Merge branch 'master' into long

Author: chrisgorgo

README.md

@@ -59,7 +59,7 @@ This App has the following command line arguments:
 		                        analyzed. Multiple participants can be specified with
 		                        a space separated list.
 		  --n_cpus N_CPUS       Number of CPUs/cores available to use. (Default is 1)
-		  --stages {all,autorecon1,autorecon2,autorecon2-cp,autorecon2-wm,autorecon2-pial,autorecon3,autorecon-all}
+		  --stages {all,autorecon1,autorecon2,autorecon2-cp,autorecon2-wm,autorecon-pial,autorecon3,autorecon-all}
 		                        Recon-all stages to run. (Default is autorecon-all)
 		  --template_name TEMPLATE_NAME
 		                        Name for the custom group level template generated
@@ -82,10 +82,14 @@ This App has the following command line arguments:
 		                        (resolution <1.2mm isovolumetric). If you want to
 		                        ignore these, specify None or T1only to generate
 		                        surfaces on the T1 alone.
-      --hires_mode {auto,enable,disable}
-                            Submilimiter (high resolution) processing. 'auto' -
-                            use only if <1.0mm data detected, 'enable' - force on,
-                            'disable' - force off
+		  --refine_pial_acquisition_label ACQUISITION_LABEL
+                    			If the dataset contains multiple T2 or FLAIR weighted
+					images from different acquisitions which one should be
+					used? Corresponds to "acq-<acquisition_label>"
+		  --hires_mode {auto,enable,disable}
+		                        Submilimiter (high resolution) processing. 'auto' -
+		                        use only if <1.0mm data detected, 'enable' - force on,
+		                        'disable' - force off
 
 To run it in participant level mode (for one participant):
 

run.py

@@ -45,14 +45,15 @@ def run(command, env={}, ignore_errors=False):
 parser.add_argument('--n_cpus', help='Number of CPUs/cores available to use.',
                    default=1, type=int)
 parser.add_argument('--stages', help='Autorecon stages to run.',
-                    choices=["autorecon1", "autorecon2", "autorecon2-cp", "autorecon2-wm", "autorecon2-pial", "autorecon3", "autorecon-all", "all"],
+                    choices=["autorecon1", "autorecon2", "autorecon2-cp", "autorecon2-wm", "autorecon-pial", "autorecon3", "autorecon-all", "all"],
                     default=["autorecon-all"],
                     nargs="+")
 parser.add_argument('--template_name', help='Name for the custom group level template generated for this dataset',
                     default="average")
 parser.add_argument('--license_key', help='FreeSurfer license key - letters and numbers after "*" in the email you received after registration. To register (for free) visit https://surfer.nmr.mgh.harvard.edu/registration.html',
                     required=True)
 parser.add_argument('--acquisition_label', help='If the dataset contains multiple T1 weighted images from different acquisitions which one should be used? Corresponds to "acq-<acquisition_label>"')
+parser.add_argument('--refine_pial_acquisition_label', help='If the dataset contains multiple T2 or FLAIR weighted images from different acquisitions which one should be used? Corresponds to "acq-<acquisition_label>"')
 parser.add_argument('--multiple_sessions', help='For datasets with multiday sessions where you do not want to '
                     'use the longitudinal pipeline, i.e., sessions were back-to-back, '
                     'set this to multiday, otherwise sessions with T1w data will be '
@@ -105,6 +106,10 @@ def run(command, env={}, ignore_errors=False):
 else:
     longitudinal_study = False
 
+if args.refine_pial_acquisition_label:
+    acq_t2 = "*acq-%s*" % args.refine_pial_acquisition_label
+else:
+    acq_t2 = "*"
 
 subjects_to_analyze = []
 # only for a subset of subjects
@@ -151,10 +156,10 @@ def run(command, env={}, ignore_errors=False):
 
                 T2s = glob(os.path.join(args.bids_dir, "sub-%s"%subject_label,
                                         "ses-%s"%session_label, "anat",
-                                        "*_T2w.nii*"))
+                                        "*%s_T2w.nii*"%acq_t2))
                 FLAIRs = glob(os.path.join(args.bids_dir, "sub-%s"%subject_label,
                                         "ses-%s"%session_label, "anat",
-                                        "*_FLAIR.nii*"))
+                                        "*%s_FLAIR.nii*"%acq_t2))
                 if args.refine_pial == "T2":
                     for T2 in T2s:
                         if max(nibabel.load(T2).header.get_zooms()) < 1.2:
@@ -249,12 +254,12 @@ def run(command, env={}, ignore_errors=False):
                                     "sub-%s"%subject_label,
                                     "ses-*",
                                     "anat",
-                                    "*_T2w.nii*"))
+                                    "*%s_T2w.nii*"%acq_t2))
             FLAIRs = glob(os.path.join(args.bids_dir,
                                     "sub-%s"%subject_label,
                                     "ses-*",
                                     "anat",
-                                    "*_FLAIR.nii*"))
+                                    "*%s_FLAIR.nii*"%acq_t2))
             if args.refine_pial == "T2":
                 for T2 in T2s:
                     if max(nibabel.load(T2).header.get_zooms()) < 1.2:
@@ -303,9 +308,9 @@ def run(command, env={}, ignore_errors=False):
                     input_args += " -hires"
                 input_args += " -i %s"%T1
             T2s = glob(os.path.join(args.bids_dir, "sub-%s"%subject_label, "anat",
-                                    "*_T2w.nii*"))
+                                    "*%s_T2w.nii*"%acq_t2))
             FLAIRs = glob(os.path.join(args.bids_dir, "sub-%s"%subject_label, "anat",
-                                    "*_FLAIR.nii*"))
+                                    "*%s_FLAIR.nii*"%acq_t2))
             if args.refine_pial == "T2":
                 for T2 in T2s:
                     if max(nibabel.load(T2).header.get_zooms()) < 1.2: