Source code for nighres.brain.mp2rage_dura_estimation

import numpy as np
import nibabel as nb
import os
import sys
import nighresjava
from 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