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_dura_estimation(second_inversion, skullstrip_mask,
background_distance=5.0, output_type='dura_region',
save_data=False, overwrite=False, output_dir=None,
file_name=None):
""" MP2RAGE dura estimation
Filters a MP2RAGE brain image to obtain a probability map of dura matter.
Parameters
----------
second_inversion: niimg
Second inversion image derived from MP2RAGE sequence
skullstrip_mask: niimg
Skullstripping mask defining the approximate region including the brain
background_distance: float
Maximum distance within the mask for dura (default is 5.0 mm)
output_type: {'dura_region','boundary','dura_prior','bg_prior',
'intens_prior'}
Type of output result (default is 'dura_region')
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)
return_filename: bool, optional
Return filename instead of object
Returns
----------
dict
Dictionary collecting outputs under the following keys
(suffix of output files in brackets)
* result (niimg): Dura probability image (_dura-proba)
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 Dura Estimation')
# make sure that saving related parameters are correct
if save_data:
output_dir = _output_dir_4saving(output_dir, second_inversion)
result_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=second_inversion,
suffix='dura-proba'))
if overwrite is False \
and os.path.isfile(result_file) :
print("skip computation (use existing results)")
output = {'result': result_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.BrainMp2rageDuraEstimation()
# 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
mask_data = load_volume(skullstrip_mask).get_data()
algo.setSkullStrippingMask(nighresjava.JArray('int')(
(mask_data.flatten('F')).astype(int).tolist()))
algo.setDistanceToBackground_mm(background_distance)
algo.setOutputType(output_type)
# 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
result_data = np.reshape(np.array(
algo.getDuraImage(),
dtype=np.float32), dimensions, 'F')
inv2_hdr['cal_max'] = np.nanmax(result_data)
result_img = nb.Nifti1Image(result_data, inv2_affine, inv2_hdr)
if save_data:
save_volume(result_file, result_img)
outputs = {'result': result_file}
else:
outputs = {'result': result_img}
return outputs