#!/usr/bin/env python import os import sys import glob import time import numpy as np import argparse import nibabel as nib import surfa as sf from datetime import timedelta from scipy.ndimage import gaussian_filter from scipy.interpolate import RegularGridInterpolator # set tensorflow logging os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' import tensorflow as tf from tensorflow import keras from tensorflow.keras import backend backend.set_image_data_format('channels_last') # ================================================================================================ # Main Entrypoint # ================================================================================================ def main(): # parse arguments parser = argparse.ArgumentParser(description="Implementation of SynthSR that generates a synthetic 1mm MP-RAGE " "from a scan of any contrast and resolution ", epilog='\n') # input/outputs parser.add_argument("--i", help="Image(s) to super-resolve. Can be a path to an image or to a folder.") parser.add_argument("--o", help="Output(s), i.e. synthetic 1mm MP-RAGE(s). " "Must be a folder if --i designates a folder.") parser.add_argument("--ct", action="store_true", help="(optional) Use this flag for CT scans in Hounsfield scale, " "it clips intensities to [0,80].") parser.add_argument("--disable_sharpening", action="store_true", help="(optional) Use this flag to disable unsharp masking.") parser.add_argument("--disable_flipping", action="store_true", help="(optional) Use this flag to disable flipping augmentation at test time.") # models parser.add_argument("--lowfield", action="store_true", help="(optional) Use model for low-field scans (e.g., acquired with Hyperfine's Swoop scanner).") parser.add_argument("--v1", action="store_true", help="(optional) Use version 1 model from July 2021.") # parameters parser.add_argument("--threads", type=int, default=1, help="(optional) Number of cores to be used. Default is 1.") parser.add_argument("--cpu", action="store_true", help="(optional) Enforce running with CPU rather than GPU.") parser.add_argument("--model", default=None, help="(optional) Use a different model file.") # check for no arguments if len(sys.argv) < 2: parser.print_help() sys.exit(1) # parse commandline args = parser.parse_args() # enforce CPU processing if necessary if args.cpu: print('using CPU, hiding all CUDA_VISIBLE_DEVICES') os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # locate model weights if not os.environ.get('FREESURFER_HOME'): path_model = None sf.system.fatal('FREESURFER_HOME is not set. Please source freesurfer.') elif args.model is None: if args.v1: print('Using version 1 model from July 2021') path_model = os.path.join(os.environ.get('FREESURFER_HOME'), 'models', 'synthsr_v10_210712.h5') elif args.lowfield: print('Using model for low-field scans from January 2023 (version 2)') path_model = os.path.join(os.environ.get('FREESURFER_HOME'), 'models', 'synthsr_lowfield_v20_230130.h5') else: print('Using general model from January 2023 (version 2)') path_model = os.path.join(os.environ.get('FREESURFER_HOME'), 'models', 'synthsr_v20_230130.h5') else: print('Using user-specified model: ' + args.model) path_model = args.model print(path_model) # run prediction predict( path_images=args.i, path_predictions=args.o, path_model=path_model, ct_mode=args.ct, disable_sharpening=args.disable_sharpening, disable_flipping=args.disable_flipping, threads=args.threads ) # ================================================================================================ # Prediction and Processing Utilities # ================================================================================================ def predict(path_images, path_predictions, path_model, ct_mode, disable_sharpening, disable_flipping, threads=1): ''' Prediction pipeline. ''' if path_model is None: sf.system.fatal("A model file is necessary") # prepare input/output filepaths path_images, path_predictions = prepare_output_files(path_images, path_predictions) if threads == 1: print('using 1 thread') else: print('using %s threads' % threads) tf.config.threading.set_inter_op_parallelism_threads(threads) tf.config.threading.set_intra_op_parallelism_threads(threads) # build network net = build_model(path_model) # perform SR/synthesis loop_info = LoopInfo(len(path_images), 10, 'predicting', True) for idx, (path_image, path_prediction) in enumerate(zip(path_images, path_predictions)): loop_info.update(idx) # preprocessing try: image, aff, h, pad_idx = preprocess(path_image, ct_mode) except Exception as e: print('\nthe following problem occured when preprocessing image %s :' % path_image) print(e) print('resuming program execution\n') continue # prediction try: if disable_flipping: pred = np.clip(255 * np.squeeze(net.predict(image)), 0, 128) else: print('Prediction without flipping') pred1 = np.squeeze(net.predict(image)) print('Prediction with flipping') pred2 = np.flip(np.squeeze(net.predict(np.flip(image, axis=1))), axis=0) pred = 0.5 * np.clip(255 * pred1, 0, 128) + 0.5 * np.clip(255 * pred2, 0, 128) except Exception as e: print('\nthe following problem occured when predicting output for image %s :' % path_image) print(e) print('\nresuming program execution') continue # postprocessing try: pred = postprocess(pred, pad_idx, aff, disable_sharpening) except Exception as e: print('\nthe following problem occured when postprocessing prediction for image %s :' % path_image) print(e) print('\nresuming program execution') continue # write results to disk try: save_volume(pred, aff, h, path_prediction) except Exception as e: print('\nthe following problem occured when saving the result for image %s :' % path_image) print(e) print('\nresuming program execution') continue # print output info if len(path_predictions) == 1: print('\nprediction saved in: ' + path_predictions[0]) else: print('\npredictions saved in: ' + os.path.dirname(path_predictions[0])) print('\nIf you use this tool in a publication, please cite:') print('\n') print('Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical ') print('MRI exams with scans of different orientation, resolution and contrast') print('JE Iglesias, B Billot, Y Balbastre, A Tabari, J Conklin, RG Gonzalez, DC Alexander,') print('P Golland, BL Edlow, B Fischl, for the ADNI') print('NeuroImage, 118206 (2021)\n') print('\n') print('SynthSR: a public AI tool to turn heterogeneous clinical brain scans into ') print('high-resolution T1-weighted images for 3D morphometry') print('JE Iglesias, B Billot, Y Balbastre, C Magdamo, S Arnold, S Das, B Edlow, D Alexander,') print('P Golland, B Fischl') print('Science Advances, 9(5), eadd3607 (2023)\n') print('\n') print('If you use the low-field (Hyperfine) version, please cite also:\n') print('\n') print('Quantitative Brain Morphometry of Portable Low-Field-Strength MRI Using ') print('Super-Resolution Machine Learning') print('JE Iglesias, R Schleicher, S Laguna, B Billot, P Schaefer, B McKaig, JN Goldstein, ') print('KN Sheth, MS Rosen, WT Kimberly') print('Radiology, 220522 (2022)\n') print('\n') def prepare_output_files(path_images, path_predictions): ''' Prepare output files. ''' # check inputs if path_images is None: sf.system.fatal('please specify an input file/folder (--i)') if path_predictions is None: sf.system.fatal('please specify an output file/folder (--o)') # convert path to absolute paths path_images = os.path.abspath(path_images) basename = os.path.basename(path_images) path_predictions = os.path.abspath(path_predictions) if basename[-4:] == '.txt': # input images if not os.path.isfile(path_images): sf.system.fatal('provided text file containing paths of input images does not exist' % path_images) with open(path_images, 'r') as f: path_images = [line.replace('\n', '') for line in f.readlines() if line != '\n'] # predictions if path_predictions[-4:] != '.txt': sf.system.fatal('if path_images given as text file, so must be the output predictions') with open(path_predictions, 'r') as f: path_predictions = [line.replace('\n', '') for line in f.readlines() if line != '\n'] # path_images is a folder elif ('.nii.gz' not in basename) & ('.nii' not in basename) & ('.mgz' not in basename) & ('.npz' not in basename): # input images if os.path.isfile(path_images): sf.system.fatal('Extension not supported for %s, only use: .nii.gz, .nii, .mgz, or .npz' % path_images) path_images = list_images_in_folder(path_images) # predictions if path_predictions[-4:] == '.txt': sf.system.fatal('path_predictions can only be given as text file when path_images is.') if (path_predictions[-7:] == '.nii.gz') | (path_predictions[-4:] == '.nii') | \ (path_predictions[-4:] == '.mgz') | (path_predictions[-4:] == '.npz'): sf.system.fatal('Output folders cannot have extensions: .nii.gz, .nii, .mgz, or .npz, had %s' % path_predictions) mkdir(path_predictions) path_predictions = [os.path.join(path_predictions, os.path.basename(image)).replace('.nii', '_synthsr.nii') for image in path_images] path_predictions = [path_pred.replace('.mgz', '_synthsr.mgz') for path_pred in path_predictions] path_predictions = [path_pred.replace('.npz', '_synthsr.npz') for path_pred in path_predictions] # path_images is an image else: # input images if not os.path.isfile(path_images): sf.system.fatal("file does not exist: %s \nplease make sure the path and the extension are correct" % path_images) path_images = [path_images] # predictions if path_predictions[-4:] == '.txt': sf.system.fatal('path_predictions can only be given as text file when path_images is.') if ('.nii.gz' not in path_predictions) & ('.nii' not in path_predictions) & \ ('.mgz' not in path_predictions) & ('.npz' not in path_predictions): mkdir(path_predictions) filename = os.path.basename(path_images[0]).replace('.nii', '_synthsr.nii') filename = filename.replace('.mgz', '_synthsr.mgz') filename = filename.replace('.npz', '_synthsr.npz') path_predictions = os.path.join(path_predictions, filename) else: mkdir(os.path.dirname(path_predictions)) path_predictions = [path_predictions] return path_images, path_predictions def preprocess(path_image, ct_mode, n_levels=5): # read image and corresponding info im, _, aff, n_dims, n_channels, h, _ = get_volume_info(path_image, True) if n_dims < 3: sf.system.fatal('input should have 3 dimensions, had %s' % n_dims) elif n_dims == 4 and n_channels == 1: im = im[..., 0] elif n_dims > 3: sf.system.fatal('input should have 3 dimensions, had %s' % n_dims) elif n_channels > 1: print('WARNING: detected more than 1 channel, only keeping the first channel.') im = im[..., 0] # resample and align image im, aff = resample_volume(im, aff, [1.0, 1.0, 1.0]) im = align_volume_to_ref(im, aff, aff_ref=np.eye(4), n_dims=3) # pad image to shape divisible by 32 padding_shape = (np.ceil(np.array(im.shape[:n_dims]) / 2**n_levels) * 2**n_levels).astype('int') im, pad_idx = pad_volume(im, padding_shape, return_pad_idx=True) # normalise image if ct_mode: im = np.clip(im, 0, 80) im = im - np.min(im) im = im / np.max(im) # add batch and channel axes im = add_axis(im, axis=[0, -1]) return im, aff, h, pad_idx def build_model(model_file): ''' Builds keras unet model. ''' if not os.path.isfile(model_file): sf.system.fatal("The provided model path does not exist.") # build UNet net = unet(nb_features=24, input_shape=[None, None, None, 1], nb_levels=5, conv_size=3, nb_labels=1, feat_mult=2, nb_conv_per_level=2, final_pred_activation='linear', batch_norm=-1) net.load_weights(model_file, by_name=True) return net def postprocess(pred, pad_idx, aff, disable_sharpening): pred = crop_volume_with_idx(pred, pad_idx, n_dims=3) # unsharp masking amount_usm = 1.0 sigma_usm = 1.5 if (sigma_usm>0) and (amount_usm>0) and (disable_sharpening==False): pred = pred + (pred - gaussian_filter(pred, sigma_usm * np.ones(3))) * amount_usm # align prediction back to first orientation pred = align_volume_to_ref(pred, aff=np.eye(4), aff_ref=aff, n_dims=3) return pred # ================================================================================================ # Neurite Utilities - See github.com/adalca/neurite # ================================================================================================ def unet(nb_features, input_shape, nb_levels, conv_size, nb_labels, name='unet', prefix=None, feat_mult=1, pool_size=2, padding='same', dilation_rate_mult=1, activation='elu', skip_n_concatenations=0, use_residuals=False, final_pred_activation='softmax', nb_conv_per_level=1, layer_nb_feats=None, conv_dropout=0, batch_norm=None, input_model=None): """ Unet-style keras model with an overdose of parametrization. Copied with permission from github.com/adalca/neurite. """ # naming model_name = name if prefix is None: prefix = model_name # volume size data ndims = len(input_shape) - 1 if isinstance(pool_size, int): pool_size = (pool_size,) * ndims # get encoding model enc_model = conv_enc(nb_features, input_shape, nb_levels, conv_size, name=model_name, prefix=prefix, feat_mult=feat_mult, pool_size=pool_size, padding=padding, dilation_rate_mult=dilation_rate_mult, activation=activation, use_residuals=use_residuals, nb_conv_per_level=nb_conv_per_level, layer_nb_feats=layer_nb_feats, conv_dropout=conv_dropout, batch_norm=batch_norm, input_model=input_model) # get decoder # use_skip_connections=True makes it a u-net lnf = layer_nb_feats[(nb_levels * nb_conv_per_level):] if layer_nb_feats is not None else None dec_model = conv_dec(nb_features, None, nb_levels, conv_size, nb_labels, name=model_name, prefix=prefix, feat_mult=feat_mult, pool_size=pool_size, use_skip_connections=True, skip_n_concatenations=skip_n_concatenations, padding=padding, dilation_rate_mult=dilation_rate_mult, activation=activation, use_residuals=use_residuals, final_pred_activation=final_pred_activation, nb_conv_per_level=nb_conv_per_level, batch_norm=batch_norm, layer_nb_feats=lnf, conv_dropout=conv_dropout, input_model=enc_model) final_model = dec_model return final_model def conv_enc(nb_features, input_shape, nb_levels, conv_size, name=None, prefix=None, feat_mult=1, pool_size=2, dilation_rate_mult=1, padding='same', activation='elu', layer_nb_feats=None, use_residuals=False, nb_conv_per_level=2, conv_dropout=0, batch_norm=None, input_model=None): """ Fully Convolutional Encoder. Copied with permission from github.com/adalca/neurite. """ # naming model_name = name if prefix is None: prefix = model_name # first layer: input name = '%s_input' % prefix if input_model is None: input_tensor = keras.layers.Input(shape=input_shape, name=name) last_tensor = input_tensor else: input_tensor = input_model.inputs last_tensor = input_model.outputs if isinstance(last_tensor, list): last_tensor = last_tensor[0] # volume size data ndims = len(input_shape) - 1 if isinstance(pool_size, int): pool_size = (pool_size,) * ndims # prepare layers convL = getattr(keras.layers, 'Conv%dD' % ndims) conv_kwargs = {'padding': padding, 'activation': activation, 'data_format': 'channels_last'} maxpool = getattr(keras.layers, 'MaxPooling%dD' % ndims) # down arm: # add nb_levels of conv + ReLu + conv + ReLu. Pool after each of first nb_levels - 1 layers lfidx = 0 # level feature index for level in range(nb_levels): lvl_first_tensor = last_tensor nb_lvl_feats = np.round(nb_features * feat_mult ** level).astype(int) conv_kwargs['dilation_rate'] = dilation_rate_mult ** level for conv in range(nb_conv_per_level): # does several conv per level, max pooling applied at the end if layer_nb_feats is not None: # None or List of all the feature numbers nb_lvl_feats = layer_nb_feats[lfidx] lfidx += 1 name = '%s_conv_downarm_%d_%d' % (prefix, level, conv) if conv < (nb_conv_per_level - 1) or (not use_residuals): last_tensor = convL(nb_lvl_feats, conv_size, **conv_kwargs, name=name)(last_tensor) else: # no activation last_tensor = convL(nb_lvl_feats, conv_size, padding=padding, name=name)(last_tensor) if conv_dropout > 0: # conv dropout along feature space only name = '%s_dropout_downarm_%d_%d' % (prefix, level, conv) noise_shape = [None, *[1] * ndims, nb_lvl_feats] last_tensor = keras.layers.Dropout(conv_dropout, noise_shape=noise_shape, name=name)(last_tensor) if use_residuals: convarm_layer = last_tensor # the "add" layer is the original input # However, it may not have the right number of features to be added nb_feats_in = lvl_first_tensor.get_shape()[-1] nb_feats_out = convarm_layer.get_shape()[-1] add_layer = lvl_first_tensor if nb_feats_in > 1 and nb_feats_out > 1 and (nb_feats_in != nb_feats_out): name = '%s_expand_down_merge_%d' % (prefix, level) last_tensor = convL(nb_lvl_feats, conv_size, **conv_kwargs, name=name)(lvl_first_tensor) add_layer = last_tensor if conv_dropout > 0: name = '%s_dropout_down_merge_%d_%d' % (prefix, level, conv) noise_shape = [None, *[1] * ndims, nb_lvl_feats] name = '%s_res_down_merge_%d' % (prefix, level) last_tensor = keras.layers.add([add_layer, convarm_layer], name=name) name = '%s_res_down_merge_act_%d' % (prefix, level) last_tensor = keras.layers.Activation(activation, name=name)(last_tensor) if batch_norm is not None: name = '%s_bn_down_%d' % (prefix, level) last_tensor = keras.layers.BatchNormalization(axis=batch_norm, name=name)(last_tensor) # max pool if we're not at the last level if level < (nb_levels - 1): name = '%s_maxpool_%d' % (prefix, level) last_tensor = maxpool(pool_size=pool_size, name=name, padding=padding)(last_tensor) # create the model and return model = keras.Model(inputs=input_tensor, outputs=[last_tensor], name=model_name) return model def conv_dec(nb_features, input_shape, nb_levels, conv_size, nb_labels, name=None, prefix=None, feat_mult=1, pool_size=2, use_skip_connections=False, skip_n_concatenations=0, padding='same', dilation_rate_mult=1, activation='elu', use_residuals=False, final_pred_activation='softmax', nb_conv_per_level=2, layer_nb_feats=None, batch_norm=None, conv_dropout=0, input_model=None): """ Fully Convolutional Decoder. Copied with permission from github.com/adalca/neurite. Parameters: ... use_skip_connections (bool): if true, turns an Enc-Dec to a U-Net. If true, input_tensor and tensors are required. It assumes a particular naming of layers. conv_enc... """ # naming model_name = name if prefix is None: prefix = model_name # if using skip connections, make sure need to use them. if use_skip_connections: assert input_model is not None, "is using skip connections, tensors dictionary is required" # first layer: input input_name = '%s_input' % prefix if input_model is None: input_tensor = keras.layers.Input(shape=input_shape, name=input_name) last_tensor = input_tensor else: input_tensor = input_model.input last_tensor = input_model.output input_shape = last_tensor.shape.as_list()[1:] # vol size info ndims = len(input_shape) - 1 if isinstance(pool_size, int): if ndims > 1: pool_size = (pool_size,) * ndims # prepare layers convL = getattr(keras.layers, 'Conv%dD' % ndims) conv_kwargs = {'padding': padding, 'activation': activation} upsample = getattr(keras.layers, 'UpSampling%dD' % ndims) # up arm: # nb_levels - 1 layers of Deconvolution3D # (approx via up + conv + ReLu) + merge + conv + ReLu + conv + ReLu lfidx = 0 for level in range(nb_levels - 1): nb_lvl_feats = np.round(nb_features * feat_mult ** (nb_levels - 2 - level)).astype(int) conv_kwargs['dilation_rate'] = dilation_rate_mult ** (nb_levels - 2 - level) # upsample matching the max pooling layers size name = '%s_up_%d' % (prefix, nb_levels + level) last_tensor = upsample(size=pool_size, name=name)(last_tensor) up_tensor = last_tensor # merge layers combining previous layer if use_skip_connections & (level < (nb_levels - skip_n_concatenations - 1)): conv_name = '%s_conv_downarm_%d_%d' % (prefix, nb_levels - 2 - level, nb_conv_per_level - 1) cat_tensor = input_model.get_layer(conv_name).output name = '%s_merge_%d' % (prefix, nb_levels + level) last_tensor = keras.layers.concatenate([cat_tensor, last_tensor], axis=ndims + 1, name=name) # convolution layers for conv in range(nb_conv_per_level): if layer_nb_feats is not None: nb_lvl_feats = layer_nb_feats[lfidx] lfidx += 1 name = '%s_conv_uparm_%d_%d' % (prefix, nb_levels + level, conv) if conv < (nb_conv_per_level - 1) or (not use_residuals): last_tensor = convL(nb_lvl_feats, conv_size, **conv_kwargs, name=name)(last_tensor) else: last_tensor = convL(nb_lvl_feats, conv_size, padding=padding, name=name)(last_tensor) if conv_dropout > 0: name = '%s_dropout_uparm_%d_%d' % (prefix, level, conv) noise_shape = [None, *[1] * ndims, nb_lvl_feats] last_tensor = keras.layers.Dropout(conv_dropout, noise_shape=noise_shape, name=name)(last_tensor) # residual block if use_residuals: # the "add" layer is the original input # However, it may not have the right number of features to be added add_layer = up_tensor nb_feats_in = add_layer.get_shape()[-1] nb_feats_out = last_tensor.get_shape()[-1] if nb_feats_in > 1 and nb_feats_out > 1 and (nb_feats_in != nb_feats_out): name = '%s_expand_up_merge_%d' % (prefix, level) add_layer = convL(nb_lvl_feats, conv_size, **conv_kwargs, name=name)(add_layer) if conv_dropout > 0: name = '%s_dropout_up_merge_%d_%d' % (prefix, level, conv) noise_shape = [None, *[1] * ndims, nb_lvl_feats] last_tensor = keras.layers.Dropout(conv_dropout, noise_shape=noise_shape, name=name)(last_tensor) name = '%s_res_up_merge_%d' % (prefix, level) last_tensor = keras.layers.add([last_tensor, add_layer], name=name) name = '%s_res_up_merge_act_%d' % (prefix, level) last_tensor = keras.layers.Activation(activation, name=name)(last_tensor) if batch_norm is not None: name = '%s_bn_up_%d' % (prefix, level) last_tensor = keras.layers.BatchNormalization(axis=batch_norm, name=name)(last_tensor) # Compute likelyhood prediction (no activation yet) name = '%s_likelihood' % prefix last_tensor = convL(nb_labels, 1, activation=None, name=name)(last_tensor) like_tensor = last_tensor # output prediction layer # we use a softmax to compute P(L_x|I) where x is each location if final_pred_activation == 'softmax': name = '%s_prediction' % prefix softmax_lambda_fcn = lambda x: keras.activations.softmax(x, axis=ndims + 1) pred_tensor = keras.layers.Lambda(softmax_lambda_fcn, name=name)(last_tensor) # otherwise create a layer that does nothing. else: name = '%s_prediction' % prefix pred_tensor = keras.layers.Activation('linear', name=name)(like_tensor) # create the model and retun model = keras.Model(inputs=input_tensor, outputs=pred_tensor, name=model_name) return model # ================================================================================================ # Lab2Im Utilities # ================================================================================================ # ---------------------------------------------- loading/saving functions ---------------------------------------------- def load_volume(path_volume, im_only=True, squeeze=True, dtype=None, aff_ref=None): """ Load volume file. :param path_volume: path of the volume to load. Can either be a nii, nii.gz, mgz, or npz format. If npz format, 1) the variable name is assumed to be 'vol_data', 2) the volume is associated with a identity affine matrix and blank header. :param im_only: (optional) if False, the function also returns the affine matrix and header of the volume. :param squeeze: (optional) whether to squeeze the volume when loading. :param dtype: (optional) if not None, convert the loaded volume to this numpy dtype. :param aff_ref: (optional) If not None, the loaded volume is aligned to this affine matrix. The returned affine matrix is also given in this new space. Must be a numpy array of dimension 4x4. :return: the volume, with corresponding affine matrix and header if im_only is False. """ assert path_volume.endswith(('.nii', '.nii.gz', '.mgz', '.npz')), 'Unknown data file: %s' % path_volume if path_volume.endswith(('.nii', '.nii.gz', '.mgz')): x = nib.load(path_volume) if squeeze: volume = np.squeeze(x.get_fdata()) else: volume = x.get_fdata() aff = x.affine header = x.header else: # npz volume = np.load(path_volume)['vol_data'] if squeeze: volume = np.squeeze(volume) aff = np.eye(4) header = nib.Nifti1Header() if dtype is not None: if 'int' in dtype: volume = np.round(volume) volume = volume.astype(dtype=dtype) # align image to reference affine matrix if aff_ref is not None: n_dims, _ = get_dims(list(volume.shape), max_channels=10) volume, aff = align_volume_to_ref(volume, aff, aff_ref=aff_ref, return_aff=True, n_dims=n_dims) if im_only: return volume else: return volume, aff, header def save_volume(volume, aff, header, path, res=None, dtype=None, n_dims=3): """ Save a volume. :param volume: volume to save :param aff: affine matrix of the volume to save. If aff is None, the volume is saved with an identity affine matrix. aff can also be set to 'FS', in which case the volume is saved with the affine matrix of FreeSurfer outputs. :param header: header of the volume to save. If None, the volume is saved with a blank header. :param path: path where to save the volume. :param res: (optional) update the resolution in the header before saving the volume. :param dtype: (optional) numpy dtype for the saved volume. :param n_dims: (optional) number of dimensions, to avoid confusion in multi-channel case. Default is None, where n_dims is automatically inferred. """ mkdir(os.path.dirname(path)) if '.npz' in path: np.savez_compressed(path, vol_data=volume) else: if header is None: header = nib.Nifti1Header() if isinstance(aff, str): if aff == 'FS': aff = np.array([[-1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]) elif aff is None: aff = np.eye(4) nifty = nib.Nifti1Image(volume, aff, header) if dtype is not None: if 'int' in dtype: volume = np.round(volume) volume = volume.astype(dtype=dtype) nifty.set_data_dtype(dtype) if res is not None: if n_dims is None: n_dims, _ = get_dims(volume.shape) res = reformat_to_list(res, length=n_dims, dtype=None) nifty.header.set_zooms(res) nib.save(nifty, path) def get_volume_info(path_volume, return_volume=False, aff_ref=None, max_channels=10): """ Gather information about a volume: shape, affine matrix, number of dimensions and channels, header, and resolution. :param path_volume: path of the volume to get information form. :param return_volume: (optional) whether to return the volume along with the information. :param aff_ref: (optional) If not None, the loaded volume is aligned to this affine matrix. All info relative to the volume is then given in this new space. Must be a numpy array of dimension 4x4. :return: volume (if return_volume is true), and corresponding info. If aff_ref is not None, the returned aff is the original one, i.e. the affine of the image before being aligned to aff_ref. """ # read image im, aff, header = load_volume(path_volume, im_only=False) # understand if image is multichannel im_shape = list(im.shape) n_dims, n_channels = get_dims(im_shape, max_channels=max_channels) im_shape = im_shape[:n_dims] # get labels res if '.nii' in path_volume: data_res = np.array(header['pixdim'][1:n_dims + 1]) elif '.mgz' in path_volume: data_res = np.array(header['delta']) # mgz image else: data_res = np.array([1.0] * n_dims) # align to given affine matrix if aff_ref is not None: ras_axes = get_ras_axes(aff, n_dims=n_dims) ras_axes_ref = get_ras_axes(aff_ref, n_dims=n_dims) im = align_volume_to_ref(im, aff, aff_ref=aff_ref, n_dims=n_dims) im_shape = np.array(im_shape) data_res = np.array(data_res) im_shape[ras_axes_ref] = im_shape[ras_axes] data_res[ras_axes_ref] = data_res[ras_axes] im_shape = im_shape.tolist() # return info if return_volume: return im, im_shape, aff, n_dims, n_channels, header, data_res else: return im_shape, aff, n_dims, n_channels, header, data_res def load_array_if_path(var, load_as_numpy=True): """If var is a string and load_as_numpy is True, this function loads the array writen at the path indicated by var. Otherwise it simply returns var as it is.""" if (isinstance(var, str)) & load_as_numpy: assert os.path.isfile(var), 'No such path: %s' % var var = np.load(var) return var # ----------------------------------------------- reformatting functions ----------------------------------------------- def reformat_to_list(var, length=None, load_as_numpy=False, dtype=None): """This function takes a variable and reformat it into a list of desired length and type (int, float, bool, str). If variable is a string, and load_as_numpy is True, it will be loaded as a numpy array. If variable is None, this funtion returns None. :param var: a str, int, float, list, tuple, or numpy array :param length: (optional) if var is a single item, it will be replicated to a list of this length :param load_as_numpy: (optional) whether var is the path to a numpy array :param dtype: (optional) convert all item to this type. Can be 'int', 'float', 'bool', or 'str' :return: reformated list """ # convert to list if var is None: return None var = load_array_if_path(var, load_as_numpy=load_as_numpy) if isinstance(var, (int, float, np.int8, np.int16, np.int32, np.int64, np.float16, np.float32, np.float64, np.float128)): var = [var] elif isinstance(var, tuple): var = list(var) elif isinstance(var, np.ndarray): if var.shape == (1,): var = [var[0]] else: var = np.squeeze(var).tolist() elif isinstance(var, str): var = [var] elif isinstance(var, bool): var = [var] if isinstance(var, list): if length is not None: if len(var) == 1: var = var * length elif len(var) != length: raise ValueError('if var is a list/tuple/numpy array, it should be of length 1 or {0}, ' 'had {1}'.format(length, var)) else: raise TypeError('var should be an int, float, tuple, list, numpy array, or path to numpy array') # convert items type if dtype is not None: if dtype == 'int': var = [int(v) for v in var] elif dtype == 'float': var = [float(v) for v in var] elif dtype == 'bool': var = [bool(v) for v in var] elif dtype == 'str': var = [str(v) for v in var] else: raise ValueError("dtype should be 'str', 'float', 'int', or 'bool'; had {}".format(dtype)) return var # ----------------------------------------------- path-related functions ----------------------------------------------- def list_images_in_folder(path_dir, include_single_image=True, check_if_empty=True): """List all files with extension nii, nii.gz, mgz, or npz whithin a folder.""" basename = os.path.basename(path_dir) if include_single_image & \ (('.nii.gz' in basename) | ('.nii' in basename) | ('.mgz' in basename) | ('.npz' in basename)): assert os.path.isfile(path_dir), 'file %s does not exist' % path_dir list_images = [path_dir] else: if os.path.isdir(path_dir): list_images = sorted(glob.glob(os.path.join(path_dir, '*nii.gz')) + glob.glob(os.path.join(path_dir, '*nii')) + glob.glob(os.path.join(path_dir, '*.mgz')) + glob.glob(os.path.join(path_dir, '*.npz'))) else: raise Exception('Folder does not exist: %s' % path_dir) if check_if_empty: assert len(list_images) > 0, 'no .nii, .nii.gz, .mgz or .npz image could be found in %s' % path_dir return list_images def mkdir(path_dir): """Recursively creates the current dir as well as its parent folders if they do not already exist.""" if path_dir[-1] == '/': path_dir = path_dir[:-1] if not os.path.isdir(path_dir): list_dir_to_create = [path_dir] while not os.path.isdir(os.path.dirname(list_dir_to_create[-1])): list_dir_to_create.append(os.path.dirname(list_dir_to_create[-1])) for dir_to_create in reversed(list_dir_to_create): os.mkdir(dir_to_create) # ---------------------------------------------- shape-related functions ----------------------------------------------- def get_dims(shape, max_channels=10): """Get the number of dimensions and channels from the shape of an array. The number of dimensions is assumed to be the length of the shape, as long as the shape of the last dimension is inferior or equal to max_channels (default 3). :param shape: shape of an array. Can be a sequence or a 1d numpy array. :param max_channels: maximum possible number of channels. :return: the number of dimensions and channels associated with the provided shape. example 1: get_dims([150, 150, 150], max_channels=10) = (3, 1) example 2: get_dims([150, 150, 150, 3], max_channels=10) = (3, 3) example 3: get_dims([150, 150, 150, 15], max_channels=10) = (4, 1), because 5>3""" if shape[-1] <= max_channels: n_dims = len(shape) - 1 n_channels = shape[-1] else: n_dims = len(shape) n_channels = 1 return n_dims, n_channels def add_axis(x, axis=0): """Add axis to a numpy array. :param axis: index of the new axis to add. Can also be a list of indices to add several axes at the same time.""" axis = reformat_to_list(axis) for ax in axis: x = np.expand_dims(x, axis=ax) return x # --------------------------------------------------- miscellaneous ---------------------------------------------------- class LoopInfo: """ Class to print the current iteration in a for loop, and optionally the estimated remaining time. Instantiate just before the loop, and call the update method at the start of the loop. The printed text has the following format: processing i/total remaining time: hh:mm:ss """ def __init__(self, n_iterations, spacing=10, text='processing', print_time=False): """ :param n_iterations: total number of iterations of the for loop. :param spacing: frequency at which the update info will be printed on screen. :param text: text to print. Default is processing. :param print_time: whether to print the estimated remaining time. Default is False. """ # loop parameters self.n_iterations = n_iterations self.spacing = spacing # text parameters self.text = text self.print_time = print_time self.print_previous_time = False self.align = len(str(self.n_iterations)) * 2 + 1 + 3 # timing parameters self.iteration_durations = np.zeros((n_iterations,)) self.start = time.time() self.previous = time.time() def update(self, idx): # time iteration now = time.time() self.iteration_durations[idx] = now - self.previous self.previous = now # print text if idx == 0: print(self.text + ' 1/{}'.format(self.n_iterations)) elif idx % self.spacing == self.spacing - 1: iteration = str(idx + 1) + '/' + str(self.n_iterations) if self.print_time: # estimate remaining time max_duration = np.max(self.iteration_durations) average_duration = np.mean(self.iteration_durations[self.iteration_durations > .01 * max_duration]) remaining_time = int(average_duration * (self.n_iterations - idx)) # print total remaining time only if it is greater than 1s or if it was previously printed if (remaining_time > 1) | self.print_previous_time: eta = str(timedelta(seconds=remaining_time)) print(self.text + ' {:<{x}} remaining time: {}'.format(iteration, eta, x=self.align)) self.print_previous_time = True else: print(self.text + ' {}'.format(iteration)) else: print(self.text + ' {}'.format(iteration)) # ---------------------------------------------------- edit volume ----------------------------------------------------- def crop_volume_with_idx(volume, crop_idx, aff=None, n_dims=None, return_copy=True): """Crop a volume with given indices. :param volume: a 2d or 3d numpy array :param crop_idx: croppping indices, in the order [lower_bound_dim_1, ..., upper_bound_dim_1, ...]. Can be a list or a 1d numpy array. :param aff: (optional) if aff is specified, this function returns an updated affine matrix of the volume after cropping. :param n_dims: (optional) number of dimensions (excluding channels) of the volume. If not provided, n_dims will be inferred from the input volume. :return: the cropped volume, and the updated affine matrix if aff is not None. """ # get info new_volume = volume.copy() if return_copy else volume n_dims = int(np.array(crop_idx).shape[0] / 2) if n_dims is None else n_dims # crop image if n_dims == 2: new_volume = new_volume[crop_idx[0]:crop_idx[2], crop_idx[1]:crop_idx[3], ...] elif n_dims == 3: new_volume = new_volume[crop_idx[0]:crop_idx[3], crop_idx[1]:crop_idx[4], crop_idx[2]:crop_idx[5], ...] else: raise Exception('cannot crop volumes with more than 3 dimensions') if aff is not None: aff[0:3, -1] = aff[0:3, -1] + aff[:3, :3] @ crop_idx[:3] return new_volume, aff else: return new_volume def pad_volume(volume, padding_shape, padding_value=0, aff=None, return_pad_idx=False): """Pad volume to a given shape :param volume: volume to be padded :param padding_shape: shape to pad volume to. Can be a number, a sequence or a 1d numpy array. :param padding_value: (optional) value used for padding :param aff: (optional) affine matrix of the volume :return: padded volume, and updated affine matrix if aff is not None. """ # get info new_volume = volume.copy() vol_shape = new_volume.shape n_dims, n_channels = get_dims(vol_shape) padding_shape = reformat_to_list(padding_shape, length=n_dims, dtype='int') # check if need to pad if np.any(np.array(padding_shape, dtype='int32') > np.array(vol_shape[:n_dims], dtype='int32')): # get padding margins min_margins = np.maximum(np.int32(np.floor((np.array(padding_shape) - np.array(vol_shape)[:n_dims]) / 2)), 0) max_margins = np.maximum(np.int32(np.ceil((np.array(padding_shape) - np.array(vol_shape)[:n_dims]) / 2)), 0) pad_idx = np.concatenate([min_margins, min_margins + np.array(vol_shape[:n_dims])]) pad_margins = tuple([(min_margins[i], max_margins[i]) for i in range(n_dims)]) if n_channels > 1: pad_margins = tuple(list(pad_margins) + [(0, 0)]) # pad volume new_volume = np.pad(new_volume, pad_margins, mode='constant', constant_values=padding_value) if aff is not None: if n_dims == 2: min_margins = np.append(min_margins, 0) aff[:-1, -1] = aff[:-1, -1] - aff[:-1, :-1] @ min_margins else: pad_idx = np.concatenate([np.array([0] * n_dims), np.array(vol_shape[:n_dims])]) # sort outputs output = [new_volume] if aff is not None: output.append(aff) if return_pad_idx: output.append(pad_idx) return output[0] if len(output) == 1 else tuple(output) def resample_volume(volume, aff, new_vox_size, interpolation='linear'): """This function resizes the voxels of a volume to a new provided size, while adjusting the header to keep the RAS :param volume: a numpy array :param aff: affine matrix of the volume :param new_vox_size: new voxel size (3 - element numpy vector) in mm :return: new volume and affine matrix """ pixdim = np.sqrt(np.sum(aff * aff, axis=0))[:-1] new_vox_size = np.array(new_vox_size) factor = pixdim / new_vox_size sigmas = 0.25 / factor sigmas[factor > 1] = 0 # don't blur if upsampling volume_filt = gaussian_filter(volume, sigmas) # volume2 = zoom(volume_filt, factor, order=1, mode='reflect', prefilter=False) x = np.arange(0, volume_filt.shape[0]) y = np.arange(0, volume_filt.shape[1]) z = np.arange(0, volume_filt.shape[2]) my_interpolating_function = RegularGridInterpolator((x, y, z), volume_filt, method=interpolation) start = - (factor - 1) / (2 * factor) step = 1.0 / factor stop = start + step * np.ceil(volume_filt.shape * factor) xi = np.arange(start=start[0], stop=stop[0], step=step[0]) yi = np.arange(start=start[1], stop=stop[1], step=step[1]) zi = np.arange(start=start[2], stop=stop[2], step=step[2]) xi[xi < 0] = 0 yi[yi < 0] = 0 zi[zi < 0] = 0 xi[xi > (volume_filt.shape[0] - 1)] = volume_filt.shape[0] - 1 yi[yi > (volume_filt.shape[1] - 1)] = volume_filt.shape[1] - 1 zi[zi > (volume_filt.shape[2] - 1)] = volume_filt.shape[2] - 1 xig, yig, zig = np.meshgrid(xi, yi, zi, indexing='ij', sparse=True) volume2 = my_interpolating_function((xig, yig, zig)) aff2 = aff.copy() for c in range(3): aff2[:-1, c] = aff2[:-1, c] / factor[c] aff2[:-1, -1] = aff2[:-1, -1] - np.matmul(aff2[:-1, :-1], 0.5 * (factor - 1)) return volume2, aff2 def get_ras_axes(aff, n_dims=3): """This function finds the RAS axes corresponding to each dimension of a volume, based on its affine matrix. :param aff: affine matrix Can be a 2d numpy array of size n_dims*n_dims, n_dims+1*n_dims+1, or n_dims*n_dims+1. :param n_dims: number of dimensions (excluding channels) of the volume corresponding to the provided affine matrix. :return: two numpy 1d arrays of lengtn n_dims, one with the axes corresponding to RAS orientations, and one with their corresponding direction. """ aff_inverted = np.linalg.inv(aff) img_ras_axes = np.argmax(np.absolute(aff_inverted[0:n_dims, 0:n_dims]), axis=0) for i in range(n_dims): if i not in img_ras_axes: unique, counts = np.unique(img_ras_axes, return_counts=True) incorrect_value = unique[np.argmax(counts)] img_ras_axes[np.where(img_ras_axes == incorrect_value)[0][-1]] = i return img_ras_axes def align_volume_to_ref(volume, aff, aff_ref=None, return_aff=False, n_dims=None, return_copy=True): """This function aligns a volume to a reference orientation (axis and direction) specified by an affine matrix. :param volume: a numpy array :param aff: affine matrix of the floating volume :param aff_ref: (optional) affine matrix of the target orientation. Default is identity matrix. :param return_aff: (optional) whether to return the affine matrix of the aligned volume :param n_dims: (optional) number of dimensions (excluding channels) of the volume. If not provided, n_dims will be inferred from the input volume. :return: aligned volume, with corresponding affine matrix if return_aff is True. """ # work on copy new_volume = volume.copy() if return_copy else volume aff_flo = aff.copy() # default value for aff_ref if aff_ref is None: aff_ref = np.eye(4) # extract ras axes if n_dims is None: n_dims, _ = get_dims(new_volume.shape) ras_axes_ref = get_ras_axes(aff_ref, n_dims=n_dims) ras_axes_flo = get_ras_axes(aff_flo, n_dims=n_dims) # align axes aff_flo[:, ras_axes_ref] = aff_flo[:, ras_axes_flo] for i in range(n_dims): if ras_axes_flo[i] != ras_axes_ref[i]: new_volume = np.swapaxes(new_volume, ras_axes_flo[i], ras_axes_ref[i]) swapped_axis_idx = np.where(ras_axes_flo == ras_axes_ref[i]) ras_axes_flo[swapped_axis_idx], ras_axes_flo[i] = ras_axes_flo[i], ras_axes_flo[swapped_axis_idx] # align directions dot_products = np.sum(aff_flo[:3, :3] * aff_ref[:3, :3], axis=0) for i in range(n_dims): if dot_products[i] < 0: new_volume = np.flip(new_volume, axis=i) aff_flo[:, i] = - aff_flo[:, i] aff_flo[:3, 3] = aff_flo[:3, 3] - aff_flo[:3, i] * (new_volume.shape[i] - 1) if return_aff: return new_volume, aff_flo else: return new_volume # execute script if __name__ == '__main__': main()