import numpy as np
import nibabel as nb
import os
import sys
import nighresjava
from ..io import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, \
_check_topology_lut_dir, _check_available_memory
[docs]def mp2rage_skullstripping(second_inversion, t1_weighted=None, t1_map=None,
skip_zero_values=True, topology_lut_dir=None,
save_data=False, overwrite=False, output_dir=None,
file_name=None):
""" MP2RAGE skull stripping
Estimates a brain mask from MRI data acquired with the MP2RAGE sequence.
At least a T1-weighted or a T1 map image is required
Parameters
----------
second_inversion: niimg
Second inversion image derived from MP2RAGE sequence
t1_weighted: niimg
T1-weighted image derived from MP2RAGE sequence (also referred to as
"uniform" image)
At least one of t1_weighted and t1_map is required
t1_map: niimg
Quantitative T1 map image derived from MP2RAGE sequence
At least one of t1_weighted and t1_map is required
skip_zero_values: bool
Ignores voxels with zero value (default is True)
topology_lut_dir: str, optional
Path to directory in which topology files are stored (default is stored
in TOPOLOGY_LUT_DIR)
save_data: bool
Save output data to file (default is False)
overwrite: bool
Overwrite existing results (default is False)
output_dir: str, optional
Path to desired output directory, will be created if it doesn't exist
file_name: str, optional
Desired base name for output files with file extension
(suffixes will be added)
Returns
----------
dict
Dictionary collecting outputs under the following keys
(suffix of output files in brackets)
* brain_mask (niimg): Binary brain mask (_strip-mask)
* inv2_masked (niimg): Masked second inversion imamge (_strip-inv2)
* t1w_masked (niimg): Masked T1-weighted image (_strip-t1w)
* t1map_masked (niimg): Masked T1 map (_strip-t1map)
Notes
----------
Original Java module by Pierre-Louis Bazin. Details on the algorithm can
be found in [1]_ and a presentation of the MP2RAGE sequence in [2]_
References
----------
.. [1] Bazin et al. (2014). A computational framework for ultra-high
resolution cortical segmentation at 7 Tesla.
DOI: 10.1016/j.neuroimage.2013.03.077
.. [2] Marques et al. (2010). MP2RAGE, a self bias-field corrected sequence
for improved segmentation and T1-mapping at high field.
DOI: 10.1016/j.neuroimage.2009.10.002
"""
print('\nMP2RAGE Skull Stripping')
# check topology lut dir and set default if not given
topology_lut_dir = _check_topology_lut_dir(topology_lut_dir)
# make sure that saving related parameters are correct
if save_data:
output_dir = _output_dir_4saving(output_dir, second_inversion)
inv2_file = os.path.join(output_dir,
_fname_4saving(file_name=file_name,
rootfile=second_inversion,
suffix='strip-inv2'))
mask_file = os.path.join(output_dir,
_fname_4saving(file_name=file_name,
rootfile=second_inversion,
suffix='strip-mask'))
if t1_weighted is not None:
t1w_file = os.path.join(output_dir,
_fname_4saving(file_name=file_name,
rootfile=t1_weighted,
suffix='strip-t1w'))
else:
t1w_file = None
if t1_map is not None:
t1map_file = os.path.join(output_dir,
_fname_4saving(file_name=file_name,
rootfile=t1_map,
suffix='strip-t1map'))
else:
t1map_file = None
if overwrite is False \
and os.path.isfile(mask_file) \
and os.path.isfile(inv2_file) :
print("skip computation (use existing results)")
output = {'brain_mask': load_volume(mask_file),
'inv2_masked': load_volume(inv2_file)}
if t1w_file is not None:
if os.path.isfile(t1w_file) :
output['t1w_masked'] = load_volume(t1w_file)
if t1map_file is not None:
if os.path.isfile(t1map_file) :
output['t1map_masked'] = load_volume(t1map_file)
return output
# start virtual machine, if not already running
try:
mem = _check_available_memory()
nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max'])
except ValueError:
pass
# create skulltripping instance
algo = nighresjava.BrainMp2rageSkullStripping()
# get dimensions and resolution from second inversion image
inv2_img = load_volume(second_inversion)
inv2_data = inv2_img.get_data()
inv2_affine = inv2_img.affine
inv2_hdr = inv2_img.header
resolution = [x.item() for x in inv2_hdr.get_zooms()]
dimensions = inv2_data.shape
algo.setDimensions(dimensions[0], dimensions[1], dimensions[2])
algo.setResolutions(resolution[0], resolution[1], resolution[2])
algo.setSecondInversionImage(nighresjava.JArray('float')(
(inv2_data.flatten('F')).astype(float)))
# pass other inputs
if (t1_weighted is None and t1_map is None):
raise ValueError('You must specify at least one of '
't1_weighted and t1_map')
if t1_weighted is not None:
t1w_img = load_volume(t1_weighted)
t1w_data = t1w_img.get_data()
t1w_affine = t1w_img.affine
t1w_hdr = t1w_img.header
algo.setT1weightedImage(nighresjava.JArray('float')(
(t1w_data.flatten('F')).astype(float)))
if t1_map is not None:
t1map_img = load_volume(t1_map)
t1map_data = t1map_img.get_data()
t1map_affine = t1map_img.affine
t1map_hdr = t1map_img.header
algo.setT1MapImage(nighresjava.JArray('float')(
(t1map_data.flatten('F')).astype(float)))
algo.setSkipZeroValues(skip_zero_values)
algo.setTopologyLUTdirectory(topology_lut_dir)
# execute skull stripping
try:
algo.execute()
except:
# if the Java module fails, reraise the error it throws
print("\n The underlying Java code did not execute cleanly: ")
print(sys.exc_info()[0])
raise
return
# collect outputs and potentially save
inv2_masked_data = np.reshape(np.array(
algo.getMaskedSecondInversionImage(),
dtype=np.float32), dimensions, 'F')
inv2_hdr['cal_max'] = np.nanmax(inv2_masked_data)
inv2_masked = nb.Nifti1Image(inv2_masked_data, inv2_affine, inv2_hdr)
mask_data = np.reshape(np.array(algo.getBrainMaskImage(),
dtype=np.uint32), dimensions, 'F')
inv2_hdr['cal_max'] = np.nanmax(mask_data)
mask = nb.Nifti1Image(mask_data, inv2_affine, inv2_hdr)
outputs = {'brain_mask': mask, 'inv2_masked': inv2_masked}
if save_data:
save_volume(inv2_file, inv2_masked)
save_volume(mask_file, mask)
if t1_weighted is not None:
t1w_masked_data = np.reshape(np.array(
algo.getMaskedT1weightedImage(),
dtype=np.float32), dimensions, 'F')
t1w_hdr['cal_max'] = np.nanmax(t1w_masked_data)
t1w_masked = nb.Nifti1Image(t1w_masked_data, t1w_affine, t1w_hdr)
outputs['t1w_masked'] = t1w_masked
if save_data:
save_volume(t1w_file, t1w_masked)
if t1_map is not None:
t1map_masked_data = np.reshape(np.array(
algo.getMaskedT1MapImage(),
dtype=np.float32), dimensions, 'F')
t1map_hdr['cal_max'] = np.nanmax(t1map_masked_data)
t1map_masked = nb.Nifti1Image(t1map_masked_data, t1map_affine,
t1map_hdr)
outputs['t1map_masked'] = t1map_masked
if save_data:
save_volume(t1map_file, t1map_masked)
return outputs