0_text_file] -mul 6000 [absolute_CBF_output_image]
With M0 and sensitivity image:
fslmaths [perfusion_image] -div cat [M0_text_file] -div [sensitivity_image] -mul 6000 [absolute_CBF_output_image]
For these calculations the CBF image should still be in the native resolution of the ASL data. The first option (with M0 only) will work with perfusion images that have been converted to an another resolution, e.g. standard space.
Typing the asl_calib with no options will give the basic usage information, the following is a more detailed version:
|Calibration data in Nifti file format with the individual images stacked in the time dimension.|
|Structural image used for determining reference ‘tissue’ mask (not required if reference ‘tissue’ mask is supplied, see below).|
|-t <asl to structural_transformation_matrix>|
|Transformation matrix for ASL images to structural image space, e.g. from |
|--mode <mode>||Specify what form the calibration data takes, options are: longtr, satrecov. See below for mode specific options.|
|The ‘tissue’ type to use as a reference, see below, options are: |
|TE of the calibration sequence in seconds, default is 0 s.|
|A perfusion image for calibration. This should be still at the native resolution of the ASL data.|
|File to which absolute CBF image should be saved, if input image has been supplied with |
|The estimated M0 value of arterial blood will be saved as text to a file of this name. This can then be used to convert a perfusion image into absolute values.|
|-m <CSF_mask>||Provide a ‘tissue’ reference mask, e.g. hand drawn, instead of relying upon automated mask creation. If a mask is supplied the structural image and ASL to structural transformation are no longer required.|
|A mask of the brain in (ASL native space), this will be used for sensitivity estimation (LongTR method) or T1 estimation (SatRecov method). If not supplied a brain mask will be generated automatically from the calibration data if it is needed, this option allows the same mask from other processing steps to be employed for consistency.|
|T1 (in seconds) for the reference tissue, the defaults for the different |
|T2(*) (in miliseconds) for the reference tissue, the defaults for the different |
|T2 (in miliseconds) for blood, the default is 150/50 (T2/T2*). The defaults are a general estimate based on the literature and should be used with care.|
Mode specific options
|TR of the calibration sequence in seconds, default is 3.2 s.|
|The relative gain of the ASL data to that of the calibration image, default 1. This allows for the case where the ASL data has been acquired with a higher gain than the calibration images, for example where background suppression was used allowing for a higher gain to be set for the ASL data.|
|A further image aquired using the same parameters as the main calibration file, but with a different coil to be used as a reference to calculate the sensitivity of the coil used for the main ASL data.|
|Specify where the sensitivity file can be saved, if a reference image has been supplied with |
|provide a sensitivity image (that matches the calibration image) to be used in calcuations.|
|Comma separated list of inversion times in the data (in seconds), e.g. |
|Flip angle in degrees for Look-Locker readouts, do not set if not using Look-Locker.|
|Low flip angle for Look-Lokcer readouts in which an extra set of TIs were acquired with a lower flip angle. This is used to estimate the correction for true flip angle at every voxel. It is assumed that the low flip angle data is the final phase (set of TIs) in the calibration data.|
|The number of phases (sets of TIs) at the higher flip angle.|
‘Tissue’ reference type
asl_calib will let you choose what ‘tissue’ you want to use as the reference. M0 is calculated within a mask of this ‘tissue’, as the mean over all the voxels within the mask. This option tells
asl_calib which ‘tissue’ from the automatic segmentation as well as what T1 and T2(*) values should be used.
asl_calib uses CSF as the reference because it is relatively easy to segment and a mask can be defined containing a reasonable number of voxels that do not suffer substantial partial volume effects. The automated masking is optimized to extract CSF from the ventricles and this is probably the best reference to use. However, ventricular CSF is likely to be in the region of lowest coil sensitivity for multi-channel coils, and the longer T1 value of CSF can lead to bias when the TR is comparatively short (< 5 seconds). White matter is a reasonable alternative as partial volume effects can be minimized to a good degree. Grey matter is generally not a good option for that reason.
Automatic reference ‘tissue’ mask
asl_calib attempts to automatically generate the reference ‘tissue’ mask from the structural image, unless you supply your own custom mask with the
-m option. It does this using
FAST, thus the normal caveats for segmentation when using that program apply, for example the structural image must already have been brain extracted.
Having a really perfect mask is not vital, since the M0 calcuation is performed over all the voxels within the mask. However, the mask needs to at least be sensible, hence it is a very good idea to check the mask created at the end. If
asl_calib detects that after segmentation, transformation into ASL native space and thresholding, that there are no voxels in the mask it will halt and tell you that the automated method has failed.