ASAP_2.0 (Automatic Software for ASL Processing) USER S MANUAL

Transcription

1 ASAP_2.0 (Automatic Software for ASL Processing) USER S MANUAL ASAP was developed as part of the COST Action "Arterial Spin Labelling Initiative in Dementia (AID)" by: Department of Neuroimaging, Institute of Psychiatry at King's College London (UK). Laboratorio de Análisis de Imagen Médica y Biometría (LAIMBIO), Universidad Rey Juan Carlos (Spain). Referencing: If you employ ASAP in your research please reference the paper below Mato Abad V, García-Polo P, O'Daly O, Hernández-Tamames JA, Zelaya F (2016). ASAP (Automatic Software for ASL Processing): A toolbox for processing Arterial Spin Labeling images. Magn Reson Imaging, 34(3): ASAP_2.0 is a beta version. Please assist us by reporting errors or questions to virginia.mato@urjc.es June 2017

2 1. Introduction ASAP processing toolbox is written in MATLAB and includes all the steps needed for preparing the resting-state ASL (Arterial Spin Labelling) maps for a statistical analysis. This toolbox includes different analysis strategies depending on the ASL image readout and the type of structural scans available from each subject (T2-weighted and/or T1- weighted high-resolution scans). The Graphical User Interface (GUI) can automatically process several ASL datasets by accessing both SPM-12 and FSL software, which are two of the most widely available image processing platforms for structural and functional MRI. These data can be setup manually through the GUI fields (which have been programmed to mimic the SPM file handling menus) or by advanced options that include a batch mode. The GUI also provides options to visualize the results and for different file format. There are different pipelines for the ASAP processing steps, depending on: (1) The reference volume used for the co-registration with the structural high-resolution scans: The ASL image The Proton Density (PD) acquisition. (2) The mode to do the co-registration: Upsampling the ASL to high-resolution. Downsampling the structural scan to the ASL resolution. Basic steps: 1. Rough skull-stripping of the resting state ASL map using the FSL Brain Extraction Tool (Bet) for noisy ASL maps using a conservative threshold. 2. Co-registration of [ASL scan <-> skull-stripped structural scan] (upsampling or downsampling mode) using SPM- 8 by to ways: 2.1. Using the ASL as source image Using the PD as source image and moving the ASL during co-register. 3. Skull-stripping of the structural scan by two different options: the FSL bet tool (applied to the T2 or T1 weighted scan) OR with a brain mask created from SPM segmentation of the T1-weighted high resolution scan. 4. Partial Volume Correction (PVC) of the ASL maps. In the current version, two different methods are available for PVC: (1) The linear regression method described by Asllani 1 and (2) The method based on a previous PET 1 Asllani I. et al.; Regression algorithm correcting for partial volume effects in arterial spin labeling MRI. Magnetic Resonance in Medicine 60: (2008). 2

3 study 2 (hereafter PET s correction) that assumes perfusion of WM is globally 40% of that of GM for correction resting cerebral blood flow. This method has been later applied in several ASL studies Skull-stripping of the co-registered and partial volume corrected ASL maps using the structural brain mask obtained on (4) above. 6. MNI Normalization of the structural scan and ASL maps obtained in (6) to the standard MNI space. 7. Smoothing of the final, spatially normalised CBF maps by a smoothing kernel chosen by the user. The software also offers additional facilities for rapid extraction of CBF values from anatomically or functionally defined Regions of Interest (ROIs). In batch mode, the software can simultaneously extract mean, median and maximum values from several ROIs in several CBF maps. The output is generated in text files that can easily be incorporated into statistical analysis packages such as SPSS, etc. IMPORTANT: Check Section 5 for important recommendations for a proper use of the different options available in the toolbox. 2 Leenders K.L. et al; Cerebral blood flow, blood volume and oxygen utilization. Normal values and effect of age. Brain : a journal of neurology 113:27-47 (1990). 3 Chen Y. et al.; Voxel-level comparison of arterial spin-labeled perfusion MRI and FDG-PET in Alzheimer disease. Neurology 77(22): (2011). Du A.T. et al.; Hypoperfusion in frontotemporal dementia and Alzheimer disease by arterial spin labeling MRI. Neurology 67(7): (2006). Johnson N.A., et al. Pattern of cerebral hypoperfusion in Alzheimer disease and mild cognitive impairment measured with arterial spinlabeling MR imaging: initial experience. Radiology 234(3): (2005). 3

4 2. Requirements ASAP runs on Unix systems (Linux, Mac OS) with MATLAB software installed (including the Tools for NIfTI and ANALYZE image package 4 ). ASAP access both FSL software and Statistical Parametric Mapping (SPM-12) software, so the toolbox requires you have: SPM-12 installed and added to your MATLAB path. FSL installed and added at your path (FSLDIR environment variable)* 3. Installation 1. Download the package ASAP_2.0.zip 2. Unzip the package: unzip ASAP_2.0.zip 3. Open MATLAB and add the ASAP folder to the MATLAB path ( File->Set path menu or addpath function). 4. Run ASL_GUI in the MATLAB console. * If you have problems with fsl path, try to add the FSLDIR environment variable in your MATLAB console: setenv('fsldir','fslabsolutepath ) 4 Tools for NIfTI and ANALYZE image, by Jimmy Shen: 4

5 4. User Guide Figure shows the ASAP processing GUI. Next are described all the GUI options and parameters. Input Files. For each subject: Structural: High-resolution structural scan. File format admitted: [.dcm.hdr.hdr.gz.img.img.gz.nii.nii.gz]. Before select files, make sure the checkbox NIFTI/ANALYZE or DICOM is in the right file format. For DICOM files, you must select all the series DICOM structural files and, after that, the output directory for the converted nifti files. In this directory, the nifti file will be saved under /<Patient_ID>/<Series_description>. Default=NIFTI/ANALYZE. T1-weighted: Check when structural scan is T1-weighted (default). T2-weighted: Check when structural scan is T2-weighted. ASL: ASL scan: CBF map or difference image (control tag image). File format admitted: [.dcm.hdr.hdr.gz.img.img.gz.nii.nii.gz]. Before select files, make sure the checkbox 5

6 NIFTI/ANALYZE or DICOM is in the right file format. For DICOM files, you must select all the series DICOM ASL files and, after that, the output directory for the converted nifti files. In this directory, the nifti file will be saved under /<Patient_ID>/<Series_description>. If the ASL series include the PD DICOM files, the PD will be also converted to nifti format and the Use PD for Coregistration will be auto selected. Default=NIFTI/ANALYZE. If you select Use PD for coregistration, the Proton Density scan is also required. File format admitted for PD: [.dcm.hdr.hdr.gz.img.img.gz.nii.nii.gz]. Before select files, make sure the checkbox NIFTI/ANALYZE or DICOM is in the right file format. For DICOM files, you must select all the series DICOM PD files and, after that, the output directory for the converted nifti files. In this directory, the nifti file will be saved under /<Patient_ID>/<Series_description>. Default=NIFTI/ ANALYZE. Check Use PD for Co-registration for using the PD scan for co-registration with the structural scan instead of the ASL volume. Output Directories. For each subject: Structural: Directory for structural output files. By default, it is set as the same as the input structural file. ASL: Directory for ASL output files. By default, it is set as the same as the input ASL file. PD: Directory for PD output files. By default, it is set as the same as the input PD file. Options. Processing parameters: Coregistration method: Upsampling: Check for upsampling the ASL to high-resolution (default) by interpolation. Downsampling: Check for downsampling the structural scan to low-resolution. ASL rough skull strip: Check for ASL rough skull-stripping using the FSL Brain Extraction Tool (Bet). Useful for noisy ASL maps. Default = 1. -f [0-1]: Fractional intensity threshold. Default = 0.2. Structural scan skull strip: Check for skull-stripping of the structural scan. Default = 1 SPM segmentation mask: Check for skull-stripping using the brain mask created with SPM segmentation output volumes. Default = 1 for T1-weighted scans and 0 for T2-weighted scans Use CSF: Check for includes the cerebrospinal fluid (CSF) map in the brain mask. Default = 1 6

7 FSL bet: Check for skull-stripping using FSL bet tool. Default = 0 for T1-weighted scans and 1 for T2-weighted scans -f [0-1]: Fractional intensity threshold. Default = 0.4 for T1-weighted scans and 0.35 for T2-weighted scans -r: Approximate head radius (mm not voxels). Default = 75. Useful for neck cleanup Partial Volume Correction: Check for apply partial volume correction to the ASL maps. The PVC of the ASL maps is only available when working in the low-resolution ASL space. Two different methods are now available: Asllani: A linear regression method described in reference [1] (see page 3). This algorithm estimates the ASL maps for grey matter (GM) and white matter (WM) independently. Kernel size: The regression-kernel size. Options are: [3x3x1, 5x5x1, 7x7x1, 9x9x1, 11x11x1] PET s correction: This method assumes that all contributions to perfusion are from brain tissue and that CSF has no contribution. ASL intensities are corrected according to the equation: I corr=i uncorr/(p GM+0.4*P WM), where I corr and I uncorr are the corrected and uncorrected intensities, the 0.4 factor is the global ratio between WM and GM and PGM and PWM are the probabilities of GM and WM, respectively. Normalization: Check for normalization to MNI space the output files. The resolution of normalized files is 2x2x2 with 91x109x91 dimensions. Smooth: Check for smooth final ASL maps. Default = 0 FWHM value. Kernel width in mm. Default = [6, 6, 6]. Output files: Format of output files [.hdr/.img.nii]. Default =.nii Quick checkreg: Check for display a quick checkreg of final ASL map in the web browser. Open SPM progress window: Opens the SPM interactive window to following the progress Actions: Next Sub.: Click to save current subject s dataset and start with the parameters for a new subject. Run: Click to start processing. Cancel: Click to reset and cancel all the inserted data (for all subjects). Quit: Exit. Advanced options: Load batch files: Load input files from text files. First, select the text file containing the input structural scans and then, the text file containing input ASL maps. If Use PD for 7

8 coregistration is checked, select in third option the text file with the input PD scans. For NIFTI/ANALYZE format, the text file will contain the absolute path of each file in a separate line. For DICOM format, the text file will contain the absolute path of the directory that contains all the DICOM files for each subject in a separate line. Important: Each text file must contain each filename or directory path in a separate line. Make sure that the subject file order is the same in all (two or three) files and that there is not blank lines at the end of files. For more than one ASL acquisition per session, repeat the structural file path as many times as ASL scans in the structural text file. Using this option, it is necessary to fill the Options panel parameters (the same options will apply for all subjects). After that, click the Run button to start the process. Output files will be saved in the same directory as input files. ROI Statistics: Open ROI GUI for statistics in ASL maps. Process For each subject: 1. Reorient to the AC-PC plane (Anterior Commissure - Posterior Commissure) the structural, PD* and ASL files. Output filenames: structural_reo, PD_reo* and ASL_reo. 2. If [ASL rough skull strip] = 1, run bet command on ASL image for a rough skull-stripping, using the input fractional threshold. This step is useful for noisy ASLs maps in upsampling mode for better co-registration with the structural scan. 3. If [Structural scan skull strip] = 1: 3.1. If [SPM segmentation mask] = 1, run tissue segmentation task of SPM-12 software on structural scan. Then, create the skull-stripped brain volume and mask with segmentation task outputs: grey matter (GM, c1) and white matter (WM, c2) maps. If [Use CSF] = 1, create the CSF (c3) map and include it in the skull-stripped brain volume and mask. Output filenames: c1structural_reo, c2structural_reo, c3structural_reo***, y_structural_reo, structural_reo_ stripped and structural_reo_stripped_mask If [FSL bet] = 1, run bet command on structural scan, using the input fractional threshold - f, the input head radius r and the m option to generate the brain mask. Output filenames: structural_reo_stripped and structural_reo_stripped_mask. 4. For co-registration: 4.1. Upsampling mode: If [Use PD for coregistration] = 1, Co-register the PD map to the skull-stripped 8

9 structural volume using the SPM-12 coreg function and moving the ASL map in the process. Output filenames: rpd_reo** and rasl_reo** If [Use PD for coregistration] = 0, Co-register the ASL map to the skull-stripped structural volume using the SPM-12 coreg function. Output filenames: rasl_reo** Downsampling mode: If [Use PD for coregistration] = 1, Co-register the skull-stripped structural volume (step 3) to the PD map using the SPM-12 coreg function and moving the ASL map in the process. Output filenames: rc1structural_reo**, rc2structural_reo**, rc3structural_reo***, rstructural_reo_stripped** and rstructural_reo_stripped_mask** If [Use PD for coregistration] = 0, Co-register the skull-stripped structural volume (step 3) to the ASL map using the SPM-12 coreg function. Output filenames: rc1structural_reo**, rc2structural_reo**, rc3structural_reo***, rstructural_reo _stripped** and rstructural_reo_stripped_mask**. 5. If [Partial volume correction] = 1, check is the structural scan is already segmented in GM, WM and CSF. If not, run tissue segmentation task of SPM-12 (see step 3.1) If Asllani method is selected run the linear regression method described in reference [1] using the selected kernel size. Four different files will be created: partial GM ASL, partial WM ASL and their respective masks: partial_gm_asl*(gm_map>0.05) and partial_wm_asl* (WM_map>0.05). Output filenames: ASL_reo_CBF_asllani_gm, ASL_reo_CBF_asllani _gm_mask, ASL_reo_CBF_asllani_wm and ASL_reo_CBF_asllani_wm_mask If PET s correction is selected, run the method described above. In this case, only the partial GM ASL file is estimated. Output filenames: ASL_reo_CBF_pet_gm. 6. Multiply the co-registered ASL file map by the skull-stripped mask (step 3) using the SPM-12 imcalc function. Output filenames: ASL_reo_stripped. 7. If [Normalization] = 1, normalise to MNI the ASL co-registered and skull-stripped map (step 6), the PD* and the structural output files using the SPM-12 normalise function. The resolution of normalized files is 2x2x2 with 91x109x91 dimensions. Output filenames: wasl_reo_stripped, wpd_reo*, wc1structural_reo, wc2structural_reo, wc3structural_reo***, wstructural_reo_stripped, wstructural_reo_stripped_mask and files created in step5 (if selected) with prefix w. 8. If [smooth] = 1, smooth the final output ASL map (step 7) using the SPM-12 smooth function and input FWHM value. Output filenames: swasl_reo_stripped and files created in step 5 (if selected) with prefix s. 9

10 9. If [quick checkreg] = 1, run FSL slicesdir command for display the output ASL map (step 7) in the web browser. * Only if [Use PD for coregistration] = 1 ** Depending on the co-registration mode, the r prefix will be in the structural (down-sampled) or ASL (upsampled) files for the subsequent steps *** Optional 10

11 ROI GUI ROI GUI offers extraction of CBF values from anatomically or functionally defined Regions of Interest. Options Save results in text file: Save the statistical results in a text file. Default = 1. Select files: Click to select first the ROI input files and, then, the CBF source files. Load list files: Click to read the input files from text files. First, select the text file with the absolute path of ROI files (.img/.nii) and then, the text file with the absolute path of source images. These files must contain each filename in a separate line. Run: Click to start processing. Quit: Exit Process For each ROI: 1. Calculates mean, median and max values for each source file. 2. Save the statistical results in a.mat file (filename: 'ROIresults_mmddyy_HHMM.mat). This file is saved in the current directory. The.mat file has 3 matrixes: ROI_mean, ROI_median and ROI_max with the ROI analysis results. Columns contain the results of each input ROI and rows contain the results of each source file. 3. If [save results in text file] = 1, the statistical results for each ROI are saved in individually files. This file is saved in the same directory that the ROI file (filename: <ROIfilename>_ROIstatistics.txt). Files will not be overwritten; new statistics over the same ROI will be appended at the end of the text file. 11

12 5. Recommendations 1. For T2-weighted structural scan is recommended the FSL options, due to the tissue segmentation in GM, WM and CSF does not usually work well with T2-weighted contrast images. 2. For T1-weighted structural scan is recommended the SPM-12 options. 3. The PVC of the ASL maps is only available when working in the low-resolution ASL space. The recommended kernel size for Asllani method for PVC is [5x5x1]. 4. Also, for PVC is highly recommended use the T1-weighted as structural scan, because high quality GM and WM maps are needed. 5. Option ASL rough skull strip is only recommended for noisy ASL maps, for better co-registration with the structural scan. It is also suggested check the skull-stripped ASL map in order to not deleting brain tissue. 6. The toolbox helps to repeating analysis by avoiding some processing steps. Useful, for example, for applying different partial volume corrections on the same input data: If input file contains the suffix _reo the toolbox accepts that the file is already reoriented and skips this step. Checking for GM, WM and CSF maps (c1*, c2* and c3* files) in the same directory that the structural scan. If they exist, the toolbox does not apply the SPM-12 segmentation step, using these files instead. Checking for structural skull-stripped and brain mask files (*_stripped and *_stripped_mask files) in the same directory that the structural scan. If they exist, the toolbox does not apply the skull strip step and uses these files instead. Checking for co-registered structural files (only in downsamplig mode). If they exist, the toolbox does not apply the co-registration of structural scan to the ASL and uses these files instead. 12

13 6. Example An example of the ASAP results is shown. The CBF map and the T1-weihgted 3D structural scan compose the dataset: Structural input data: T1file (1mm, 256x256x196) ASL input data: CBFfile

14 Selected options for analysis: Upsampling mode Rough skull-strip for CBF map T1-weighted tissue segmentation T1-weighted skull-strip using the GM+WM+CSF maps MNI normalization Next page shows the results for: Normalized and skull-stripped CBF map Normalized and skull-stripped T1-weighted scan (SPM- 12) Normalized tissue maps: GM and WM 14

15 Normalized and skull-stripped CBF: wrcbffile_reo_stripped T1 normalized and skull-stripped: wt1file_reo_stripped Normalized GM map: wc1t1file_reo Normalized WM map: wc2t1file_reo 15