def resample_data_resolution(dir_src, dir_out, verbose=False): fmask = pjoin(dir_src, 'nodif_brain_mask.nii.gz') fdwi = pjoin(dir_out, 'data_' + par_b_tag + '.nii.gz') data, affine = load_nifti(fdwi, verbose) mask, _ = load_nifti(fmask, verbose) data2, affine2 = reslice(data, affine, (1.25,) * 3, (par_dim_vox,) * 3) mask2, _ = reslice(mask, affine, (1.25,) * 3, (par_dim_vox,) * 3, order=0) fname = 'data_' + par_b_tag + '_' + par_dim_tag + '.nii.gz' save_nifti(pjoin(dir_out, fname), data2, affine2) fname = 'nodif_brain_mask_' + par_dim_tag + '.nii.gz' save_nifti(pjoin(dir_out, fname), mask2, affine2) fwmparc = pjoin(dir_src, '../wmparc.nii.gz') data, affine = load_nifti(fwmparc, verbose) data2, affine2 = reslice(data, affine, (0.7,) * 3, (par_dim_vox,) * 3, order=0) fname = 'wmparc_' + par_dim_tag + '.nii.gz' save_nifti(pjoin(dir_out, fname), data2, affine2) ft1w = pjoin(dir_src, '../T1w_acpc_dc_restore_brain.nii.gz') data, affine = load_nifti(ft1w, verbose) data2, affine2 = reslice(data, affine, (0.7,) * 3, (par_dim_vox,) * 3, order=0, mode='constant') fname = 't1w_acpc_dc_restore_' + par_dim_tag + '.nii.gz' save_nifti(pjoin(dir_out, fname), data2, affine2)
def resample(img, pixdim=1.5, ref_file=None): d = img.get_data().astype(np.float64) # option to align to reference volume if ref_file!=None: # NOT WORKING! I don't think the dipy registration routine is applying the affine. ref = nb.load(ref_file) mn = nb.Nifti1Image(d.mean(axis=3), img.get_affine()) reg = registration.HistogramRegistration(mn, ref, interp='tri') T = reg.optimize('rigid') resamp_xform = np.dot(img.get_affine(), T.inv().as_affine()) else: resamp_xform = img.get_affine() try: from dipy.align.aniso2iso import reslice except: from dipy.align.aniso2iso import resample as reslice data,xform = reslice(d, resamp_xform, img.get_header().get_zooms()[:3], [pixdim]*3, order=5) return nb.Nifti1Image(data, xform)
def resample(): # extract resampling factor sct.printv('\nParse resampling factor...', param.verbose) factor_split = param.factor.split('x') factor = [float(factor_split[i]) for i in range(len(factor_split))] # check if it has three values if not len(factor) == 3: sct.printv('\nERROR: factor should have three dimensions. E.g., 2x2x1.\n', 1, 'error') else: fx, fy, fz = [float(factor_split[i]) for i in range(len(factor_split))] # Extract path/file/extension path_data, file_data, ext_data = sct.extract_fname(param.fname_data) path_out, file_out, ext_out = path_data, file_data, ext_data if param.fname_out != '': file_out = sct.extract_fname(param.fname_out)[1] else: file_out.append(param.file_suffix) input_im = Image(param.fname_data) # Get dimensions of data sct.printv('\nGet dimensions of data...', param.verbose) nx, ny, nz, nt, px, py, pz, pt = input_im.dim sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), param.verbose) dim = 4 # by default, will be adjusted later if nt == 1: dim = 3 if nz == 1: dim = 2 #TODO : adapt for 2D too or change description sct.run('ERROR (sct_resample): Dimension of input data is different from 3 or 4. Exit program', param.verbose, 'error') # Calculate new dimensions sct.printv('\nCalculate new dimensions...', param.verbose) nx_new = int(round(nx*fx)) ny_new = int(round(ny*fy)) nz_new = int(round(nz*fz)) px_new = px/fx py_new = py/fy pz_new = pz/fz sct.printv(' ' + str(nx_new) + ' x ' + str(ny_new) + ' x ' + str(nz_new)+ ' x ' + str(nt), param.verbose) zooms = input_im.hdr.get_zooms()[:3] affine = input_im.hdr.get_base_affine() new_zooms = (px_new, py_new, pz_new) if type(param.interpolation) == int: order = param.interpolation elif type(param.interpolation) == str and param.interpolation in param.x_to_order.keys(): order = param.x_to_order[param.interpolation] else: order = 1 sct.printv('WARNING: wrong input for the interpolation. Using default value = trilinear', param.verbose, 'warning') new_data, new_affine = dp_iso.reslice(input_im.data, affine, zooms, new_zooms, mode=param.mode, order=order) new_im = Image(param=new_data) new_im.absolutepath = path_out+file_out+ext_out new_im.path = path_out new_im.file_name = file_out new_im.ext = ext_out zooms_to_set = list(new_zooms) if dim == 4: zooms_to_set.append(nt) new_im.hdr = input_im.hdr new_im.hdr.set_zooms(zooms_to_set) new_im.save() # to view results sct.printv('\nDone! To view results, type:', param.verbose) sct.printv('fslview '+param.fname_out+' &', param.verbose, 'info') print
def resample(): # extract resampling factor sct.printv('\nParse resampling factor...', param.verbose) factor_split = param.factor.split('x') factor = [float(factor_split[i]) for i in range(len(factor_split))] # check if it has three values if not len(factor) == 3: sct.printv( '\nERROR: factor should have three dimensions. E.g., 2x2x1.\n', 1, 'error') else: fx, fy, fz = [float(factor_split[i]) for i in range(len(factor_split))] # Extract path/file/extension path_data, file_data, ext_data = sct.extract_fname(param.fname_data) path_out, file_out, ext_out = path_data, file_data, ext_data if param.fname_out != '': file_out = sct.extract_fname(param.fname_out)[1] else: file_out.append(param.file_suffix) input_im = Image(param.fname_data) # Get dimensions of data sct.printv('\nGet dimensions of data...', param.verbose) nx, ny, nz, nt, px, py, pz, pt = input_im.dim sct.printv( ' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), param.verbose) dim = 4 # by default, will be adjusted later if nt == 1: dim = 3 if nz == 1: dim = 2 #TODO : adapt for 2D too or change description sct.run( 'ERROR (sct_resample): Dimension of input data is different from 3 or 4. Exit program', param.verbose, 'error') # Calculate new dimensions sct.printv('\nCalculate new dimensions...', param.verbose) nx_new = int(round(nx * fx)) ny_new = int(round(ny * fy)) nz_new = int(round(nz * fz)) px_new = px / fx py_new = py / fy pz_new = pz / fz sct.printv( ' ' + str(nx_new) + ' x ' + str(ny_new) + ' x ' + str(nz_new) + ' x ' + str(nt), param.verbose) zooms = input_im.hdr.get_zooms()[:3] affine = input_im.hdr.get_base_affine() new_zooms = (px_new, py_new, pz_new) if type(param.interpolation) == int: order = param.interpolation elif type(param.interpolation ) == str and param.interpolation in param.x_to_order.keys(): order = param.x_to_order[param.interpolation] else: order = 1 sct.printv( 'WARNING: wrong input for the interpolation. Using default value = trilinear', param.verbose, 'warning') new_data, new_affine = dp_iso.reslice(input_im.data, affine, zooms, new_zooms, mode=param.mode, order=order) new_im = Image(param=new_data) new_im.absolutepath = path_out + file_out + ext_out new_im.path = path_out new_im.file_name = file_out new_im.ext = ext_out zooms_to_set = list(new_zooms) if dim == 4: zooms_to_set.append(nt) new_im.hdr = input_im.hdr new_im.hdr.set_zooms(zooms_to_set) new_im.save() # to view results sct.printv('\nDone! To view results, type:', param.verbose) sct.printv('fslview ' + param.fname_out + ' &', param.verbose, 'info') print
def resample(): # extract resampling factor sct.printv('\nParse resampling factor...', param.verbose) new_size_split = param.new_size.split('x') new_size = [float(new_size_split[i]) for i in range(len(new_size_split))] # check if it has three values if not len(new_size) == 3: sct.printv('\nERROR: new size should have three dimensions. E.g., 2x2x1.\n', 1, 'error') else: ns_x, ns_y, ns_z = new_size # Extract path/file/extension path_data, file_data, ext_data = sct.extract_fname(param.fname_data) path_out, file_out, ext_out = '', file_data, ext_data if param.fname_out != '': path_out, file_out, ext_out = sct.extract_fname(param.fname_out) else: file_out += param.file_suffix param.fname_out = path_out+file_out+ext_out input_im = Image(param.fname_data) # Get dimensions of data sct.printv('\nGet dimensions of data...', param.verbose) nx, ny, nz, nt, px, py, pz, pt = input_im.dim sct.printv(' ' + str(px) + ' x ' + str(py) + ' x ' + str(pz)+ ' x ' + str(pt)+'mm', param.verbose) dim = 4 # by default, will be adjusted later if nt == 1: dim = 3 if nz == 1: dim = 2 sct.run('ERROR (sct_resample): Dimension of input data is different from 3 or 4. Exit program', param.verbose, 'error') # Calculate new dimensions sct.printv('\nCalculate new dimensions...', param.verbose) if param.new_size_type == 'factor': px_new = px/ns_x py_new = py/ns_y pz_new = pz/ns_z elif param.new_size_type == 'vox': px_new = px*nx/ns_x py_new = py*ny/ns_y pz_new = pz*nz/ns_z else: px_new = ns_x py_new = ns_y pz_new = ns_z sct.printv(' ' + str(px_new) + ' x ' + str(py_new) + ' x ' + str(pz_new)+ ' x ' + str(pt)+'mm', param.verbose) zooms = (px, py, pz) # input_im.hdr.get_zooms()[:3] affine = input_im.hdr.get_qform() # get_base_affine() new_zooms = (px_new, py_new, pz_new) if type(param.interpolation) == int: order = param.interpolation elif type(param.interpolation) == str and param.interpolation in param.x_to_order.keys(): order = param.x_to_order[param.interpolation] else: order = 1 sct.printv('WARNING: wrong input for the interpolation. Using default value = linear', param.verbose, 'warning') new_data, new_affine = dp_iso.reslice(input_im.data, affine, zooms, new_zooms, mode=param.mode, order=order) new_im = Image(param=new_data) new_im.absolutepath = param.fname_out new_im.path = path_out new_im.file_name = file_out new_im.ext = ext_out zooms_to_set = list(new_zooms) if dim == 4: zooms_to_set.append(nt) new_im.hdr = input_im.hdr new_im.hdr.set_zooms(zooms_to_set) # Set the new sform and qform: new_im.hdr.set_sform(new_affine) new_im.hdr.set_qform(new_affine) new_im.save() # to view results sct.printv('\nDone! To view results, type:', param.verbose) sct.printv('fslview '+param.fname_out+' &', param.verbose, 'info')