Source code for mriqc.interfaces.qc

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
# pylint: disable=no-member
#
# @Author: oesteban
# @Date:   2016-01-05 11:29:40
# @Email:  code@oscaresteban.es
# @Last modified by:   oesteban
# @Last Modified time: 2016-02-25 14:16:42
""" Nipype interfaces to quality control measures """

import numpy as np
import nibabel as nb
from ..qc.anatomical import (snr, cnr, fber, efc, artifacts,
                             volume_fraction, rpve, summary_stats)
from ..qc.functional import (gsr, dvars, fd_jenkinson, gcor)
from nipype.interfaces.base import (BaseInterface, traits, TraitedSpec, File,
                                    InputMultiPath, BaseInterfaceInputSpec)

from nipype import logging
IFLOGGER = logging.getLogger('interface')

[docs]class StructuralQCInputSpec(BaseInterfaceInputSpec): in_file = File(exists=True, mandatory=True, desc='File to be plotted') in_segm = File(exists=True, mandatory=True, desc='segmentation file from FSL FAST') in_bias = File(exists=True, mandatory=True, desc='bias file') in_pvms = InputMultiPath(File(exists=True), mandatory=True, desc='partial volume maps from FSL FAST') in_tpms = InputMultiPath(File(), desc='tissue probability maps from FSL FAST')
[docs]class StructuralQCOutputSpec(TraitedSpec): summary = traits.Dict(desc='summary statistics per tissue') icvs = traits.Dict(desc='intracranial volume (ICV) fractions') rpve = traits.Dict(desc='partial volume fractions') size = traits.Dict(desc='image sizes') spacing = traits.Dict(desc='image sizes') bias = traits.Dict(desc='summary statistics of the bias field') snr = traits.Dict cnr = traits.Float fber = traits.Float efc = traits.Float qi1 = traits.Float out_qc = traits.Dict(desc='output flattened dictionary with all measures')
[docs]class StructuralQC(BaseInterface): """ Computes anatomical :abbr:`QC (Quality Control)` measures on the structural image given as input """ input_spec = StructuralQCInputSpec output_spec = StructuralQCOutputSpec def __init__(self, **inputs): self._results = {} super(StructuralQC, self).__init__(**inputs) def _list_outputs(self): return self._results def _run_interface(self, runtime): imnii = nb.load(self.inputs.in_file) imdata = np.nan_to_num(imnii.get_data()) # Cast to float32 imdata = imdata.astype(np.float32) # Remove negative values imdata[imdata < 0] = 0 segnii = nb.load(self.inputs.in_segm) segdata = segnii.get_data().astype(np.uint8) # SNR snrvals = [] self._results['snr'] = {} for tlabel in ['csf', 'wm', 'gm']: snrvals.append(snr(imdata, segdata, tlabel)) self._results['snr'][tlabel] = snrvals[-1] self._results['snr']['total'] = np.mean(snrvals) # CNR self._results['cnr'] = cnr(imdata, segdata) # FBER self._results['fber'] = fber(imdata, segdata) # EFC self._results['efc'] = efc(imdata) # Artifacts self._results['qi1'] = artifacts(imdata, segdata)[0] pvmdata = [] for fname in self.inputs.in_pvms: pvmdata.append(nb.load(fname).get_data().astype(np.float32)) # ICVs self._results['icvs'] = volume_fraction(pvmdata) # RPVE self._results['rpve'] = rpve(pvmdata, segdata) # Summary stats mean, stdv, p95, p05 = summary_stats(imdata, pvmdata) self._results['summary'] = {'mean': mean, 'stdv': stdv, 'p95': p95, 'p05': p05} # Image specs self._results['size'] = {'x': imdata.shape[0], 'y': imdata.shape[1], 'z': imdata.shape[2]} self._results['spacing'] = { i: v for i, v in zip(['x', 'y', 'z'], imnii.get_header().get_zooms()[:3])} try: self._results['size']['t'] = imdata.shape[3] except IndexError: pass try: self._results['spacing']['tr'] = imnii.get_header().get_zooms()[3] except IndexError: pass # Bias bias = nb.load(self.inputs.in_bias).get_data()[segdata > 0] self._results['bias'] = { 'max': bias.max(), 'min': bias.min(), 'med': np.median(bias)} #pylint: disable=E1101 # Flatten the dictionary self._results['out_qc'] = _flatten_dict(self._results) return runtime
[docs]class FunctionalQCInputSpec(BaseInterfaceInputSpec): in_epi = File(exists=True, mandatory=True, desc='input EPI file') in_hmc = File(exists=True, mandatory=True, desc='input motion corrected file') in_tsnr = File(exists=True, mandatory=True, desc='input tSNR volume') in_mask = File(exists=True, mandatory=True, desc='input mask') direction = traits.Enum('all', 'x', 'y', '-x', '-y', usedefault=True, desc='direction for GSR computation')
[docs]class FunctionalQCOutputSpec(TraitedSpec): fber = traits.Float efc = traits.Float snr = traits.Float gsr = traits.Dict m_tsnr = traits.Float dvars = traits.Float gcor = traits.Float size = traits.Dict spacing = traits.Dict summary = traits.Dict out_qc = traits.Dict(desc='output flattened dictionary with all measures')
[docs]class FunctionalQC(BaseInterface): """ Computes anatomical :abbr:`QC (Quality Control)` measures on the structural image given as input """ input_spec = FunctionalQCInputSpec output_spec = FunctionalQCOutputSpec def __init__(self, **inputs): self._results = {} super(FunctionalQC, self).__init__(**inputs) def _list_outputs(self): return self._results def _run_interface(self, runtime): # Get the mean EPI data and get it ready epinii = nb.load(self.inputs.in_epi) epidata = np.nan_to_num(epinii.get_data()) epidata = epidata.astype(np.float32) epidata[epidata < 0] = 0 # Get EPI data (with mc done) and get it ready hmcnii = nb.load(self.inputs.in_hmc) hmcdata = np.nan_to_num(hmcnii.get_data()) hmcdata = hmcdata.astype(np.float32) hmcdata[hmcdata < 0] = 0 # Get EPI data (with mc done) and get it ready msknii = nb.load(self.inputs.in_mask) mskdata = np.nan_to_num(msknii.get_data()) mskdata = mskdata.astype(np.uint8) mskdata[mskdata < 0] = 0 mskdata[mskdata > 0] = 1 # SNR self._results['snr'] = snr(epidata, mskdata, 1) # FBER self._results['fber'] = fber(epidata, mskdata) # EFC self._results['efc'] = efc(epidata) # GSR self._results['gsr'] = {} if self.inputs.direction == 'all': epidir = ['x', 'y'] else: epidir = [self.inputs.direction] for axis in epidir: self._results['gsr'][axis] = gsr(epidata, mskdata, direction=axis) # Summary stats mean, stdv, p95, p05 = summary_stats(epidata, mskdata) self._results['summary'] = {'mean': mean, 'stdv': stdv, 'p95': p95, 'p05': p05} # DVARS self._results['dvars'] = dvars(hmcdata, mskdata).mean(axis=0)[0] # tSNR tsnr_data = nb.load(self.inputs.in_tsnr).get_data() self._results['m_tsnr'] = np.median(tsnr_data[mskdata > 0]) # GCOR self._results['gcor'] = gcor(hmcdata, mskdata) # Image specs self._results['size'] = {'x': hmcdata.shape[0], 'y': hmcdata.shape[1], 'z': hmcdata.shape[2]} self._results['spacing'] = { i: v for i, v in zip(['x', 'y', 'z'], hmcnii.get_header().get_zooms()[:3])} try: self._results['size']['t'] = hmcdata.shape[3] except IndexError: pass try: self._results['spacing']['tr'] = hmcnii.get_header().get_zooms()[3] except IndexError: pass self._results['out_qc'] = _flatten_dict(self._results) return runtime
def _flatten_dict(indict): out_qc = {} for k, value in list(indict.items()): if not isinstance(value, dict): out_qc[k] = value else: for subk, subval in list(value.items()): if not isinstance(subval, dict): out_qc['%s_%s' % (k, subk)] = subval else: for ssubk, ssubval in list(subval.items()): out_qc['%s_%s_%s' % (k, subk, ssubk)] = ssubval return out_qc
[docs]class FramewiseDisplacementInputSpec(BaseInterfaceInputSpec): in_file = File(exists=True, mandatory=True, desc='input file generated with FSL 3dvolreg') rmax = traits.Float(80., usedefault=True, desc='default brain radius') threshold = traits.Float(1., usedefault=True, desc='motion threshold')
[docs]class FramewiseDisplacementOutputSpec(TraitedSpec): out_file = File(desc='output file') fd_stats = traits.Dict
[docs]class FramewiseDisplacement(BaseInterface): """ Computes anatomical :abbr:`QC (Quality Control)` measures on the structural image given as input """ input_spec = FramewiseDisplacementInputSpec output_spec = FramewiseDisplacementOutputSpec def __init__(self, **inputs): self._results = {} super(FramewiseDisplacement, self).__init__(**inputs) def _list_outputs(self): return self._results def _run_interface(self, runtime): out_file = fd_jenkinson(self.inputs.in_file, self.inputs.rmax) self._results['out_file'] = out_file fddata = np.loadtxt(out_file) num_fd = np.float((fddata > self.inputs.threshold).sum()) self._results['fd_stats'] = { 'mean_fd': fddata.mean(), 'num_fd': num_fd, 'perc_fd': num_fd * 100 / (len(fddata) + 1) } return runtime