Source code for nighres.brain.filter_stacking

import numpy as np
import nibabel as nb
import os
import sys
from import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving

[docs]def filter_stacking(dura_img=None, pvcsf_img=None, arteries_img=None, save_data=False, overwrite=False, output_dir=None, file_name=None): """ Filter stacking A small utility to combine multiple priors derived from filtering of the CSF partial voluming, arteries, and/or remaining dura mater. The filter priors (in [0,1]) are arranged in a specific way (in increments of 2) that is expected by the Filters contrast type in MGDM Parameters ---------- dura_img: niimg, optional Prior for the location of remaining dura mater after skull stripping. At least one prior image is required pvcsf_img: niimg, optional Prior for the location of CSF partial voluming (mostly in sulcal regions). At least one prior image is required arteries_img: niimg, optional Prior for the location of arteries, visible e.g. in MP2RAGE images. At least one prior image is required 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 in brackets) * result (niimg): Combined image, where only the strongest priors are kept (_bfs-img) Notes ---------- Original Java module by Pierre-Louis Bazin. """ print('\nFilter stacking') # check if there's inputs if (dura_img is None and pvcsf_img is None and arteries_img is None): raise ValueError('You must specify at least one of ' 'pvcsf_img, arteries_img and dura_img') # find the first existing input for dimensions, resolutions, name img = None if (dura_img != None): img = dura_img elif (pvcsf_img != None): img = pvcsf_img elif (arteries_img != None): img = arteries_img # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, img) filter_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=img, suffix='bfs-img')) if overwrite is False \ and os.path.isfile(filter_file) : print("skip computation (use existing results)") output = {"result": filter_file} return output affine = load_volume(img).affine header = load_volume(img).header resolution = [x.item() for x in header.get_zooms()] dimensions = header.get_data_shape() nx = dimensions[0]; ny = dimensions[1]; nz = dimensions[2]; # build empty filter filter_data = np.zeros(dimensions) # add inputs if dura_img is not None: dura_data = load_volume(dura_img).get_data() for x in range(nx): for y in range(ny): for z in range(nz): filter_data[x,y,z] = dura_data[x,y,z] dura_data = None dura_img = None if pvcsf_img is not None: pvcsf_data = load_volume(pvcsf_img).get_data() for x in range(nx): for y in range(ny): for z in range(nz): if (pvcsf_data[x,y,z]>filter_data[x,y,z]): filter_data[x,y,z] = pvcsf_data[x,y,z]+2.0 pvcsf_data = None pvcsf_img = None if arteries_img is not None: arteries_data = load_volume(arteries_img).get_data() for x in range(nx): for y in range(ny): for z in range(nz): if (filter_data[x,y,z]>2): if (arteries_data[x,y,z]>filter_data[x,y,z]-2): filter_data[x,y,z] = arteries_data[x,y,z]+4.0 else: if (arteries_data[x,y,z]>filter_data[x,y,z]): filter_data[x,y,z] = arteries_data[x,y,z]+4.0 arteries_data = None arteries_img = None header['cal_max'] = np.nanmax(filter_data) filter_img = nb.Nifti1Image(filter_data, affine, header) if save_data: save_volume(filter_file, filter_img) return {"result": filter_file} else: return {"result": filter_img}