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 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}