Source code for nighres.intensity.phase_unwrapping

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 phase_unwrapping(image, mask=None, nquadrants=3, tv_flattening=False, tv_scale=0.5, save_data=False, overwrite=False, output_dir=None, file_name=None): """ Fast marching phase unwrapping Fast marching method for unwrapping phase images, based on _[1] Parameters ---------- image: niimg Input phase image to unwrap mask: niimg, optional Data mask to specify acceptable seeding regions nquadrants: int, optional Number of image quadrants to use (default is 3) tv_flattening: bool, optional Whether or not to run a post-processing step to remove background phase variations with a total variation filter (default is False) tv_scale: float, optional Relative intensity scale for the TV filter (default is 0.5) 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): The unwrapped image rescaled in radians Notes ---------- Original Java module by Pierre-Louis Bazin. Algorithm adapted from [1]_ with additional seeding in multiple image quadrants to reduce the effects of possible phase singularities References ---------- .. [1] Abdul-Rahman, Gdeisat, Burton and Lalor. Fast three-dimensional phase-unwrapping algorithm based on sorting by reliability following a non-continuous path. doi: 10.1117/12.611415 """ print('\nFast marching phase unwrapping') # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, image) out_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=image, suffix='unwrap-img')) if overwrite is False \ and os.path.isfile(out_file) : print("skip computation (use existing results)") output = {'result': out_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 instance unwrap = nighresjava.FastMarchingPhaseUnwrapping() # set parameters # load image and use it to set dimensions and resolution img = load_volume(image) data = img.get_data() affine = img.affine header = img.header resolution = [x.item() for x in header.get_zooms()] dimensions = data.shape dimensions3D = (dimensions[0], dimensions[1], dimensions[2]) unwrap.setDimensions(dimensions[0], dimensions[1], dimensions[2]) unwrap.setResolutions(resolution[0], resolution[1], resolution[2]) unwrap.setPhaseImage(nighresjava.JArray('float')( (data.flatten('F')).astype(float)[0:dimensions[0]*dimensions[1]*dimensions[2]])) if mask is not None: unwrap.setMaskImage(idx, nighresjava.JArray('int')( (load_volume(mask).get_data().flatten('F')).astype(int).tolist())) # set algorithm parameters unwrap.setQuadrantNumber(nquadrants) if tv_flattening: unwrap.setTVPostProcessing("TV-residuals") unwrap.setTVScale(tv_scale) # execute the algorithm try: unwrap.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 # reshape output to what nibabel likes unwrap_data = np.reshape(np.array(unwrap.getCorrectedImage(), dtype=np.float32), dimensions3D, 'F') # adapt header max for each image so that correct max is displayed # and create nifiti objects header['cal_min'] = np.nanmin(unwrap_data) header['cal_max'] = np.nanmax(unwrap_data) out = nb.Nifti1Image(unwrap_data, affine, header) if save_data: save_volume(out_file, out) return {'result': out_file} else: return {'result': out}