Replace BOLD and EPI with ASL in code

aslprep/cli/parser.py

@@ -319,7 +319,7 @@ def _bids_filter(value, parser):
         choices=['bbr', 'no-bbr', 'syn-sdc', 'fmap-jacobian', 'ge', 'no-ge'],
         help='Force selected processing choices, overriding automatic selections '
         '(a space delimited list).\n'
-        ' * [no-]bbr: Use/disable boundary-based registration for BOLD-to-T1w coregistration\n'
+        ' * [no-]bbr: Use/disable boundary-based registration for ASL-to-T1w coregistration\n'
         '             (No goodness-of-fit checks)\n'
         ' * syn-sdc: Calculate SyN-SDC correction *in addition* to other fieldmaps\n'
         ' * [no-]ge: Use/disable GE-specific processing\n',
@@ -353,7 +353,7 @@ def _bids_filter(value, parser):
         help=(
             'Method of initial ASL to anatomical coregistration. If `auto`, a T2w image is used '
             'if available, otherwise the T1w image. `t1w` forces use of the T1w, `t2w` forces use '
-            'of the T2w, and `header` uses the BOLD header information without an initial '
+            'of the T2w, and `header` uses the ASL header information without an initial '
             'registration.'
         ),
     )
@@ -484,7 +484,7 @@ def _bids_filter(value, parser):
         choices=('91k', '170k'),
         type=str,
         help=(
-            'Output preprocessed BOLD as a CIFTI dense timeseries. '
+            'Output preprocessed ASL as a CIFTI dense timeseries. '
             'Optionally, the number of grayordinate can be specified '
             '(default is 91k, which equates to 2mm resolution)'
         ),

aslprep/cli/workflow.py

@@ -141,6 +141,9 @@ def build_boilerplate(config_file, workflow):
     citation_files = {ext: logs_path / f'CITATION.{ext}' for ext in ('bib', 'tex', 'md', 'html')}
 
     if boilerplate:
+        # Rename "BOLD" to "ASL" in the boilerplate
+        boilerplate = boilerplate.replace('BOLD', 'ASL')
+
         # To please git-annex users and also to guarantee consistency
         # among different renderings of the same file, first remove any
         # existing one

aslprep/config.py

@@ -537,7 +537,7 @@ class workflow(_Config):
     asl2anat_init = 'auto'
     """Method of initial ASL to anatomical coregistration. If `auto`, a T2w image is used
     if available, otherwise the T1w image. `t1w` forces use of the T1w, `t2w` forces use of
-    the T2w, and `header` uses the BOLD header information without an initial registration."""
+    the T2w, and `header` uses the ASL header information without an initial registration."""
     m0_scale = float(1)
     """Relative scale between ASL (delta-M) and M0."""
     cifti_output = None

aslprep/data/reports-spec-asl.yml

@@ -33,8 +33,8 @@ sections:
       Inhomogeneities of the <em>B<sub>0</sub></em> field introduce (oftentimes severe) spatial distortions
       along the phase-encoding direction of the image. Utilizing two or more images with different
       phase-encoding polarities (PEPolar) or directions, it is possible to estimate the inhomogeneity
-      of the field. The plot below shows a reference EPI (echo-planar imaging) volume generated
-      using two or more EPI images with varying phase-encoding blips.
+      of the field. The plot below shows a reference ASL volume generated
+      using two or more ASL images with varying phase-encoding blips.
     description: |
       Hover on the panels with the mouse pointer to also visualize the intensity of the
       inhomogeneity of the field in Hertz.
@@ -45,8 +45,8 @@ sections:
       Inhomogeneities of the <em>B<sub>0</sub></em> field introduce (oftentimes severe) spatial distortions
       along the phase-encoding direction of the image. Utilizing an <em>anatomically-correct</em> acquisition
       (for instance, T1w or T2w), it is possible to estimate the inhomogeneity of the field by means of nonlinear
-      registration. The plot below shows a reference EPI (echo-planar imaging) volume generated
-      using two or more EPI images with the same PE encoding, after alignment to the anatomical scan.
+      registration. The plot below shows a reference ASL volume generated
+      using two or more ASL images with the same PE encoding, after alignment to the anatomical scan.
     description: |
       Hover on the panels with the mouse pointer to also visualize the intensity of the
       inhomogeneity of the field in Hertz.
@@ -60,18 +60,18 @@ sections:
   - bids: {datatype: figures, desc: validation, suffix: asl, extension: [.svg]}
   - bids: {datatype: figures, desc: fmapCoreg, suffix: asl, extension: [.svg]}
     caption: |
-      The estimated fieldmap was aligned to the corresponding EPI reference
+      The estimated fieldmap was aligned to the corresponding ASL reference
       with a rigid-registration process of the anatomical reference of the fieldmap,
       using <code>antsRegistration</code>.
-      Overlaid on top of the co-registration results, the final BOLD mask is represented
+      Overlaid on top of the co-registration results, the final ASL mask is represented
       with a red contour for reference.
     static: false
-    subtitle: Alignment between the anatomical reference of the fieldmap and the target EPI (debug mode)
+    subtitle: Alignment between the anatomical reference of the fieldmap and the target ASL (debug mode)
   - bids: {datatype: figures, desc: fieldmap, suffix: asl, extension: [.svg]}
     caption: |
-      Estimated fieldmap, as reconstructed on the target BOLD run space to allow
+      Estimated fieldmap, as reconstructed on the target ASL run space to allow
       the assessment of its alignment with the distorted data.
-      The anatomical reference is the fieldmap's reference moved into the target EPI's grid through
+      The anatomical reference is the fieldmap's reference moved into the target ASL's grid through
       the estimated transformation.
       In other words, this plot should be equivalent to that of the
       <em>Preprocessed estimation with varying Phase-Encoding (PE) blips</em> shown above in the
@@ -82,20 +82,20 @@ sections:
     subtitle: "Reconstructed <em>B<sub>0</sub></em> map in the corresponding run's space (debug mode)"
   - bids: {datatype: figures, desc: sdc, suffix: asl, extension: [.svg]}
     caption: |
-      Results of performing susceptibility distortion correction (SDC) on the EPI
+      Results of performing susceptibility distortion correction (SDC) on the ASL
     static: false
     subtitle: Susceptibility distortion correction
   - bids: {datatype: figures, desc: forcedsyn, suffix: asl, extension: [.svg]}
     caption: |
       The dataset contained some fieldmap information, but the argument <code>--force syn</code>
       was used. The higher-priority SDC method was used. Here, we show the results
-      of performing SyN-based SDC on the EPI for comparison.
+      of performing SyN-based SDC on the ASL for comparison.
     static: false
     subtitle: Experimental fieldmap-less susceptibility distortion correction
   - bids: {datatype: figures, desc: flirtnobbr, suffix: asl, extension: [.svg]}
     caption: |
       <code>mri_coreg</code> (FreeSurfer) was used to generate transformations
-      from EPI space to T1 Space - BBR refinement using FSL <code>flirt</code> rejected.
+      from ASL space to T1 Space - BBR refinement using FSL <code>flirt</code> rejected.
       Note that Nearest Neighbor interpolation is used in the reportlets in order to
       highlight potential spin-history and other artifacts, whereas final images are
       resampled using Lanczos interpolation.
@@ -104,15 +104,15 @@ sections:
   - bids: {datatype: figures, desc: coreg, suffix: asl, extension: [.svg]}
     caption: |
       <code>mri_coreg</code> (FreeSurfer) was used to generate transformations
-      from EPI space to T1 Space - <code>bbregister</code> refinement rejected. Note
+      from ASL space to T1 Space - <code>bbregister</code> refinement rejected. Note
       that Nearest Neighbor interpolation is used in the reportlets in order to highlight
       potential spin-history and other artifacts, whereas final images are resampled
       using Lanczos interpolation.
     static: false
     subtitle: Alignment of functional and anatomical MRI data (volume based)
   - bids: {datatype: figures, desc: flirtbbr, suffix: asl, extension: [.svg]}
     caption: |
-      FSL <code>flirt</code> was used to generate transformations from EPI-space
+      FSL <code>flirt</code> was used to generate transformations from ASL-space
       to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain
       tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation
       is used in the reportlets in order to highlight potential spin-history and other
@@ -121,15 +121,15 @@ sections:
     subtitle: Alignment of functional and anatomical MRI data (surface driven)
   - bids: {datatype: figures, desc: bbregister, suffix: asl, extension: [.svg]}
     caption: |
-      <code>bbregister</code> was used to generate transformations from EPI-space
+      <code>bbregister</code> was used to generate transformations from ASL-space
       to T1w-space. Note that Nearest Neighbor interpolation is used in the reportlets
       in order to highlight potential spin-history and other artifacts, whereas final
       images are resampled using Lanczos interpolation.
     static: false
     subtitle: Alignment of functional and anatomical MRI data (surface driven)
   - bids: {datatype: figures, desc: rois, suffix: asl, extension: [.svg]}
     caption: |
-      Brain mask calculated on the BOLD signal (red contour), along with the
+      Brain mask calculated on the ASL signal (red contour), along with the
       regions of interest (ROIs) used for the estimation of physiological and movement
       confounding components that can be then used as nuisance regressors in analysis.<br />
       The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining

aslprep/data/reports-spec.yml

@@ -64,8 +64,8 @@ sections:
       Inhomogeneities of the <em>B<sub>0</sub></em> field introduce (oftentimes severe) spatial distortions
       along the phase-encoding direction of the image. Utilizing two or more images with different
       phase-encoding polarities (PEPolar) or directions, it is possible to estimate the inhomogeneity
-      of the field. The plot below shows a reference EPI (echo-planar imaging) volume generated
-      using two or more EPI images with varying phase-encoding blips.
+      of the field. The plot below shows a reference ASL volume generated
+      using two or more ASL images with varying phase-encoding blips.
     description: |
       Hover on the panels with the mouse pointer to also visualize the intensity of the
       inhomogeneity of the field in Hertz.
@@ -76,8 +76,8 @@ sections:
       Inhomogeneities of the <em>B<sub>0</sub></em> field introduce (oftentimes severe) spatial distortions
       along the phase-encoding direction of the image. Utilizing an <em>anatomically-correct</em> acquisition
       (for instance, T1w or T2w), it is possible to estimate the inhomogeneity of the field by means of nonlinear
-      registration. The plot below shows a reference EPI (echo-planar imaging) volume generated
-      using two or more EPI images with the same PE encoding, after alignment to the anatomical scan.
+      registration. The plot below shows a reference ASL volume generated
+      using two or more ASL images with the same PE encoding, after alignment to the anatomical scan.
     description: |
       Hover on the panels with the mouse pointer to also visualize the intensity of the
       inhomogeneity of the field in Hertz.
@@ -91,18 +91,18 @@ sections:
   - bids: {datatype: figures, desc: validation, suffix: asl, extension: [.svg]}
   - bids: {datatype: figures, desc: fmapCoreg, suffix: asl, extension: [.svg]}
     caption: |
-      The estimated fieldmap was aligned to the corresponding EPI reference
+      The estimated fieldmap was aligned to the corresponding ASL reference
       with a rigid-registration process of the anatomical reference of the fieldmap,
       using <code>antsRegistration</code>.
-      Overlaid on top of the co-registration results, the final BOLD mask is represented
+      Overlaid on top of the co-registration results, the final ASL mask is represented
       with a red contour for reference.
     static: false
-    subtitle: Alignment between the anatomical reference of the fieldmap and the target EPI (debug mode)
+    subtitle: Alignment between the anatomical reference of the fieldmap and the target ASL (debug mode)
   - bids: {datatype: figures, desc: fieldmap, suffix: asl, extension: [.svg]}
     caption: |
-      Estimated fieldmap, as reconstructed on the target BOLD run space to allow
+      Estimated fieldmap, as reconstructed on the target ASL run space to allow
       the assessment of its alignment with the distorted data.
-      The anatomical reference is the fieldmap's reference moved into the target EPI's grid through
+      The anatomical reference is the fieldmap's reference moved into the target ASL's grid through
       the estimated transformation.
       In other words, this plot should be equivalent to that of the
       <em>Preprocessed estimation with varying Phase-Encoding (PE) blips</em> shown above in the
@@ -113,20 +113,20 @@ sections:
     subtitle: "Reconstructed <em>B<sub>0</sub></em> map in the corresponding run's space (debug mode)"
   - bids: {datatype: figures, desc: sdc, suffix: asl, extension: [.svg]}
     caption: |
-      Results of performing susceptibility distortion correction (SDC) on the EPI
+      Results of performing susceptibility distortion correction (SDC) on the ASL
     static: false
     subtitle: Susceptibility distortion correction
   - bids: {datatype: figures, desc: forcedsyn, suffix: asl, extension: [.svg]}
     caption: |
       The dataset contained some fieldmap information, but the argument <code>--force syn-sdc</code>
       was used. The higher-priority SDC method was used. Here, we show the results
-      of performing SyN-based SDC on the EPI for comparison.
+      of performing SyN-based SDC on the ASL for comparison.
     static: false
     subtitle: Experimental fieldmap-less susceptibility distortion correction
   - bids: {datatype: figures, desc: flirtnobbr, suffix: asl, extension: [.svg]}
     caption: |
       <code>mri_coreg</code> (FreeSurfer) was used to generate transformations
-      from EPI space to T1 Space - BBR refinement using FSL <code>flirt</code> rejected.
+      from ASL space to T1 Space - BBR refinement using FSL <code>flirt</code> rejected.
       Note that Nearest Neighbor interpolation is used in the reportlets in order to
       highlight potential spin-history and other artifacts, whereas final images are
       resampled using Lanczos interpolation.
@@ -135,15 +135,15 @@ sections:
   - bids: {datatype: figures, desc: coreg, suffix: asl, extension: [.svg]}
     caption: |
       <code>mri_coreg</code> (FreeSurfer) was used to generate transformations
-      from EPI space to T1 Space - <code>bbregister</code> refinement rejected. Note
+      from ASL space to T1 Space - <code>bbregister</code> refinement rejected. Note
       that Nearest Neighbor interpolation is used in the reportlets in order to highlight
       potential spin-history and other artifacts, whereas final images are resampled
       using Lanczos interpolation.
     static: false
     subtitle: Alignment of functional and anatomical MRI data (volume based)
   - bids: {datatype: figures, desc: flirtbbr, suffix: asl, extension: [.svg]}
     caption: |
-      FSL <code>flirt</code> was used to generate transformations from EPI-space
+      FSL <code>flirt</code> was used to generate transformations from ASL-space
       to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain
       tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation
       is used in the reportlets in order to highlight potential spin-history and other
@@ -152,15 +152,15 @@ sections:
     subtitle: Alignment of functional and anatomical MRI data (surface driven)
   - bids: {datatype: figures, desc: bbregister, suffix: asl, extension: [.svg]}
     caption: |
-      <code>bbregister</code> was used to generate transformations from EPI-space
+      <code>bbregister</code> was used to generate transformations from ASL-space
       to T1w-space. Note that Nearest Neighbor interpolation is used in the reportlets
       in order to highlight potential spin-history and other artifacts, whereas final
       images are resampled using Lanczos interpolation.
     static: false
     subtitle: Alignment of functional and anatomical MRI data (surface driven)
   - bids: {datatype: figures, desc: rois, suffix: asl, extension: [.svg]}
     caption: |
-      Brain mask calculated on the BOLD signal (red contour), along with the
+      Brain mask calculated on the ASL signal (red contour), along with the
       regions of interest (ROIs) used for the estimation of physiological and movement
       confounding components that can be then used as nuisance regressors in analysis.<br />
       The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining

aslprep/interfaces/plotting.py

@@ -19,8 +19,8 @@
 
 
 class _ASLCarpetPlotInputSpec(BaseInterfaceInputSpec):
-    in_nifti = File(exists=True, mandatory=True, desc='input BOLD (4D NIfTI file)')
-    in_cifti = File(exists=True, desc='input BOLD (CIFTI dense timeseries)')
+    in_nifti = File(exists=True, mandatory=True, desc='input ASL (4D NIfTI file)')
+    in_cifti = File(exists=True, desc='input ASL (CIFTI dense timeseries)')
     in_segm = File(exists=True, desc='volumetric segmentation corresponding to in_nifti')
     confounds_file = File(exists=True, desc="BIDS' _confounds.tsv file")
 

aslprep/workflows/asl/apply.py

@@ -44,7 +44,7 @@ def init_asl_cifti_resample_wf(
     Parameters
     ----------
     metadata
-        BIDS metadata for BOLD file.
+        BIDS metadata for ASL file.
     mem_gb
     fieldmap_id
         Fieldmap identifier, if fieldmap correction is to be applied.
@@ -58,12 +58,12 @@ def init_asl_cifti_resample_wf(
     asl_file
         ASL series to resample.
     bold_ref_file
-        Reference image to which BOLD series is aligned.
+        Reference image to which ASL series is aligned.
     target_ref_file
         Reference image defining the target space.
     target_mask
         Brain mask corresponding to ``target_ref_file``.
-        This is used to define the field of view for the resampled BOLD series.
+        This is used to define the field of view for the resampled ASL series.
     motion_xfm
         List of affine transforms aligning each volume to ``bold_ref_file``.
         If undefined, no motion correction is performed.
@@ -87,7 +87,7 @@ def init_asl_cifti_resample_wf(
         The ``bold_file`` input, resampled to ``target_ref_file`` space.
     resampling_reference
         An empty reference image with the correct affine and header for resampling
-        further images into the BOLD series' space.
+        further images into the ASL series' space.
     """
     from fmriprep.workflows.bold.apply import init_bold_volumetric_resample_wf
     from fmriprep.workflows.bold.resampling import (
@@ -177,7 +177,7 @@ def init_asl_cifti_resample_wf(
     )
 
     workflow.connect([
-        # Resample BOLD to MNI152NLin6Asym, may duplicate asl_std_wf above
+        # Resample ASL to MNI152NLin6Asym, may duplicate asl_std_wf above
         (inputnode, asl_MNI6_wf, [
             ('mni6_mask', 'inputnode.target_ref_file'),
             ('mni6_mask', 'inputnode.target_mask'),
@@ -191,7 +191,7 @@ def init_asl_cifti_resample_wf(
             ('aslref2fmap_xfm', 'inputnode.boldref2fmap_xfm'),
             ('aslref2anat_xfm', 'inputnode.boldref2anat_xfm'),
         ]),
-        # Resample T1w-space BOLD to fsLR surfaces
+        # Resample T1w-space ASL to fsLR surfaces
         (inputnode, asl_fsLR_resampling_wf, [
             ('asl_anat', 'inputnode.bold_file'),
             ('white', 'inputnode.white'),