Overview

ASLtbx and the sample data provided in this website are free for academic use. The current version of ASLtbx is based on Matlab 5.3 and above versions (MathWorks Inc, Natick MA) and SPM 5/8/12 (The Wellcome Trust Centre for Neuroimaging at University College London). It's a collection of batch scripts, implementing a pipeline for processing ASL MRI data for resting or functional studies. Some details about the ASL data processing pipeline can be found in the following reference, which should also be the appropriate citation for this toolbox:

1. Ze Wang, Geoffrey Aguirre, Hengyi Rao, JiongJiong Wang, Anna R. Childress, John A. Detre, Empirical ASL data analysis using an ASL data processing toolbox: ASLtbx, Magnetic Resonance Imaging, 2008, 26(2):261-9.

2. Ze Wang, Improving cerebral blood flow quantification for arterial spin labeled perfusion MRI by removing residual motion artifacts and global signal fluctuations, Magnetic Resonance Imaging, 2012 (in press). http://dx.doi.org/10.1016/j.mri.2012.05.004.

The current version has been tested with SPM5/8/12, and we are implementing more data denoising procedures, which have been partly published in the following papers.

William T. Hu*, Ze Wang*, Virginia M.-Y. Lee, John Q. Trojanowski, John Detre, Murray Grossman, Distinct Cerebral Perfusion Patterns in FTLD and AD, Neurology, 2010 Sep 7;75(10):881-8. (contributed equally).

Ze Wang, Sandhitsu R. Das, Sharon X. Xie, Steven E Arnold, John A Detre, David A. Wolk, For ADNI, Arterial Spin Labeled MRI in Prodromal Alzheimer's Disease: A Multi-Site Study, NeuroImage: clinical, 2013, 2, 630-636.

Ze Wang, Support Vector Machine Learning-based Cerebral Blood Flow Quantification for Arterial Spin Labeling MRI, Human Brain Mapping, 2014, 35(7):2869-75.

Cerebral Blood Flow calculation code

This program is independent of SPM except for two SPM functions for reading and writing images. We are now converting the CBF quantification code into C++ code so that it can be incorporated into other neuroimaging data analysis packages too.

The current version is written in Matlab m-scripts (asl_perf_subtract.m), which can be used in batch mode or interactively through the GUI. Once ready, online CBF calculation code working in Siemens platform will be available following the same procedures as those for getting our ASL imaging sequence. For a simple trial, you can just type asl_perf_subtract in Matlab command window and follow the steps prompt in a popup window. Documentations about the quantification procedure can be found by typing "help asl_perf_subtract" in Matlab command window or checking the code directly.

Note: this code will output the whole brain mean CBF value. When that value is abnormally low, like 12, you may need to check the mean CBF map to see whether there are large areas with extremely negative CBF values. The most vulnerable regions might be found in the inferior slices and superior slices and regions close to the ventricles.

Processing ASL data in batch mode

Please refer to the manual and the readme file included in the zip file for an overview of the whole processing steps, especially for setting the environment variables, parameters, and options for processing a whole dataset. Briefly, you need to follow the same data organization rule for each subject, and modify the setting file: par.m. Each processing step like motion correction, coregisteration, smoothing, CBF calculation, normalization, GLM, group analysis, etc, is coded in a separate file. The processing pipeline is in "batch_run.m". The setting file can be tested by typing "par" in Matlab and check the field by typing "PAR.subjects{:}" etc. Once the setting file is ok, you can test several steps in batch_run.m.

Some people may want to use the pure boxcar function as the reference in GLM. In the toolbox, this can be done by turning on line 73 "SPM.xBF.name='Fourier set' and commenting out line 74 "SPM.xBF.name='hrf'".

Running GLM using SPM user interface

It may be helpful to perform ASL data analysis step by step using SPM interface. For SPM<8, before running GLM on perfusion difference images or CBF images using SPM, you should change the default relative threshold that is used to remove background voxels. Just type "spm_defaults" and "defaults.mask.thresh=0.01" (or set it to -inf) after you load spm GUI. Then you can go ahead to set up the design matrix. For SPM8, you need to load SPM.mat after setting up the design matrix and change the threshold in the mat file by typing "

.This problem has been solved in the batch scripts.

Download

Please register before downloading ASLtbx, ASL toolbox Manual and two sample datasets. We are providing a CASL sample dataset and a PASL sample dataset. But please beware of that by providing sample data, we are not intending to provide any golden standard for either CASL or PASL mean CBF maps.

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