# ASL processing pipeline details¶

*ASLPrep* adapts its pipeline depending on what data and metadata are
available and are used as inputs. It requires the input data to be
BIDS-valid and include necessary ASL parameters.

## Structural Preprocessing¶

The anatomical sub-workflow is from sMRIPrep. It first constructs an average image by conforming all found T1w images to a common voxel size, and, in the case of multiple images, averages them into a single reference template.

See also *sMRIPrep*’s
`init_anat_preproc_wf()`

.

### Brain extraction, brain tissue segmentation and spatial normalization¶

Next, the T1w reference is skull-stripped using a Nipype implementation of
the `antsBrainExtraction.sh`

tool (ANTs), which is an atlas-based
brain extraction workflow:

(Source code, png, svg, pdf)

An example of brain extraction is shown below:

Once the brain mask is computed, FSL `fast`

is utilized for brain tissue segmentation.

Finally, spatial normalization to standard spaces is performed using ANTs’ `antsRegistration`

in a multiscale, mutual-information based, nonlinear registration scheme.
See Standard and nonstandard spaces for more information on how standard and nonstandard spaces can
be set to resample the preprocessed data onto the final output spaces.

## ASL preprocessing¶

Preprocessing of ASL files is split into multiple sub-workflows described below.

### ASL reference image estimation¶

`init_asl_reference_wf()`

(Source code, png, svg, pdf)

This workflow estimates a reference image for an
ASL series.
The reference image is then used to calculate a brain mask for the
ASL signal using *NiWorkflow’s*
`init_enhance_and_skullstrip_asl_wf()`

.
Subsequently, the reference image is fed to the head-motion estimation
workflow and the registration workflow to map the
ASL series onto the T1w image of the same subject.

### Head-motion estimation¶

(Source code, png, svg, pdf)

Using the previously estimated reference scan,
FSL `mcflirt`

or AFNI `3dvolreg`

is used to estimate head-motion.
As a result, one rigid-body transform with respect to
the reference image is written for each ASL
time-step.
Additionally, a list of 6-parameters (three rotations and
three translations) per time-step is written and fed to the
confounds workflow,
for a more accurate estimation of head-motion.

### Slice time correction¶

(Source code, png, svg, pdf)

If the `SliceTiming`

field is available within the input dataset metadata,
this workflow performs slice time correction prior to other signal resampling
processes.
Slice time correction is performed using AFNI `3dTShift`

.
All slices are realigned in time to the middle of each TR.

Slice time correction can be disabled with the `--ignore slicetiming`

command line argument.

### Confounds estimation¶

(Source code, png, svg, pdf)

Calculated confounds include frame-wise displacement, 6 motion parameters, and DVARS.

### Susceptibility Distortion Correction (SDC)¶

One of the major problems that affects EPI data is the spatial distortion caused by the inhomogeneity of the field inside the scanner. Please refer to Susceptibility Distortion Correction (SDC) for details on the available workflows.

See also *SDCFlows*’ `init_sdc_estimate_wf()`

### Preprocessed ASL in native space¶

`init_asl_preproc_trans_wf()`

(Source code, png, svg, pdf)

A new *preproc* ASL series is generated
from either the slice-timing corrected data or the original data (if
STC was not applied) in the
original space.
All volumes in the ASL series are
resampled in their native space by concatenating the mappings found in previous
correction workflows (HMC and
SDC, if executed)
for a one-shot interpolation process.
Interpolation uses a Lanczos kernel.

## CBF Computation in native space¶

`init_cbf_compt_wf()`

(Source code, png, svg, pdf)

ASL data consist of multiple pairs of labeled and control images. *ASLPrep* first checks for
the reference ASL volume(s) (M0,``sub-task_xxxx-acq-YYY_m0scan.nii.gz``). In the absence of M0 images,
the average of control images is used as the reference image.

After preprocessing, the pairs of labeled and control images are subtracted:

The CBF computation of either single or multiple PLD (post labelling delay) is done using a relatively simple model. For P/CASL (pseudo continuous ASL), CBF is calculated by using a general kinetic model [Buxton1998]:

PASL (Pulsed ASL) is also computed by the QUIPSS model [Wong1998]:

\(\tau,\quad \lambda,\quad and\quad \alpha\quad\) are label duration, brain-blood partition coefficient, and labeling efficiency, respectively. In the absence of any of these parameters, standard values are used based on the scan type and scanning parameters.

The computed CBF time series is shown in carpet plot below.

Mean CBF is computed from the average of CBF timeseries.

For multi-PLDs (Post Labeling Delay) ASL data, the CBF is first computed for each PLD and the weighted average CBF is computed over all PLDs at time = t,([Daiw2012]).

ASLPrep includes option of CBF denoising by SCORE and SCRUB. Structural Correlation based Outlier Rejection (SCORE) ([Dolui2017]) detects and discards extreme outliers in the CBF volume(s) from the CBF time series. SCORE first discards CBF volumes whose CBF within grey matter (GM) means are 2.5 standard deviations away from the median of the CBF within GM. Next, it iteratively removes volumes that are most structurally correlated to the intermediate mean CBF map unless the variance within each tissue type starts increasing (which implies an effect of white noise removal as opposed to outlier rejection).

The mean CBF after denoising by SCORE is plotted below

After discarding extreme outlier CBF volume(s) (if present) by SCORE, SCRUB (Structural Correlation with RobUst Bayesian) uses robust Bayesian estimation of CBF using iterative reweighted least square method [Dolui2016] to denoise CBF. The SCRUB algorithm is described below:

\(CBF_{t},\quad \mu,\quad \theta,\quad and\quad p\) equal CBF time series (after any extreme outliers are discarded by SCORE), mean CBF, ratio of temporal variance at each voxel to overall variance of all voxels, and probability tissue maps, respectively. Other variables include \(\lambda\quad and\quad \rho\) that represent the weighting parameter and Tukey’s bisquare function, respectively.

An example of CBF denoised by SCRUB is shown below.

*ASLPrep* also includes option of CBF computation by Bayesian Inference for Arterial Spin Labeling
(BASIL). BASIL also implements a simple kinetic model as
described above, but using Bayesian Inference principles ([Chappell2009]).
BASIL is mostly suitable for multi-PLD. It includes bolus arrival time estimation
with spatial regularization [Groves2009] and the correction of partial volume effects [Chappell2011].

The sample of BASIL CBF with spatial regularization is shown below:

The CBF map shown below is the result of partial volume corrected CBF computed by BASIL.

## Quality control measures¶

`init_cbfqc_compt_wf()`

(Source code, png, svg, pdf)

Quality control (QC) measures such as FD (framewise displacement), coregistration, normalization index, and quality evaluation index (QEI) are included for all CBF maps. The QEI [Dolui2017b] evaluates the quality of the computed CBF maps considering three factors: structural similarity, spatial variability, and percentage of voxels in GM with negative CBF.

## ASL and CBF to T1w registration¶

(Source code, png, svg, pdf)

*ASLPrep* uses the `FSL BBR`

routine to calculate the alignment between each run’s ASL reference image
and the reconstructed subject using the gray/white matter boundary

FSL `flirt`

is run with the BBR cost function, using the
`fast`

segmentation to establish the gray/white matter boundary. After BBR is run,
the resulting affine transform will be compared to the initial transform found by `flirt`

.
Excessive deviation will result in rejection of the BBR refinement and acceptance
of the original affine registration. The computed CBF
is registered to T1w using the transformation from ASL-T1w registration.

### Resampling ASL and CBF runs onto standard spaces¶

(Source code, png, svg, pdf)

This sub-workflow concatenates the transforms calculated upstream (see
Head-motion estimation, Susceptibility Distortion Correction (SDC)) if
fieldmaps are available, and an anatomical-to-standard
transform from Structural Preprocessing to map the
ASL and CBF images to the standard spaces is given by the `--output-spaces`

argument
(see Standard and nonstandard spaces).
It also maps the T1w-based mask to each of those standard spaces.

Transforms are concatenated and applied all at once, with one interpolation (Lanczos) step, so as little information is lost as possible.

References

[Buxton1998] | Buxton R.B., Frank L.R., Wong E.C., Siewert B, Warach S, Edelman R.R. A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magn Reson Med. 1998;40(3):383-396. doi:10.1002/mrm.1910400308. |

[Wong1998] | Wong E.C., Buxton R.B., Frank L.R. Quantitative imaging of perfusion using a single subtraction (QUIPSS and QUIPSS II). Magn Reson Med. 1998;39(5):702-708. doi:10.1002/mrm.1910390506 |

[Dolui2017] | Dolui S, Wang Z, Shinohara R.T., Wolk D.A., Detre J.A.; Alzheimer’s Disease Neuroimaging Initiative. Structural Correlation-based Outlier Rejection (SCORE) algorithm for arterial spin labeling time series. J Magn Reson Imaging. 2017;45(6):1786-1797. doi:10.1002/jmri.25436 |

[Dolui2016] | Dolui S., Wolk D.A., Detre J.A. SCRUB: a structural correlation and empirical robust bayesian method for ASL data. Proceedings of the International Society of Magnetic Resonance in Medicine; Singapore; 2016 |

[Chappell2009] | Chappell M.A., Groves R.B, Whitcher B., and Woolrich M. W., “Variational Bayesian Inference for a Nonlinear Forward Model,” in IEEE Transactions on Signal Processing, vol. 57, no. 1, pp. 223-236, Jan. 2009, doi:10.1109/TSP.2008.2005752. |

[Groves2009] | Groves A.R., Chappell M.A., Woolrich M.W., Combined spatial and non-spatial prior for inference on MRI time-series. Neuroimage. 2009;45(3):795-809. doi:10.1016/j.neuroimage.2008.12.027. |

[Chappell2011] | Chappell M.A., Groves A.R., MacIntosh B.J., Donahue M.J., Jezzard P., Woolrich M.W., Partial volume correction of multiple inversion time arterial spin labeling MRI data. Magn Reson Med. 2011;65(4):1173-1183. doi:10.1002/mrm.22641 |

[Dolui2017b] | Dolui S., Wolf R. & Nabavizadeh S., David W., Detre, J. (2017). Automated Quality Evaluation Index for 2D ASL CBF Maps. ISMR 2017 |

[Daiw2012] | Dai W., Robson P.M., Shankaranarayanan A., Alsop D.C. Reduced resolution transit delay prescan for quantitative continuous arterial spin labeling perfusion imaging. Magn Reson Med. 2012;67(5):1252-1265. doi:10.1002/mrm.23103 |