options to create csf_mask from T2star map, and edge and out-of-the-b…

…rain mask from brain mask given by the user

aroma/aroma.py

@@ -27,7 +27,13 @@ def aroma_workflow(
  debug=False,
  quiet=False,
  mc_source="auto",
- f_hp=0.01
+ f_hp=0.01,
+ T2star_map=None,
+ brain_mask=None,
+ T2star_thresh=0.08,
+ erode_csf_voxels=2,
+ erode_edge_voxels=2,
+ dilate_out_voxels=0
 ):
  """Run the AROMA workflow.
 
@@ -56,6 +62,21 @@ def aroma_workflow(
  What format is the mc file in?
  f_hp : float, optional
  High-pass cutoff frequency in spectrum computations.
+ T2star_map : str, optional
+ path to nifti file with T2star map.
+ brain_mask: str, optional
+ path to nifti file with brain mask
+ T2star_thresh: float, optional
+ mask will only consider voxels with T2-star values
+ above T2star_thresh. Default value of 0.080 sec assumes 3T 
+ acquisition.
+ erode_voxels: : int, optional
+ number of voxels to erode (radius of ball)
+ erode_edge_voxels: int, optional
+ Number of voxels to erode in order to compute the edge mask.
+ dilate_out_voxels: int, optional
+ Number of voxels to erode in order to compute the out-of-brain mask
+
  """
  if not op.isfile(in_file):
  raise FileNotFoundError(f"Input file does not exist: {in_file}")
@@ -167,6 +188,23 @@ def aroma_workflow(
  f"number of rows in motion parameters ({motion_params.shape[0]})."
  )
 
+ # Compute spatial metrics (CSF & Edge fraction)
+
+ # create csf mask
+ if (T2star_map is None) and (brain_mask is None) :
+ masks_dir = utils.get_resource_path()
+ csf_mask = os.path.join(masks_dir, "mask_csf.nii.gz")
+ else:
+ csf_mask = utils.csf_mask_from_T2star(T2star_map,brain_mask,out_dir,T2star_thresh,erode_csf_voxels)
+
+ # create edge and out-of-the-brain masks
+ if brain_mask is None:
+ masks_dir = utils.get_resource_path()
+ edge_mask = os.path.join(masks_dir, "mask_edge.nii.gz")
+ out_mask = os.path.join(masks_dir, "mask_out.nii.gz")
+ else:
+ edge_mask,out_mask = utils.edge_out_mask(brain_mask,out_dir,erode_edge_voxels,dilate_out_voxels)
+
  LGR.info(" - extracting the CSF & Edge fraction features")
  metric_metadata = {}
  features_df = pd.DataFrame()
@@ -176,13 +214,15 @@ def aroma_workflow(
  metric_metadata
  ) = features.feature_spatial(component_maps, csf_mask, edge_mask, out_mask, metric_metadata)
 
+ # Compute temporal metrics (correlation with RP)
  LGR.info(" - extracting the Maximum RP correlation feature")
  features_df["max_RP_corr"], metric_metadata = features.feature_time_series(
  mixing,
  motion_params,
  metric_metadata,
  )
 
+ # Compute frequency metrics (half-power frequency)
  LGR.info(" - extracting the High-frequency content feature")
  # Should probably check that the frequencies match up with MELODIC's outputs
  mel_FT_mix, FT_freqs = utils.get_spectrum(mixing, TR)

aroma/utils.py

@@ -3,7 +3,6 @@
 import logging
 import os.path as op
 import shutil
-import tempfile
 
 import nibabel as nib
 import numpy as np
@@ -442,20 +441,22 @@ def get_spectrum(data: np.array, tr: float):
  idx = np.argsort(freqs)
  return power_spectrum[idx, :], freqs[idx]
 
-def csf_mask_from_T2star(T2star_map:str, brain_mask:str, T2star_thresh:float= 0.080, erode_voxels:int=2):
+def csf_mask_from_T2star(T2star_map:str, brain_mask:str, out_dir:str, T2star_thresh:float= 0.080, erode_voxels:int=2):
  """"Create mask of ventricle CSF based on T2-star map. 
 
  Parameters
  ----------
  T2star_map: str
  path to nifti file with T2star map.
  brain_mask: str
- path to nifti file with brain mask
+ path to nifti file with brain mask.
+ out_dir: str
+ Output directory.
  T2star_thresh: :obj:`float`
  mask will only consider voxels with T2-star values
  above T2star_thresh. Default value of 0.080 sec assumes 3T 
  acquisition.
- erode_voxels: :obj:`int``
+ erode_voxels: :obj:`int`
  number of voxels to erode (radius of ball)
 
  Returns
@@ -471,7 +472,6 @@ def csf_mask_from_T2star(T2star_map:str, brain_mask:str, T2star_thresh:float= 0.
  # TODO: pass number of iterations as input argument
 
  # save to nifti file
- temp_dir = tempfile.mkdtemp()
  path_csf_mask = op.join(temp_dir,"mask_csf.nii.gz")
 
  # Apply threshold to identify CSF voxels
@@ -482,7 +482,7 @@ def csf_mask_from_T2star(T2star_map:str, brain_mask:str, T2star_thresh:float= 0.
  return path_csf_mask
 
 
-def edge_out_mask(brain_mask:str, erode_edge_voxels:int=2, dilate_out_voxels:int=0):
+def edge_out_mask(brain_mask:str, out_dir:str, erode_edge_voxels:int=2, dilate_out_voxels:int=0):
  """"Create mask of edge voxels and out-of-the-brain voxels. 
  The edge mask will be the subtraction of the brain mask minus
  this mask eroded by erode_voxels, whereas the out_mask will
@@ -492,10 +492,13 @@ def edge_out_mask(brain_mask:str, erode_edge_voxels:int=2, dilate_out_voxels:int
 
  Parameters
  ----------
- brain_img: path to nifti file with a brain to compute the within-brain mask
+ brain_mask: str,
+ path to nifti file with a brain to compute the within-brain mask.
+ out_dir: str,
+ Output directory.
  erode_edge_voxels: :obj:`int`
  Number of voxels to erode in order to compute the edge mask.
- dilate_out_voxels: :obj:`int``
+ dilate_out_voxels: :obj:`int`
  Number of voxels to erode in order to compute the out-of-brain mask
 
  Returns
@@ -516,9 +519,8 @@ def edge_out_mask(brain_mask:str, erode_edge_voxels:int=2, dilate_out_voxels:int
  out_mask_bool = binary_dilation(brain_mask_bool, structure=ball(dilate_out_voxels)) 
 
  # save to nifti file
- temp_dir = tempfile.mkdtemp()
- path_edge_mask = op.join(temp_dir,"mask_edge.nii.gz")
- path_out_mask = op.join(temp_dir,"mask_out.nii.gz")
+ path_edge_mask = op.join(out_dir,"mask_edge.nii.gz")
+ path_out_mask = op.join(out_dir,"mask_out.nii.gz")
 
  # Compute masks
  edge_mask = nib.Nifti1Image(brain_edge_bool,