import os
import numpy as np
import nibabel as nb
import nighresjava
from ..io import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, _check_available_memory
[docs]def laminar_iterative_smoothing(profile_surface_image, intensity_image, fwhm_mm,
roi_mask_image=None,
save_data=False, overwrite=False, output_dir=None,
file_name=None):
'''Smoothing data on multiple intracortical layers
Parameters
-----------
data_image: niimg
Image from which data should be sampled
profile_surface_image: niimg
4D image containing levelset representations of different intracortical
surfaces on which data should be sampled
fwhm_mm: float
Full width half maximum distance to use in smoothing (in mm)
roi_mask_image: niimg, optional
Mask image defining a region of interest to restrict the smoothing
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)
* result (niimg): smoothed intensity image (_lis-smooth)
Notes
----------
Original Java module by Pierre-Louis Bazin
Important: this method assumes isotropic voxels
'''
print('\nLaminar iterative smoothing')
# make sure that saving related parameters are correct
if save_data:
output_dir = _output_dir_4saving(output_dir, intensity_image)
smoothed_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=intensity_image,
suffix='lis-smooth'))
if overwrite is False \
and os.path.isfile(smoothed_file) :
print("skip computation (use existing results)")
output = {"result": smoothed_file}
return output
# start VM if not already running
try:
mem = _check_available_memory()
nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max'])
except ValueError:
pass
# initate class
smoother = nighresjava.LaminarIterativeSmoothing()
# load the data
surface_img = load_volume(profile_surface_image)
surface_data = surface_img.get_data()
layers = surface_data.shape[3]-1
intensity_img = load_volume(intensity_image)
intensity_data = intensity_img.get_data()
hdr = intensity_img.header
aff = intensity_img.affine
resolution = [x.item() for x in hdr.get_zooms()]
dimensions = intensity_data.shape
if (roi_mask_image!=None) :
roi_mask_data = load_volume(data_image).get_data()
else :
roi_mask_data = None
# pass inputs
smoother.setIntensityImage(nighresjava.JArray('float')(
(intensity_data.flatten('F')).astype(float)))
smoother.setProfileSurfaceImage(nighresjava.JArray('float')(
(surface_data.flatten('F')).astype(float)))
smoother.setResolutions(resolution[0], resolution[1], resolution[2])
smoother.setDimensions(dimensions[0], dimensions[1], dimensions[2])
smoother.setLayers(layers)
if (len(dimensions)>3) :
smoother.set4thDimension(dimensions[3])
else :
smoother.set4thDimension(1)
if (roi_mask_data!=None):
smoother.setROIMask(nighresjava.JArray('int')(
(roi_mask_data.flatten('F')).astype(int).tolist()))
smoother.setFWHMmm(float(fwhm_mm))
# execute class
try:
smoother.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
# collecting outputs
smoothed_data = np.reshape(np.array(
smoother.getSmoothedIntensityImage(),
dtype=np.float32), dimensions, 'F')
hdr['cal_max'] = np.nanmax(smoothed_data)
smoothed = nb.Nifti1Image(smoothed_data, aff, hdr)
if save_data:
save_volume(smoothed_file, smoothed)
return {"result": smoothed_file}
else:
return {"result": smoothed}