Use correct bolus values in CBF calculation and temporarily disable Q2TIPS, multi-PLD, and no-BolusCutOff processing

aslprep/interfaces/ge.py

@@ -20,8 +20,6 @@ class _GeReferenceFileInputSpec(BaseInterfaceInputSpec):
     in_file = File(exists=True, mandatory=True, desc="asl_file")
     in_metadata = traits.Dict(exists=True, mandatory=True, desc="metadata for asl or deltam ")
     bids_dir = traits.Str(exits=True, mandatory=True, desc=" bids directory")
-    ref_file = File(exists=False, mandatory=False, desc="ref file")
-    m0_file = File(exists=False, mandatory=False, desc="m0 file")
     fwhm = traits.Float(
         exits=False, mandatory=False, default_value=5, desc="smoothing kernel for M0"
     )
@@ -42,11 +40,11 @@ def _run_interface(self, runtime):
         filex = os.path.abspath(self.inputs.in_file)
         aslcontext1 = filex.replace("_asl.nii.gz", "_aslcontext.tsv")
         aslcontext = pd.read_csv(aslcontext1)
-        idasl = aslcontext["volume_type"].tolist()
-        m0list = [i for i in range(0, len(idasl)) if idasl[i] == "m0scan"]
-        deltamlist = [i for i in range(0, len(idasl)) if idasl[i] == "deltam"]
-        controllist = [i for i in range(0, len(idasl)) if idasl[i] == "control"]
-        cbflist = [i for i in range(0, len(idasl)) if idasl[i] == "CBF"]
+        vol_types = aslcontext["volume_type"].tolist()
+        control_volume_idx = [i for i, vol_type in enumerate(vol_types) if vol_type == "control"]
+        m0_volume_idx = [i for i, vol_type in enumerate(vol_types) if vol_type == "m0scan"]
+        deltam_volume_idx = [i for i, vol_type in enumerate(vol_types) if vol_type == "deltam"]
+        cbf_volume_idx = [i for i, vol_type in enumerate(vol_types) if vol_type == "CBF"]
 
         allasl = nb.load(self.inputs.in_file)
         dataasl = allasl.get_fdata()
@@ -57,7 +55,7 @@ def _run_interface(self, runtime):
             m0file = reffile
 
         elif self.inputs.in_metadata["M0Type"] == "Included":
-            modata2 = dataasl[:, :, :, m0list]
+            modata2 = dataasl[:, :, :, m0_volume_idx]
             m0filename = fname_presuffix(
                 self.inputs.in_file, suffix="_mofile", newpath=os.getcwd()
             )
@@ -68,12 +66,12 @@ def _run_interface(self, runtime):
 
         elif self.inputs.in_metadata["M0Type"] == "Estimate":
             m0num = float(self.inputs.in_metadata["M0Estimate"])
-            if len(deltamlist) > 0:
-                modata2 = dataasl[:, :, :, deltamlist]
-            elif len(controllist) > 0:
-                modata2 = dataasl[:, :, :, controllist]
-            elif len(cbflist) > 0:
-                modata2 = dataasl[:, :, :, cbflist]
+            if len(deltam_volume_idx) > 0:
+                modata2 = dataasl[:, :, :, deltam_volume_idx]
+            elif len(control_volume_idx) > 0:
+                modata2 = dataasl[:, :, :, control_volume_idx]
+            elif len(cbf_volume_idx) > 0:
+                modata2 = dataasl[:, :, :, cbf_volume_idx]
 
             m0filename = fname_presuffix(
                 self.inputs.in_file, suffix="_m0file", newpath=os.getcwd()
@@ -98,8 +96,8 @@ def _run_interface(self, runtime):
             reffile = gen_reference(reffilename, fwhm=0, newpath=runtime.cwd)
 
         elif self.inputs.in_metadata["M0Type"] == "Absent":
-            if len(controllist) > 0:
-                modata2 = dataasl[:, :, :, controllist]
+            if len(control_volume_idx) > 0:
+                modata2 = dataasl[:, :, :, control_volume_idx]
             m0filename = fname_presuffix(
                 self.inputs.in_file, suffix="_m0file", newpath=os.getcwd()
             )
@@ -126,8 +124,8 @@ def _run_interface(self, runtime):
 
         else:
             raise RuntimeError("no path way to obtain real m0scan")
+
         self._results["ref_file"] = reffile
         self._results["m0_file"] = m0file
-        self.inputs.ref_file = os.path.abspath(self._results["ref_file"])
-        self.inputs.m0_file = os.path.abspath(self._results["m0_file"])
+
         return runtime

aslprep/tests/data/test_outputs_sub10R01383.txt

@@ -0,0 +1,46 @@
+aslprep/logs
+aslprep/sub-10R01383
+aslprep/sub-10R01383/perf
+aslprep/sub-10R01383/perf/sub-10R01383_aslref.nii.gz
+aslprep/sub-10R01383/perf/sub-10R01383_cbf.nii.gz
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-HarvardOxford_desc-mean_basil.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-HarvardOxford_desc-mean_cbf.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-HarvardOxford_desc-mean_pvc.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-HarvardOxford_desc-mean_score.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-HarvardOxford_desc-mean_scrub.csv
+aslprep/sub-10R01383/perf/sub-10R01383_desc-basil_cbf.nii.gz
+aslprep/sub-10R01383/perf/sub-10R01383_desc-bat_cbf.nii.gz
+aslprep/sub-10R01383/perf/sub-10R01383_desc-brain_mask.json
+aslprep/sub-10R01383/perf/sub-10R01383_desc-brain_mask.nii.gz
+aslprep/sub-10R01383/perf/sub-10R01383_desc-confounds_regressors.tsv
+aslprep/sub-10R01383/perf/sub-10R01383_desc-preproc_asl.json
+aslprep/sub-10R01383/perf/sub-10R01383_desc-preproc_asl.nii.gz
+aslprep/sub-10R01383/perf/sub-10R01383_desc-pvGM_cbf.nii.gz
+aslprep/sub-10R01383/perf/sub-10R01383_desc-pvWM_cbf.nii.gz
+aslprep/sub-10R01383/perf/sub-10R01383_desc-qualitycontrol_cbf.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer200x17_desc-mean_basil.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer200x17_desc-mean_cbf.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer200x17_desc-mean_pvc.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer200x17_desc-mean_score.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer200x17_desc-mean_scrub.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer200x7_desc-mean_basil.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer200x7_desc-mean_cbf.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer200x7_desc-mean_pvc.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer200x7_desc-mean_score.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer200x7_desc-mean_scrub.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer400x17_desc-mean_basil.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer400x17_desc-mean_cbf.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer400x17_desc-mean_pvc.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer400x17_desc-mean_score.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer400x17_desc-mean_scrub.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer400x7_desc-mean_basil.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer400x7_desc-mean_cbf.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer400x7_desc-mean_pvc.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer400x7_desc-mean_score.csv
+aslprep/sub-10R01383/perf/sub-10R01383_atlas-schaefer400x7_desc-mean_scrub.csv
+aslprep/sub-10R01383/perf/sub-10R01383_desc-score_cbf.nii.gz
+aslprep/sub-10R01383/perf/sub-10R01383_desc-meanScore_cbf.nii.gz
+aslprep/sub-10R01383/perf/sub-10R01383_desc-scrub_cbf.nii.gz
+aslprep/sub-10R01383/perf/sub-10R01383_from-T1w_to-scanner_mode-image_xfm.txt
+aslprep/sub-10R01383/perf/sub-10R01383_from-scanner_to-T1w_mode-image_xfm.txt
+aslprep/sub-10R01383/perf/sub-10R01383_desc-mean_cbf.nii.gz

aslprep/tests/test_cli.py

@@ -88,3 +88,51 @@ def test_subA00086748(datasets, output_dir, working_dir):
 
     output_list_file = os.path.join(test_data_dir, "test_outputs_subA00086748.txt")
     check_generated_files(out_dir, output_list_file)
+
+
+@pytest.mark.sub10R01383
+def test_sub10R01383(datasets, output_dir, working_dir):
+    """Run aslprep on sub-10R01383.
+
+    Currently skipped.
+
+    Notes
+    -----
+    scorescrub fails on this dataset, so I've dropped that parameter.
+    I'll probably need to dig into why that happens at some point.
+    """
+    from aslprep import config
+
+    test_name = "test_sub10R01383"
+
+    data_dir = datasets["dset"]
+    smriprep_dir = datasets["smriprep"]
+    out_dir = os.path.join(output_dir, test_name)
+    work_dir = os.path.join(working_dir, test_name)
+
+    test_data_dir = get_test_data_path()
+
+    parameters = [
+        data_dir,
+        out_dir,
+        "participant",
+        "--participant-label=10R01383",
+        f"-w={work_dir}",
+        "--nthreads=2",
+        "--omp-nthreads=2",
+        "--output-spaces=asl",
+        "--basil",
+        "--use-syn-sdc",
+        f"--anat-derivatives={smriprep_dir}",
+    ]
+    parse_args(parameters)
+    config_file = config.execution.work_dir / f"config-{config.execution.run_uuid}.toml"
+    config.to_filename(config_file)
+
+    retval = {}
+    retval = build_workflow(config_file, retval=retval)
+    aslprep_wf = retval.get("workflow", None)
+    aslprep_wf.run()
+
+    output_list_file = os.path.join(test_data_dir, "test_outputs_sub10R01383.txt")
+    check_generated_files(out_dir, output_list_file)