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 mp2rageme_pd_mapping(first_inversion, second_inversion,
t1map, r2smap, echo_times,
inversion_times, flip_angles, inversion_TR,
excitation_TR, N_excitations, efficiency=0.96,
b1map=None,
save_data=False, overwrite=False, output_dir=None,
file_name=None):
""" MP2RAGEME PD mapping
Estimate PD maps from MP2RAGEME data, combining T1 and R2* estimates
with the MPRAGE model of [1]_ .
Parameters
----------
first_inversion: [niimg]
List of {magnitude, phase} images for the first inversion
second_inversion: [niimg]
List of {magnitude, phase} images for the second inversion
t1map: niimg
Quantitative T1 map image, in milliseconds
r2smap: niimg
Quantitative R2* map image, in kHz
echo_times: [float]
List of {te1, te2, te3, te4, te5} echo times, in seconds
inversion_times: [float]
List of {first, second} inversion times, in seconds
flip_angles: [float]
List of {first, second} flip angles, in degrees
inversion_TR: float
Inversion repetition time, in seconds
excitation_TR: [float]
List of {first,second} repetition times,in seconds
N_excitations: int
Number of excitations
efficiency: float
Inversion efficiency (default is 0.96)
correct_B1: bool
Whether to correct for B1 inhomogeneities (default is False)
b1map: niimg
Computed B1 map (optional)
scale_phase: bool
Whether to rescale the phase image in [0,2PI] or to assume it is
already in radians
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)
* pd (niimg): Map of estimated proton density ratio (_qpd-map)
Notes
----------
Original Java module by Pierre-Louis Bazin.
References
----------
.. [1] Marques, Kober, Krueger, van der Zwaag, Van de Moortele, Gruetter (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('\nPD Mapping')
# make sure that saving related parameters are correct
if save_data:
output_dir = _output_dir_4saving(output_dir, first_inversion[0])
pd_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=first_inversion[0],
suffix='qpd-map'))
if overwrite is False \
and os.path.isfile(pd_file) :
output = {'pd': pd_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 algorithm instance
qpdmap = nighresjava.IntensityMp2ragemePDmapping()
# set algorithm parameters
qpdmap.setFirstEchoTime(echo_times[0])
qpdmap.setFirstInversionTime(inversion_times[0])
qpdmap.setSecondInversionTime(inversion_times[1])
qpdmap.setFirstFlipAngle(flip_angles[0])
qpdmap.setSecondFlipAngle(flip_angles[1])
qpdmap.setInversionRepetitionTime(inversion_TR)
qpdmap.setFirstExcitationRepetitionTime(excitation_TR[0])
qpdmap.setSecondExcitationRepetitionTime(excitation_TR[1])
qpdmap.setNumberExcitations(N_excitations)
qpdmap.setInversionEfficiency(efficiency)
qpdmap.setCorrectB1inhomogeneities(b1map!=None)
# load first image and use it to set dimensions and resolution
img = load_volume(first_inversion[0])
data = img.get_data()
#data = data[0:10,0:10,0:10]
affine = img.affine
header = img.header
resolution = [x.item() for x in header.get_zooms()]
dimensions = data.shape
qpdmap.setDimensions(dimensions[0], dimensions[1], dimensions[2])
qpdmap.setResolutions(resolution[0], resolution[1], resolution[2])
# input images
qpdmap.setFirstInversionMagnitude(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
data = load_volume(first_inversion[1]).get_data()
qpdmap.setFirstInversionPhase(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
data = load_volume(second_inversion[0]).get_data()
qpdmap.setSecondInversionMagnitude(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
data = load_volume(second_inversion[1]).get_data()
qpdmap.setSecondInversionPhase(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
data = load_volume(t1map).get_data()
qpdmap.setT1mapImage(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
data = load_volume(r2smap).get_data()
qpdmap.setR2smapImage(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
if (b1map!=None):
data = load_volume(b1map).get_data()
qpdmap.setB1mapImage(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
# execute the algorithm
try:
qpdmap.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
pd_data = np.reshape(np.array(qpdmap.getProtonDensityImage(),
dtype=np.float32), dimensions, 'F')
# adapt header max for each image so that correct max is displayed
# and create nifiti objects
header['cal_min'] = np.nanmin(pd_data)
header['cal_max'] = np.nanmax(pd_data)
pd = nb.Nifti1Image(pd_data, affine, header)
if save_data:
save_volume(pd_file, pd)
return {'pd': pd_file}
else:
return {'pd': pd}