Source code for cortex.xfm

"""Affine transformation class
"""
import os
import numpy as np
import subprocess

[docs] class Transform(object): ''' A standard affine transform. Typically holds a transform from anatomical magnet space to epi file space. '''
[docs] def __init__(self, xfm, reference): self.xfm = xfm self.reference = None if isstr(reference): import nibabel try: self.reference = nibabel.load(reference) self.shape = self.reference.shape[:3][::-1] except IOError: self.reference = reference elif isinstance(reference, tuple): self.shape = reference else: self.reference = reference self.shape = self.reference.shape[:3][::-1]
def __call__(self, pts): return np.dot(self.xfm, np.hstack([pts, np.ones((len(pts),1))]).T)[:3].T @property def inv(self): ref = self.reference if ref is None: ref = self.shape return Transform(np.linalg.inv(self.xfm), ref) def __mul__(self, other): ref = self.reference if ref is None: ref = self.shape if isinstance(other, Transform): other = other.xfm return Transform(np.dot(self.xfm, other), ref) def __rmul__(self, other): ref = self.reference if ref is None: ref = self.shape if isinstance(other, Transform): other = other.xfm return Transform(np.dot(other, self.xfm), ref) def __repr__(self): try: path, fname = os.path.split(self.reference.get_filename()) return "<Transform into %s space>"%fname except AttributeError: return "<Reference free affine transform>" def save(self, subject, name, xfmtype="magnet"): if self.reference is None: raise ValueError('Cannot save reference-free transforms into the database') from .database import db db.save_xfm(subject, name, self.xfm, xfmtype=xfmtype, reference=self.reference.get_filename()) @classmethod def from_fsl(cls, xfm, func_nii, anat_nii): """Converts an fsl transform to a pycortex transform. Converts a transform computed using FSL's FLIRT to a transform ("xfm") object in pycortex. The transform must have been computed FROM the nifti volume specified in `func_nii` TO the volume specified in `anat_nii` (See Notes below). Parameters ---------- xfm : array 4x4 transformation matrix, loaded from an FSL .mat file, for a transform computed FROM the func_nii volume TO the anat_nii volume. Alternatively, a string file name for the FSL .mat file. anat_nii : str or nibabel.Nifti1Image nibabel image object (or path to nibabel-readable image) for anatomical volume from which cortical surface was created func_nii : str or nibabel.Nifti1Image nibabel image object (or string path to nibabel-readable image) for (functional) data volume to be projected onto cortical surface Returns ------- xfm : cortex.xfm.Transform object A pycortex COORD transform. Notes ----- The transform is assumed to be computed FROM the functional data TO the anatomical data. In FSL speak, that means that the arguments to flirt should have been: flirt -in <func_nii> -ref <anat_nii> ... """ ## -- Adapted from dipy.external.fsl.flirt2aff -- ## import nibabel import numpy.linalg as npl inv = npl.inv # Load transform from text file, if string is provided if isinstance(xfm, str): with open(xfm, 'r') as fid: L = fid.readlines() xfm = np.array([[np.float_(s) for s in ll.split() if s] for ll in L]) # Internally, pycortex computes the OPPOSITE transform: from anatomical volume to functional volume. # Thus, assign anat to "infile" (starting point for transform) infile = anat_nii # Assign func to "reffile" (end point for transform) reffile = func_nii # and invert the usual direction (change from func>anat to anat>func) xfm = inv(xfm) try: inIm = nibabel.load(infile) except AttributeError: inIm = infile refIm = nibabel.load(reffile) in_hdr = inIm.header ref_hdr = refIm.header # get_zooms gets the positive voxel sizes as returned in the header inspace = np.diag(in_hdr.get_zooms()[:3] + (1,)) refspace = np.diag(ref_hdr.get_zooms()[:3] + (1,)) # Since FSL does not use the full transform info in the nifti header, # determine whether the transform indicates that the X axis should be # flipped; if so, flip the X axis (for both infile and reffile) if npl.det(in_hdr.get_best_affine())>=0: inspace = np.dot(inspace, _x_flipper(in_hdr.get_data_shape()[0])) if npl.det(ref_hdr.get_best_affine())>=0: refspace = np.dot(refspace, _x_flipper(ref_hdr.get_data_shape()[0])) inAffine = inIm.affine coord = np.dot(inv(refspace),np.dot(xfm,np.dot(inspace,inv(inAffine)))) return cls(coord, refIm) def to_fsl(self, anat_nii, direction='func>anat'): """Converts a pycortex transform to an FSL transform. Uses the stored "reference" file provided when the transform was created (usually a functional data or statistical volume) and the supplied anatomical file to create an FSL transform. By default, returns the transform FROM the reference volume (usually the functional data volume) to the anatomical volume (`anat_nii` input). Parameters ---------- anat_nii : str or nibabel.Nifti1Image nibabel image object (or path to nibabel-readable image) for anatomical volume from which cortical surface was created direction : str, optional {'func>anat', 'anat>func'} Direction of transform to return. Defaults to 'func>anat' Notes ----- This function will only work for "coord" transform objects, (those retrieved with cortex.db.get_xfm(xfmtype='coord',...)). It will fail hard for "magnet" transforms! """ import nibabel import numpy.linalg as npl inv = npl.inv ## -- Internal notes -- ## # pycortex transforms are internally stored as anatomical space -> functional data space # transforms. Thus the anatomical file is the "infile" in FSL-speak. infile = anat_nii try: inIm = nibabel.load(infile) except AttributeError: inIm = infile in_hdr = inIm.header ref_hdr = self.reference.header # get_zooms gets the positive voxel sizes as returned in the header inspace = np.diag(in_hdr.get_zooms()[:3] + (1,)) refspace = np.diag(ref_hdr.get_zooms()[:3] + (1,)) # Since FSL does not use the full transform info in the nifti header, # determine whether the transform indicates that the X axis should be # flipped; if so, flip the X axis (for both infile and reffile) if npl.det(in_hdr.get_best_affine())>=0: print("Determinant is > 0: FLIPPING!") inspace = np.dot(inspace, _x_flipper(in_hdr.get_data_shape()[0])) if npl.det(ref_hdr.get_best_affine())>=0: print("Determinant is > 0: FLIPPING!") refspace = np.dot(refspace, _x_flipper(ref_hdr.get_data_shape()[0])) inAffine = inIm.affine fslx = np.dot(refspace,np.dot(self.xfm,np.dot(inAffine,inv(inspace)))) if direction=='func>anat': return inv(fslx) elif direction=='anat>func': return fslx @classmethod def from_freesurfer(cls, fs_register, func_nii, subject, freesurfer_subject_dir=None): """Converts a FreeSurfer transform to a pycortex transform. Converts a transform computed using FreeSurfer alignment tools (e.g., bbregister) to a transform ("xfm") object in pycortex. The transform must have been computed FROM the nifti volume specified in `func_nii` TO the anatomical volume of the FreeSurfer subject `subject` (See Notes below). Parameters ---------- fs_register : array 4x4 transformation matrix, described in an FreeSurfer .dat or .lta file, for a transform computed FROM the func_nii volume TO the anatomical volume of the FreeSurfer subject `subject`. Alternatively, a string file name for the FreeSurfer .dat or .lta file. func_nii : str or nibabel.Nifti1Image nibabel image object (or string path to nibabel-readable image) for (functional) data volume to be projected onto cortical surface subject : str FreeSurfer subject name for which the anatomical volume was registered for. freesurfer_subject_dir : str | None Directory of FreeSurfer subjects. Defaults to the value for the environment variable 'SUBJECTS_DIR' (which should be set by freesurfer) Returns ------- xfm : cortex.xfm.Transform object A pycortex COORD transform. Notes ----- The transform is assumed to be computed FROM the functional data TO the anatomical data of the specified FreeSurfer subject. In FreeSurfer speak, that means that the arguments to FreeSurfer alignment tools should have been: bbregister --s <subject> --mov <func_nii> --reg <fs_register> ... """ import subprocess import nibabel import numpy.linalg as npl inv = npl.inv # Load anatomical to functional transform from register.dat file, if string is provided if isinstance(fs_register, str): with open(fs_register, 'r') as fid: L = fid.readlines() anat2func = np.array([[np.float_(s) for s in ll.split() if s] for ll in L[4:8]]) else: anat2func = fs_register # Set FreeSurfer subject directory if freesurfer_subject_dir is None: freesurfer_subject_dir = os.environ['SUBJECTS_DIR'] # Set path to the anatomical volume used to compute fs_register anat_mgz = os.path.join(freesurfer_subject_dir, subject, 'mri', 'orig.mgz') # Read vox2ras transform for the anatomical volume try: cmd = ('mri_info', '--vox2ras', anat_mgz) L = decode(subprocess.check_output(cmd)).splitlines() anat_vox2ras = np.array([[np.float_(s) for s in ll.split() if s] for ll in L]) except OSError: print ("Error occurred while executing:\n{}".format(' '.join(cmd))) raise # Read tkrvox2ras transform for the anatomical volume anat_tkrvox2ras = _vox2ras_tkr(anat_mgz) # Read tkvox2ras transform for the functional volume func_tkrvox2ras = _vox2ras_tkr(func_nii) # Calculate pycorex transform (i.e. scanner to functional transform) coord = np.dot(inv(func_tkrvox2ras), np.dot(anat2func, np.dot(anat_tkrvox2ras, inv(anat_vox2ras)))) try: refIm = nibabel.load(func_nii) except AttributeError: refIm = func_nii return cls(coord, refIm) def to_freesurfer(self, fs_register, subject, freesurfer_subject_dir=None): """Converts a pycortex transform to a FreeSurfer transform. Converts a transform stored in pycortex xfm object to the FreeSurfer format (i.e., register.dat format: https://surfer.nmr.mgh.harvard.edu/fswiki/RegisterDat) Parameters ---------- fs_register : str Output path for the FreeSurfer formatted transform to be output. subject : str FreeSurfer subject name from which the pycortex subject was imported freesurfer_subject_dir : str | None Directory of FreeSurfer subjects. If None, defaults to the value for the environment variable 'SUBJECTS_DIR' (which should be set by freesurfer) """ import tempfile import subprocess import nibabel import numpy.linalg as npl from .database import db inv = npl.inv # Set path to the anatomical volume for the FreeSurfer subject anat = db.get_anat(subject, type='raw') # Read vox2ras transform for the anatomical volume anat_vox2ras = anat.affine # Read tkrvox2ras transform for the anatomical volume anat_tkrvox2ras = _vox2ras_tkr(anat.get_filename()) # Read tkvox2ras transform for the functional volume func_tkrvox2ras = _vox2ras_tkr(self.reference.get_filename()) # Read voxel resolution of the functional volume func_voxres = self.reference.header.get_zooms() # Calculate FreeSurfer transform fs_anat2func = np.dot(func_tkrvox2ras, np.dot(self.xfm, np.dot(anat_vox2ras, inv(anat_tkrvox2ras)))) # Write out to `fs_register` in register.dat format with open(fs_register, 'w') as fid: fid.write('{}\n'.format(subject)) fid.write('{:.6f}\n'.format(func_voxres[0])) fid.write('{:.6f}\n'.format(func_voxres[1])) fid.write('0.150000\n') for row in fs_anat2func: fid.write(' '.join(['{:.15e}'.format(x) for x in row]) + '\n') print('Wrote:') subprocess.call(('cat', fs_register)) return fs_anat2func
def isstr(obj): """Check for stringy-ness in python 2.7 or 3""" try: return isinstance(obj, basestring) except NameError: return isinstance(obj, str) def decode(obj): if isinstance(obj, bytes): obj = obj.decode() return obj def _x_flipper(N_i): #Copied from dipy flipr = np.diag([-1, 1, 1, 1]) flipr[0,3] = N_i - 1 return flipr def _vox2ras_tkr(image): """Run `mri_info --vox2ras-tkr` on `image` and return a numpy array with the output affine""" try: cmd = ('mri_info', '--vox2ras-tkr', image) L = decode(subprocess.check_output(cmd)).splitlines() # Skip headers/additional information. Example output of # mri_info --vox2ras-tkr # # niiRead(): NIFTI_UNITS_UNKNOWN, assuming mm # -2.61900 0.00000 0.00000 81.18900 # 0.00000 0.00000 2.60000 -63.70000 # 0.00000 -2.61900 0.00000 87.73650 # 0.00000 0.00000 0.00000 1.00000 # # Just take the last 4 lines because the length of the extra info is # unpredictable. L = L[-4:] tkrvox2ras = np.array( [[np.float_(s) for s in ll.split() if s] for ll in L]) except OSError as e: print("Error occurred while executing:\n{}".format(' '.join(cmd))) raise e return tkrvox2ras