Source code for nighres.intensity.mp2rage_t1_mapping

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_t1_mapping(first_inversion, second_inversion, inversion_times, flip_angles, inversion_TR, excitation_TR, N_excitations, efficiency=0.96, correct_B1=False, B1_map=None, scale_phase=True, save_data=False, overwrite=False, output_dir=None, file_name=None): """ MP2RAGE T1 mapping Estimate T1/R1 by a look-up table method adapted from [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 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) B1_map: niimg Computed B1 map 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) * t1 (niimg): Map of estimated T1 times (_qt1map-t1) * r1 (niimg): Map of estimated R1 relaxation rate (_qt1map-r1) * uni (niimg): Estimated PD weighted image at TE=0 (_qt1map-uni) 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('\nT1 Mapping') # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, first_inversion[0]) t1_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=first_inversion[0], suffix='qt1map-t1')) r1_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=first_inversion[0], suffix='qt1map-r1')) uni_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=first_inversion[0], suffix='qt1map-uni')) if overwrite is False \ and os.path.isfile(t1_file) \ and os.path.isfile(r1_file) \ and os.path.isfile(uni_file) : output = {'t1': t1_file, 'r1': r1_file, 'uni': uni_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 qt1map = nighresjava.IntensityMp2rageT1Fitting() # set algorithm parameters qt1map.setFirstInversionTime(inversion_times[0]) qt1map.setSecondInversionTime(inversion_times[1]) qt1map.setFirstFlipAngle(flip_angles[0]) qt1map.setSecondFlipAngle(flip_angles[1]) qt1map.setInversionRepetitionTime(inversion_TR) qt1map.setFirstExcitationRepetitionTime(excitation_TR[0]) qt1map.setSecondExcitationRepetitionTime(excitation_TR[1]) qt1map.setNumberExcitations(N_excitations) qt1map.setInversionEfficiency(efficiency) qt1map.setCorrectB1inhomogeneities(correct_B1) # 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 qt1map.setDimensions(dimensions[0], dimensions[1], dimensions[2]) qt1map.setResolutions(resolution[0], resolution[1], resolution[2]) # input images qt1map.setFirstInversionMagnitude(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) data = load_volume(first_inversion[1]).get_data() qt1map.setFirstInversionPhase(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) data = load_volume(second_inversion[0]).get_data() qt1map.setSecondInversionMagnitude(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) data = load_volume(second_inversion[1]).get_data() qt1map.setSecondInversionPhase(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) if (correct_B1): data = load_volume(B1_map).get_data() qt1map.setB1mapImage(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) # execute the algorithm try: qt1map.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 t1_data = np.reshape(np.array(qt1map.getQuantitativeT1mapImage(), dtype=np.float32), dimensions, 'F') r1_data = np.reshape(np.array(qt1map.getQuantitativeR1mapImage(), dtype=np.float32), dimensions, 'F') uni_data = np.reshape(np.array(qt1map.getUniformT1weightedImage(), 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(t1_data) header['cal_max'] = np.nanmax(t1_data) t1 = nb.Nifti1Image(t1_data, affine, header) header['cal_min'] = np.nanmin(r1_data) header['cal_max'] = np.nanmax(r1_data) r1 = nb.Nifti1Image(r1_data, affine, header) header['cal_min'] = np.nanmin(uni_data) header['cal_max'] = np.nanmax(uni_data) uni = nb.Nifti1Image(uni_data, affine, header) if save_data: save_volume(t1_file, t1) save_volume(r1_file, r1) save_volume(uni_file, uni) return {'t1': t1_file, 'r1': r1_file, 'uni': uni_file} else: return {'t1': t1, 'r1': r1, 'uni': uni}