def proc_file(infile, opts): # figure out the output filename, and see if it exists basefilename = splitext_addext(os.path.basename(infile))[0] if opts.outdir is not None: # set output path basefilename = os.path.join(opts.outdir, basefilename) # prep a file if opts.compressed: verbose('Using gzip compression') outfilename = basefilename + '.nii.gz' else: outfilename = basefilename + '.nii' if os.path.isfile(outfilename) and not opts.overwrite: raise IOError('Output file "%s" exists, use --overwrite to ' 'overwrite it' % outfilename) # load the PAR header and data scaling = 'dv' if opts.scaling == 'off' else opts.scaling infile = fname_ext_ul_case(infile) pr_img = pr.load(infile, permit_truncated=opts.permit_truncated, scaling=scaling, strict_sort=opts.strict_sort) pr_hdr = pr_img.header affine = pr_hdr.get_affine(origin=opts.origin) slope, intercept = pr_hdr.get_data_scaling(scaling) if opts.scaling != 'off': verbose('Using data scaling "%s"' % opts.scaling) # get original scaling, and decide if we scale in-place or not if opts.scaling == 'off': slope = np.array([1.]) intercept = np.array([0.]) in_data = pr_img.dataobj.get_unscaled() out_dtype = pr_hdr.get_data_dtype() elif not np.any(np.diff(slope)) and not np.any(np.diff(intercept)): # Single scalefactor case slope = slope.ravel()[0] intercept = intercept.ravel()[0] in_data = pr_img.dataobj.get_unscaled() out_dtype = pr_hdr.get_data_dtype() else: # Multi scalefactor case slope = np.array([1.]) intercept = np.array([0.]) in_data = np.array(pr_img.dataobj) out_dtype = np.float64 # Reorient data block to LAS+ if necessary ornt = io_orientation(np.diag([-1, 1, 1, 1]).dot(affine)) if np.all(ornt == [[0, 1], [1, 1], [2, 1]]): # already in LAS+ t_aff = np.eye(4) else: # Not in LAS+ t_aff = inv_ornt_aff(ornt, pr_img.shape) affine = np.dot(affine, t_aff) in_data = apply_orientation(in_data, ornt) bvals, bvecs = pr_hdr.get_bvals_bvecs() if not opts.keep_trace: # discard Philips DTI trace if present if bvecs is not None: bad_mask = np.logical_and(bvals != 0, (bvecs == 0).all(axis=1)) if bad_mask.sum() > 0: pl = 's' if bad_mask.sum() != 1 else '' verbose('Removing %s DTI trace volume%s' % (bad_mask.sum(), pl)) good_mask = ~bad_mask in_data = in_data[..., good_mask] bvals = bvals[good_mask] bvecs = bvecs[good_mask] # Make corresponding NIfTI image nimg = nifti1.Nifti1Image(in_data, affine, pr_hdr) nhdr = nimg.header nhdr.set_data_dtype(out_dtype) nhdr.set_slope_inter(slope, intercept) nhdr.set_sform(affine, code=1) nhdr.set_qform(affine, code=1) if 'parse' in opts.minmax: # need to get the scaled data verbose('Loading (and scaling) the data to determine value range') if opts.minmax[0] == 'parse': nhdr['cal_min'] = in_data.min() * slope + intercept else: nhdr['cal_min'] = float(opts.minmax[0]) if opts.minmax[1] == 'parse': nhdr['cal_max'] = in_data.max() * slope + intercept else: nhdr['cal_max'] = float(opts.minmax[1]) # container for potential NIfTI1 header extensions if opts.store_header: # dump the full PAR header content into an extension with open(infile, 'rb') as fobj: # contents must be bytes hdr_dump = fobj.read() dump_ext = nifti1.Nifti1Extension('comment', hdr_dump) nhdr.extensions.append(dump_ext) verbose('Writing %s' % outfilename) nibabel.save(nimg, outfilename) # write out bvals/bvecs if requested if opts.bvs: if bvals is None and bvecs is None: verbose('No DTI volumes detected, bvals and bvecs not written') elif bvecs is None: verbose('DTI volumes detected, but no diffusion direction info was' 'found. Writing .bvals file only.') with open(basefilename + '.bvals', 'w') as fid: # np.savetxt could do this, but it's just a loop anyway for val in bvals: fid.write('%s ' % val) fid.write('\n') else: verbose('Writing .bvals and .bvecs files') # Transform bvecs with reorientation affine orig2new = npl.inv(t_aff) bv_reorient = from_matvec(to_matvec(orig2new)[0], [0, 0, 0]) bvecs = apply_affine(bv_reorient, bvecs) with open(basefilename + '.bvals', 'w') as fid: # np.savetxt could do this, but it's just a loop anyway for val in bvals: fid.write('%s ' % val) fid.write('\n') with open(basefilename + '.bvecs', 'w') as fid: for row in bvecs.T: for val in row: fid.write('%s ' % val) fid.write('\n') # export data labels varying along the 4th dimensions if requested if opts.vol_info: labels = pr_img.header.get_volume_labels() if len(labels) > 0: vol_keys = list(labels.keys()) with open(basefilename + '.ordering.csv', 'w') as csvfile: csvwriter = csv.writer(csvfile, delimiter=',') csvwriter.writerow(vol_keys) for vals in zip(*[labels[k] for k in vol_keys]): csvwriter.writerow(vals) # write out dwell time if requested if opts.dwell_time: try: dwell_time = calculate_dwell_time(pr_hdr.get_water_fat_shift(), pr_hdr.get_echo_train_length(), opts.field_strength) except MRIError: verbose('No EPI factors, dwell time not written') else: verbose('Writing dwell time (%r sec) calculated assuming %sT ' 'magnet' % (dwell_time, opts.field_strength)) with open(basefilename + '.dwell_time', 'w') as fid: fid.write('%r\n' % dwell_time)
def proc_file(infile, opts): # figure out the output filename, and see if it exists basefilename = splitext_addext(os.path.basename(infile))[0] if opts.outdir is not None: # set output path basefilename = os.path.join(opts.outdir, basefilename) # prep a file if opts.compressed: verbose("Using gzip compression") outfilename = basefilename + ".nii.gz" else: outfilename = basefilename + ".nii" if os.path.isfile(outfilename) and not opts.overwrite: raise IOError('Output file "%s" exists, use --overwrite to ' "overwrite it" % outfilename) # load the PAR header and data scaling = "dv" if opts.scaling == "off" else opts.scaling infile = fname_ext_ul_case(infile) pr_img = pr.load(infile, permit_truncated=opts.permit_truncated, scaling=scaling, strict_sort=opts.strict_sort) pr_hdr = pr_img.header affine = pr_hdr.get_affine(origin=opts.origin) slope, intercept = pr_hdr.get_data_scaling(scaling) if opts.scaling != "off": verbose('Using data scaling "%s"' % opts.scaling) # get original scaling, and decide if we scale in-place or not if opts.scaling == "off": slope = np.array([1.0]) intercept = np.array([0.0]) in_data = pr_img.dataobj.get_unscaled() out_dtype = pr_hdr.get_data_dtype() elif not np.any(np.diff(slope)) and not np.any(np.diff(intercept)): # Single scalefactor case slope = slope.ravel()[0] intercept = intercept.ravel()[0] in_data = pr_img.dataobj.get_unscaled() out_dtype = pr_hdr.get_data_dtype() else: # Multi scalefactor case slope = np.array([1.0]) intercept = np.array([0.0]) in_data = np.array(pr_img.dataobj) out_dtype = np.float64 # Reorient data block to LAS+ if necessary ornt = io_orientation(np.diag([-1, 1, 1, 1]).dot(affine)) if np.all(ornt == [[0, 1], [1, 1], [2, 1]]): # already in LAS+ t_aff = np.eye(4) else: # Not in LAS+ t_aff = inv_ornt_aff(ornt, pr_img.shape) affine = np.dot(affine, t_aff) in_data = apply_orientation(in_data, ornt) bvals, bvecs = pr_hdr.get_bvals_bvecs() if not opts.keep_trace: # discard Philips DTI trace if present if bvecs is not None: bad_mask = np.logical_and(bvals != 0, (bvecs == 0).all(axis=1)) if bad_mask.sum() > 0: pl = "s" if bad_mask.sum() != 1 else "" verbose("Removing %s DTI trace volume%s" % (bad_mask.sum(), pl)) good_mask = ~bad_mask in_data = in_data[..., good_mask] bvals = bvals[good_mask] bvecs = bvecs[good_mask] # Make corresponding NIfTI image nimg = nifti1.Nifti1Image(in_data, affine, pr_hdr) nhdr = nimg.header nhdr.set_data_dtype(out_dtype) nhdr.set_slope_inter(slope, intercept) nhdr.set_sform(affine, code=1) nhdr.set_qform(affine, code=1) if "parse" in opts.minmax: # need to get the scaled data verbose("Loading (and scaling) the data to determine value range") if opts.minmax[0] == "parse": nhdr["cal_min"] = in_data.min() * slope + intercept else: nhdr["cal_min"] = float(opts.minmax[0]) if opts.minmax[1] == "parse": nhdr["cal_max"] = in_data.max() * slope + intercept else: nhdr["cal_max"] = float(opts.minmax[1]) # container for potential NIfTI1 header extensions if opts.store_header: # dump the full PAR header content into an extension with open(infile, "rb") as fobj: # contents must be bytes hdr_dump = fobj.read() dump_ext = nifti1.Nifti1Extension("comment", hdr_dump) nhdr.extensions.append(dump_ext) verbose("Writing %s" % outfilename) nibabel.save(nimg, outfilename) # write out bvals/bvecs if requested if opts.bvs: if bvals is None and bvecs is None: verbose("No DTI volumes detected, bvals and bvecs not written") elif bvecs is None: verbose("DTI volumes detected, but no diffusion direction info was" "found. Writing .bvals file only.") with open(basefilename + ".bvals", "w") as fid: # np.savetxt could do this, but it's just a loop anyway for val in bvals: fid.write("%s " % val) fid.write("\n") else: verbose("Writing .bvals and .bvecs files") # Transform bvecs with reorientation affine orig2new = npl.inv(t_aff) bv_reorient = from_matvec(to_matvec(orig2new)[0], [0, 0, 0]) bvecs = apply_affine(bv_reorient, bvecs) with open(basefilename + ".bvals", "w") as fid: # np.savetxt could do this, but it's just a loop anyway for val in bvals: fid.write("%s " % val) fid.write("\n") with open(basefilename + ".bvecs", "w") as fid: for row in bvecs.T: for val in row: fid.write("%s " % val) fid.write("\n") # export data labels varying along the 4th dimensions if requested if opts.vol_info: labels = pr_img.header.get_volume_labels() if len(labels) > 0: vol_keys = list(labels.keys()) with open(basefilename + ".ordering.csv", "w") as csvfile: csvwriter = csv.writer(csvfile, delimiter=",") csvwriter.writerow(vol_keys) for vals in zip(*[labels[k] for k in vol_keys]): csvwriter.writerow(vals) # write out dwell time if requested if opts.dwell_time: try: dwell_time = calculate_dwell_time( pr_hdr.get_water_fat_shift(), pr_hdr.get_echo_train_length(), opts.field_strength ) except MRIError: verbose("No EPI factors, dwell time not written") else: verbose("Writing dwell time (%r sec) calculated assuming %sT " "magnet" % (dwell_time, opts.field_strength)) with open(basefilename + ".dwell_time", "w") as fid: fid.write("%r\n" % dwell_time)
trans, img_to.shape, order=order) return out_class(data, img_to.affine) def gmean_norm(data): in_data = data > np.mean(data) * 0.8 gmean = np.mean(data[in_data]) return data / gmean if __name__ == '__main__': np.set_printoptions(suppress=True, precision=4) normal_fname = "Phantom_EPI_3mm_tra_SENSE_6_1.PAR" normal_img = parrec.load(normal_fname) normal_data = normal_img.get_data() normal_normed = gmean_norm(normal_data) print("RMS of standard image {:<44}: {}".format( normal_fname, np.sqrt(np.sum(normal_normed ** 2)))) for parfile in glob.glob("*.PAR"): if parfile == normal_fname: continue funny_img = parrec.load(parfile) fixed_img = resample_img2img(normal_img, funny_img) fixed_data = fixed_img.get_data() difference_data = normal_normed - gmean_norm(fixed_data) print('RMS resliced {:<52} : {}'.format(
trans, img_to.shape, order=order) return out_class(data, img_to.affine) def gmean_norm(data): in_data = data > np.mean(data) * 0.8 gmean = np.mean(data[in_data]) return data / gmean if __name__ == '__main__': np.set_printoptions(suppress=True, precision=4) normal_fname = "Phantom_EPI_3mm_tra_SENSE_6_1.PAR" normal_img = parrec.load(normal_fname) normal_data = normal_img.get_data() normal_normed = gmean_norm(normal_data) print("RMS of standard image {:<44}: {}".format( normal_fname, np.sqrt(np.sum(normal_normed**2)))) for parfile in glob.glob("*.PAR"): if parfile == normal_fname: continue funny_img = parrec.load(parfile) fixed_img = resample_img2img(normal_img, funny_img) fixed_data = fixed_img.get_data() difference_data = normal_normed - gmean_norm(fixed_data) print('RMS resliced {:<52} : {}'.format( parfile, np.sqrt(np.sum(difference_data**2))))
def loader(self, fname): return parrec.load(fname)