def test_linear_intensity_correction(self): # Create stack of Lena slices shape_z = 15 # Original stack nda_2D = ph.read_image( os.path.join(self.dir_test_data, "2D_Lena_256.png")) nda_3D = np.tile(nda_2D, (shape_z, 1, 1)).astype('double') stack_sitk = sitk.GetImageFromArray(nda_3D) stack = st.Stack.from_sitk_image( image_sitk=stack_sitk, filename="Lena", slice_thickness=stack_sitk.GetSpacing()[-1], ) # 1) Create linearly corrupted intensity stack nda_3D_corruped = np.zeros_like(nda_3D) for i in range(0, shape_z): nda_3D_corruped[i, :, :] = nda_3D[i, :, :] / (i + 1.) stack_corrupted_sitk = sitk.GetImageFromArray(nda_3D_corruped) stack_corrupted = st.Stack.from_sitk_image( image_sitk=stack_corrupted_sitk, filename="stack_corrupted", slice_thickness=stack_corrupted_sitk.GetSpacing()[-1], ) # stack_corrupted.show_slices() # sitkh.show_stacks([stack, stack_corrupted]) # Ground truth-parameter: ic_values = np.zeros((shape_z, 1)) for i in range(0, shape_z): ic_values[i, :] = (i + 1.) intensity_correction = ic.IntensityCorrection( stack=stack_corrupted, reference=stack, use_individual_slice_correction=True, use_verbose=self.use_verbose) intensity_correction.run_linear_intensity_correction() ic_values_est = intensity_correction.get_intensity_correction_coefficients( ) nda_diff = ic_values - ic_values_est self.assertEqual( np.round(np.linalg.norm(nda_diff), decimals=self.accuracy), 0)
def main(): time_start = ph.start_timing() # Set print options for numpy np.set_printoptions(precision=3) input_parser = InputArgparser( description="Volumetric MRI reconstruction framework to reconstruct " "an isotropic, high-resolution 3D volume from multiple stacks of 2D " "slices with motion correction. The resolution of the computed " "Super-Resolution Reconstruction (SRR) is given by the in-plane " "spacing of the selected target stack. A region of interest can be " "specified by providing a mask for the selected target stack. Only " "this region will then be reconstructed by the SRR algorithm which " "can substantially reduce the computational time.", ) input_parser.add_filenames(required=True) input_parser.add_filenames_masks() input_parser.add_output(required=True) input_parser.add_suffix_mask(default="_mask") input_parser.add_target_stack(default=None) input_parser.add_search_angle(default=45) input_parser.add_multiresolution(default=0) input_parser.add_shrink_factors(default=[3, 2, 1]) input_parser.add_smoothing_sigmas(default=[1.5, 1, 0]) input_parser.add_sigma(default=1) input_parser.add_reconstruction_type(default="TK1L2") input_parser.add_iterations(default=15) input_parser.add_alpha(default=0.015) input_parser.add_alpha_first(default=0.2) input_parser.add_iter_max(default=10) input_parser.add_iter_max_first(default=5) input_parser.add_dilation_radius(default=3) input_parser.add_extra_frame_target(default=10) input_parser.add_bias_field_correction(default=0) input_parser.add_intensity_correction(default=1) input_parser.add_isotropic_resolution(default=1) input_parser.add_log_config(default=1) input_parser.add_subfolder_motion_correction() input_parser.add_write_motion_correction(default=1) input_parser.add_verbose(default=0) input_parser.add_two_step_cycles(default=3) input_parser.add_use_masks_srr(default=0) input_parser.add_boundary_stacks(default=[10, 10, 0]) input_parser.add_metric(default="Correlation") input_parser.add_metric_radius(default=10) input_parser.add_reference() input_parser.add_reference_mask() input_parser.add_outlier_rejection(default=1) input_parser.add_threshold_first(default=0.5) input_parser.add_threshold(default=0.8) input_parser.add_interleave(default=3) input_parser.add_slice_thicknesses(default=None) input_parser.add_viewer(default="itksnap") input_parser.add_v2v_method(default="RegAladin") input_parser.add_argument( "--v2v-robust", "-v2v-robust", action='store_true', help="If given, a more robust volume-to-volume registration step is " "performed, i.e. four rigid registrations are performed using four " "rigid transform initializations based on " "principal component alignment of associated masks." ) input_parser.add_argument( "--s2v-hierarchical", "-s2v-hierarchical", action='store_true', help="If given, a hierarchical approach for the first slice-to-volume " "registration cycle is used, i.e. sub-packages defined by the " "specified interleave (--interleave) are registered until each " "slice is registered independently." ) input_parser.add_argument( "--sda", "-sda", action='store_true', help="If given, the volumetric reconstructions are performed using " "Scattered Data Approximation (Vercauteren et al., 2006). " "'alpha' is considered the final 'sigma' for the " "iterative adjustment. " "Recommended value is, e.g., --alpha 0.8" ) input_parser.add_option( option_string="--transforms-history", type=int, help="Write entire history of applied slice motion correction " "transformations to motion correction output directory", default=0, ) args = input_parser.parse_args() input_parser.print_arguments(args) rejection_measure = "NCC" threshold_v2v = -2 # 0.3 debug = False if args.v2v_method not in V2V_METHOD_OPTIONS: raise ValueError("v2v-method must be in {%s}" % ( ", ".join(V2V_METHOD_OPTIONS))) if np.alltrue([not args.output.endswith(t) for t in ALLOWED_EXTENSIONS]): raise ValueError( "output filename invalid; allowed extensions are: %s" % ", ".join(ALLOWED_EXTENSIONS)) if args.alpha_first < args.alpha and not args.sda: raise ValueError("It must hold alpha-first >= alpha") if args.threshold_first > args.threshold: raise ValueError("It must hold threshold-first <= threshold") dir_output = os.path.dirname(args.output) ph.create_directory(dir_output) if args.log_config: input_parser.log_config(os.path.abspath(__file__)) # --------------------------------Read Data-------------------------------- ph.print_title("Read Data") data_reader = dr.MultipleImagesReader( file_paths=args.filenames, file_paths_masks=args.filenames_masks, suffix_mask=args.suffix_mask, stacks_slice_thicknesses=args.slice_thicknesses, ) if len(args.boundary_stacks) is not 3: raise IOError( "Provide exactly three values for '--boundary-stacks' to define " "cropping in i-, j-, and k-dimension of the input stacks") data_reader.read_data() stacks = data_reader.get_data() ph.print_info("%d input stacks read for further processing" % len(stacks)) if all(s.is_unity_mask() is True for s in stacks): ph.print_warning("No mask is provided! " "Generated reconstruction space may be very big!") ph.print_warning("Consider using a mask to speed up computations") # args.extra_frame_target = 0 # ph.wrint_warning("Overwritten: extra-frame-target set to 0") # Specify target stack for intensity correction and reconstruction space if args.target_stack is None: target_stack_index = 0 else: try: target_stack_index = args.filenames.index(args.target_stack) except ValueError as e: raise ValueError( "--target-stack must correspond to an image as provided by " "--filenames") # ---------------------------Data Preprocessing--------------------------- ph.print_title("Data Preprocessing") segmentation_propagator = segprop.SegmentationPropagation( # registration_method=regflirt.FLIRT(use_verbose=args.verbose), # registration_method=niftyreg.RegAladin(use_verbose=False), dilation_radius=args.dilation_radius, dilation_kernel="Ball", ) data_preprocessing = dp.DataPreprocessing( stacks=stacks, segmentation_propagator=segmentation_propagator, use_cropping_to_mask=True, use_N4BiasFieldCorrector=args.bias_field_correction, target_stack_index=target_stack_index, boundary_i=args.boundary_stacks[0], boundary_j=args.boundary_stacks[1], boundary_k=args.boundary_stacks[2], unit="mm", ) data_preprocessing.run() time_data_preprocessing = data_preprocessing.get_computational_time() # Get preprocessed stacks stacks = data_preprocessing.get_preprocessed_stacks() # Define reference/target stack for registration and reconstruction if args.reference is not None: reference = st.Stack.from_filename( file_path=args.reference, file_path_mask=args.reference_mask, extract_slices=False) else: reference = st.Stack.from_stack(stacks[target_stack_index]) # ------------------------Volume-to-Volume Registration-------------------- if len(stacks) > 1: if args.v2v_method == "FLIRT": # Define search angle ranges for FLIRT in all three dimensions search_angles = ["-searchr%s -%d %d" % (x, args.search_angle, args.search_angle) for x in ["x", "y", "z"]] options = (" ").join(search_angles) # options += " -noresample" vol_registration = regflirt.FLIRT( registration_type="Rigid", use_fixed_mask=True, use_moving_mask=True, options=options, use_verbose=False, ) else: vol_registration = niftyreg.RegAladin( registration_type="Rigid", use_fixed_mask=True, use_moving_mask=True, # options="-ln 2 -voff", use_verbose=False, ) v2vreg = pipeline.VolumeToVolumeRegistration( stacks=stacks, reference=reference, registration_method=vol_registration, verbose=debug, robust=args.v2v_robust, ) v2vreg.run() stacks = v2vreg.get_stacks() time_registration = v2vreg.get_computational_time() else: time_registration = ph.get_zero_time() # ---------------------------Intensity Correction-------------------------- if args.intensity_correction: ph.print_title("Intensity Correction") intensity_corrector = ic.IntensityCorrection() intensity_corrector.use_individual_slice_correction(False) intensity_corrector.use_reference_mask(True) intensity_corrector.use_stack_mask(True) intensity_corrector.use_verbose(False) for i, stack in enumerate(stacks): if i == target_stack_index: ph.print_info("Stack %d (%s): Reference image. Skipped." % ( i + 1, stack.get_filename())) continue else: ph.print_info("Stack %d (%s): Intensity Correction ... " % ( i + 1, stack.get_filename()), newline=False) intensity_corrector.set_stack(stack) intensity_corrector.set_reference( stacks[target_stack_index].get_resampled_stack( resampling_grid=stack.sitk, interpolator="NearestNeighbor", )) intensity_corrector.run_linear_intensity_correction() stacks[i] = intensity_corrector.get_intensity_corrected_stack() print("done (c1 = %g) " % intensity_corrector.get_intensity_correction_coefficients()) # ---------------------------Create first volume--------------------------- time_tmp = ph.start_timing() # Isotropic resampling to define HR target space ph.print_title("Reconstruction Space Generation") HR_volume = reference.get_isotropically_resampled_stack( resolution=args.isotropic_resolution) ph.print_info( "Isotropic reconstruction space with %g mm resolution is created" % HR_volume.sitk.GetSpacing()[0]) if args.reference is None: # Create joint image mask in target space joint_image_mask_builder = imb.JointImageMaskBuilder( stacks=stacks, target=HR_volume, dilation_radius=1, ) joint_image_mask_builder.run() HR_volume = joint_image_mask_builder.get_stack() ph.print_info( "Isotropic reconstruction space is centered around " "joint stack masks. ") # Crop to space defined by mask (plus extra margin) HR_volume = HR_volume.get_cropped_stack_based_on_mask( boundary_i=args.extra_frame_target, boundary_j=args.extra_frame_target, boundary_k=args.extra_frame_target, unit="mm", ) # Create first volume # If outlier rejection is activated, eliminate obvious outliers early # from stack and re-run SDA to get initial volume without them ph.print_title("First Estimate of HR Volume") if args.outlier_rejection and threshold_v2v > -1: ph.print_subtitle("SDA Approximation") SDA = sda.ScatteredDataApproximation( stacks, HR_volume, sigma=args.sigma) SDA.run() HR_volume = SDA.get_reconstruction() # Identify and reject outliers ph.print_subtitle("Eliminate slice outliers (%s < %g)" % ( rejection_measure, threshold_v2v)) outlier_rejector = outre.OutlierRejector( stacks=stacks, reference=HR_volume, threshold=threshold_v2v, measure=rejection_measure, verbose=True, ) outlier_rejector.run() stacks = outlier_rejector.get_stacks() ph.print_subtitle("SDA Approximation Image") SDA = sda.ScatteredDataApproximation( stacks, HR_volume, sigma=args.sigma) SDA.run() HR_volume = SDA.get_reconstruction() ph.print_subtitle("SDA Approximation Image Mask") SDA = sda.ScatteredDataApproximation( stacks, HR_volume, sigma=args.sigma, sda_mask=True) SDA.run() # HR volume contains updated mask based on SDA HR_volume = SDA.get_reconstruction() HR_volume.set_filename(SDA.get_setting_specific_filename()) time_reconstruction = ph.stop_timing(time_tmp) if args.verbose: tmp = list(stacks) tmp.insert(0, HR_volume) sitkh.show_stacks(tmp, segmentation=HR_volume, viewer=args.viewer) # -----------Two-step Slice-to-Volume Registration-Reconstruction---------- if args.two_step_cycles > 0: # Slice-to-volume registration set-up if args.metric == "ANTSNeighborhoodCorrelation": metric_params = {"radius": args.metric_radius} else: metric_params = None registration = regsitk.SimpleItkRegistration( moving=HR_volume, use_fixed_mask=True, use_moving_mask=True, interpolator="Linear", metric=args.metric, metric_params=metric_params, use_multiresolution_framework=args.multiresolution, shrink_factors=args.shrink_factors, smoothing_sigmas=args.smoothing_sigmas, initializer_type="SelfGEOMETRY", optimizer="ConjugateGradientLineSearch", optimizer_params={ "learningRate": 1, "numberOfIterations": 100, "lineSearchUpperLimit": 2, }, scales_estimator="Jacobian", use_verbose=debug, ) # Volumetric reconstruction set-up if args.sda: recon_method = sda.ScatteredDataApproximation( stacks, HR_volume, sigma=args.sigma, use_masks=args.use_masks_srr, ) alpha_range = [args.sigma, args.alpha] else: recon_method = tk.TikhonovSolver( stacks=stacks, reconstruction=HR_volume, reg_type="TK1", minimizer="lsmr", alpha=args.alpha_first, iter_max=np.min([args.iter_max_first, args.iter_max]), verbose=True, use_masks=args.use_masks_srr, ) alpha_range = [args.alpha_first, args.alpha] # Define the regularization parameters for the individual # reconstruction steps in the two-step cycles alphas = np.linspace( alpha_range[0], alpha_range[1], args.two_step_cycles) # Define outlier rejection threshold after each S2V-reg step thresholds = np.linspace( args.threshold_first, args.threshold, args.two_step_cycles) two_step_s2v_reg_recon = \ pipeline.TwoStepSliceToVolumeRegistrationReconstruction( stacks=stacks, reference=HR_volume, registration_method=registration, reconstruction_method=recon_method, cycles=args.two_step_cycles, alphas=alphas[0:args.two_step_cycles - 1], outlier_rejection=args.outlier_rejection, threshold_measure=rejection_measure, thresholds=thresholds, interleave=args.interleave, viewer=args.viewer, verbose=args.verbose, use_hierarchical_registration=args.s2v_hierarchical, ) two_step_s2v_reg_recon.run() HR_volume_iterations = \ two_step_s2v_reg_recon.get_iterative_reconstructions() time_registration += \ two_step_s2v_reg_recon.get_computational_time_registration() time_reconstruction += \ two_step_s2v_reg_recon.get_computational_time_reconstruction() stacks = two_step_s2v_reg_recon.get_stacks() # no two-step s2v-registration/reconstruction iterations else: HR_volume_iterations = [] # Write motion-correction results ph.print_title("Write Motion Correction Results") if args.write_motion_correction: dir_output_mc = os.path.join( dir_output, args.subfolder_motion_correction) ph.clear_directory(dir_output_mc) for stack in stacks: stack.write( dir_output_mc, write_stack=False, write_mask=False, write_slices=False, write_transforms=True, write_transforms_history=args.transforms_history, ) if args.outlier_rejection: deleted_slices_dic = {} for i, stack in enumerate(stacks): deleted_slices = stack.get_deleted_slice_numbers() deleted_slices_dic[stack.get_filename()] = deleted_slices # check whether any stack was removed entirely stacks0 = data_preprocessing.get_preprocessed_stacks() if len(stacks) != len(stacks0): stacks_remain = [s.get_filename() for s in stacks] for stack in stacks0: if stack.get_filename() in stacks_remain: continue # add info that all slices of this stack were rejected deleted_slices = [ slice.get_slice_number() for slice in stack.get_slices() ] deleted_slices_dic[stack.get_filename()] = deleted_slices ph.print_info( "All slices of stack '%s' were rejected entirely. " "Information added." % stack.get_filename()) ph.write_dictionary_to_json( deleted_slices_dic, os.path.join( dir_output, args.subfolder_motion_correction, "rejected_slices.json" ) ) # ---------------------Final Volumetric Reconstruction--------------------- ph.print_title("Final Volumetric Reconstruction") if args.sda: recon_method = sda.ScatteredDataApproximation( stacks, HR_volume, sigma=args.alpha, use_masks=args.use_masks_srr, ) else: if args.reconstruction_type in ["TVL2", "HuberL2"]: recon_method = pd.PrimalDualSolver( stacks=stacks, reconstruction=HR_volume, reg_type="TV" if args.reconstruction_type == "TVL2" else "huber", iterations=args.iterations, use_masks=args.use_masks_srr, ) else: recon_method = tk.TikhonovSolver( stacks=stacks, reconstruction=HR_volume, reg_type="TK1" if args.reconstruction_type == "TK1L2" else "TK0", use_masks=args.use_masks_srr, ) recon_method.set_alpha(args.alpha) recon_method.set_iter_max(args.iter_max) recon_method.set_verbose(True) recon_method.run() time_reconstruction += recon_method.get_computational_time() HR_volume_final = recon_method.get_reconstruction() ph.print_subtitle("Final SDA Approximation Image Mask") SDA = sda.ScatteredDataApproximation( stacks, HR_volume_final, sigma=args.sigma, sda_mask=True) SDA.run() # HR volume contains updated mask based on SDA HR_volume_final = SDA.get_reconstruction() time_reconstruction += SDA.get_computational_time() elapsed_time_total = ph.stop_timing(time_start) # Write SRR result filename = recon_method.get_setting_specific_filename() HR_volume_final.set_filename(filename) dw.DataWriter.write_image( HR_volume_final.sitk, args.output, description=filename) dw.DataWriter.write_mask( HR_volume_final.sitk_mask, ph.append_to_filename(args.output, "_mask"), description=SDA.get_setting_specific_filename()) HR_volume_iterations.insert(0, HR_volume_final) for stack in stacks: HR_volume_iterations.append(stack) if args.verbose: sitkh.show_stacks( HR_volume_iterations, segmentation=HR_volume_final, viewer=args.viewer, ) # Summary ph.print_title("Summary") exe_file_info = os.path.basename(os.path.abspath(__file__)).split(".")[0] print("%s | Computational Time for Data Preprocessing: %s" % (exe_file_info, time_data_preprocessing)) print("%s | Computational Time for Registrations: %s" % (exe_file_info, time_registration)) print("%s | Computational Time for Reconstructions: %s" % (exe_file_info, time_reconstruction)) print("%s | Computational Time for Entire Reconstruction Pipeline: %s" % (exe_file_info, elapsed_time_total)) ph.print_line_separator() return 0
def main(): time_start = ph.start_timing() np.set_printoptions(precision=3) input_parser = InputArgparser( description="Perform (linear) intensity correction across " "stacks/images given a reference stack/image", ) input_parser.add_filenames(required=True) input_parser.add_dir_output(required=True) input_parser.add_reference(required=True) input_parser.add_suffix_mask(default="_mask") input_parser.add_search_angle(default=180) input_parser.add_prefix_output(default="IC_") input_parser.add_log_config(default=1) input_parser.add_option( option_string="--registration", type=int, help="Turn on/off registration from image to reference prior to " "intensity correction.", default=0) input_parser.add_verbose(default=0) args = input_parser.parse_args() input_parser.print_arguments(args) if args.log_config: input_parser.log_config(os.path.abspath(__file__)) if args.reference in args.filenames: args.filenames.remove(args.reference) # Read data data_reader = dr.MultipleImagesReader(args.filenames, suffix_mask=args.suffix_mask, extract_slices=False) data_reader.read_data() stacks = data_reader.get_data() data_reader = dr.MultipleImagesReader([args.reference], suffix_mask=args.suffix_mask, extract_slices=False) data_reader.read_data() reference = data_reader.get_data()[0] if args.registration: # Define search angle ranges for FLIRT in all three dimensions search_angles = [ "-searchr%s -%d %d" % (x, args.search_angle, args.search_angle) for x in ["x", "y", "z"] ] search_angles = (" ").join(search_angles) registration = regflirt.FLIRT( moving=reference, registration_type="Rigid", use_fixed_mask=True, use_moving_mask=True, options=search_angles, use_verbose=False, ) # Perform Intensity Correction ph.print_title("Perform Intensity Correction") intensity_corrector = ic.IntensityCorrection( use_reference_mask=True, use_individual_slice_correction=False, prefix_corrected=args.prefix_output, use_verbose=False, ) stacks_corrected = [None] * len(stacks) for i, stack in enumerate(stacks): if args.registration: ph.print_info("Image %d/%d: Registration ... " % (i + 1, len(stacks)), newline=False) registration.set_fixed(stack) registration.run() transform_sitk = registration.get_registration_transform_sitk() stack.update_motion_correction(transform_sitk) print("done") ph.print_info("Image %d/%d: Intensity Correction ... " % (i + 1, len(stacks)), newline=False) ref = reference.get_resampled_stack(stack.sitk) ref = st.Stack.from_sitk_image(image_sitk=ref.sitk, image_sitk_mask=stack.sitk_mask * ref.sitk_mask, filename=reference.get_filename()) intensity_corrector.set_stack(stack) intensity_corrector.set_reference(ref) intensity_corrector.run_linear_intensity_correction() # intensity_corrector.run_affine_intensity_correction() stacks_corrected[i] = \ intensity_corrector.get_intensity_corrected_stack() print("done (c1 = %g) " % intensity_corrector.get_intensity_correction_coefficients()) # Write Data stacks_corrected[i].write(args.dir_output, write_mask=True, suffix_mask=args.suffix_mask) if args.verbose: sitkh.show_stacks( [ reference, stacks_corrected[i], # stacks[i], ], segmentation=stacks_corrected[i]) # ph.pause() # Write reference too (although not intensity corrected) reference.write(args.dir_output, filename=args.prefix_output + reference.get_filename(), write_mask=True, suffix_mask=args.suffix_mask) elapsed_time = ph.stop_timing(time_start) ph.print_title("Summary") print("Computational Time for Intensity Correction(s): %s" % (elapsed_time)) return 0
def main(): time_start = ph.start_timing() # Set print options for numpy np.set_printoptions(precision=3) # Read input input_parser = InputArgparser( description="Volumetric MRI reconstruction framework to reconstruct " "an isotropic, high-resolution 3D volume from multiple " "motion-corrected (or static) stacks of low-resolution slices.", ) input_parser.add_filenames(required=True) input_parser.add_filenames_masks() input_parser.add_dir_input_mc() input_parser.add_output(required=True) input_parser.add_suffix_mask(default="_mask") input_parser.add_target_stack(default=None) input_parser.add_extra_frame_target(default=10) input_parser.add_isotropic_resolution(default=None) input_parser.add_intensity_correction(default=1) input_parser.add_reconstruction_space(default=None) input_parser.add_minimizer(default="lsmr") input_parser.add_iter_max(default=10) input_parser.add_reconstruction_type(default="TK1L2") input_parser.add_data_loss(default="linear") input_parser.add_data_loss_scale(default=1) input_parser.add_alpha(default=0.01 # TK1L2 # default=0.006 #TVL2, HuberL2 ) input_parser.add_rho(default=0.5) input_parser.add_tv_solver(default="PD") input_parser.add_pd_alg_type(default="ALG2") input_parser.add_iterations(default=15) input_parser.add_log_config(default=1) input_parser.add_use_masks_srr(default=0) input_parser.add_slice_thicknesses(default=None) input_parser.add_verbose(default=0) input_parser.add_viewer(default="itksnap") input_parser.add_argument( "--mask", "-mask", action='store_true', help="If given, input images are interpreted as image masks. " "Obtained volumetric reconstruction will be exported in uint8 format.") input_parser.add_argument( "--sda", "-sda", action='store_true', help="If given, the volume is reconstructed using " "Scattered Data Approximation (Vercauteren et al., 2006). " "--alpha is considered the value for the standard deviation then. " "Recommended value is, e.g., --alpha 0.8") args = input_parser.parse_args() input_parser.print_arguments(args) if args.reconstruction_type not in ["TK1L2", "TVL2", "HuberL2"]: raise IOError("Reconstruction type unknown") if np.alltrue([not args.output.endswith(t) for t in ALLOWED_EXTENSIONS]): raise ValueError("output filename '%s' invalid; " "allowed image extensions are: %s" % (args.output, ", ".join(ALLOWED_EXTENSIONS))) dir_output = os.path.dirname(args.output) ph.create_directory(dir_output) if args.log_config: input_parser.log_config(os.path.abspath(__file__)) if args.verbose: show_niftis = [] # show_niftis = [f for f in args.filenames] # --------------------------------Read Data-------------------------------- ph.print_title("Read Data") if args.mask: filenames_masks = args.filenames else: filenames_masks = args.filenames_masks data_reader = dr.MultipleImagesReader( file_paths=args.filenames, file_paths_masks=filenames_masks, suffix_mask=args.suffix_mask, dir_motion_correction=args.dir_input_mc, stacks_slice_thicknesses=args.slice_thicknesses, ) data_reader.read_data() stacks = data_reader.get_data() ph.print_info("%d input stacks read for further processing" % len(stacks)) # Specify target stack for intensity correction and reconstruction space if args.target_stack is None: target_stack_index = 0 else: filenames = ["%s.nii.gz" % s.get_filename() for s in stacks] filename_target_stack = os.path.basename(args.target_stack) try: target_stack_index = filenames.index(filename_target_stack) except ValueError as e: raise ValueError( "--target-stack must correspond to an image as provided by " "--filenames") # ---------------------------Intensity Correction-------------------------- if args.intensity_correction and not args.mask: ph.print_title("Intensity Correction") intensity_corrector = ic.IntensityCorrection() intensity_corrector.use_individual_slice_correction(False) intensity_corrector.use_stack_mask(True) intensity_corrector.use_reference_mask(True) intensity_corrector.use_verbose(False) for i, stack in enumerate(stacks): if i == target_stack_index: ph.print_info("Stack %d (%s): Reference image. Skipped." % (i + 1, stack.get_filename())) continue else: ph.print_info("Stack %d (%s): Intensity Correction ... " % (i + 1, stack.get_filename()), newline=False) intensity_corrector.set_stack(stack) intensity_corrector.set_reference( stacks[target_stack_index].get_resampled_stack( resampling_grid=stack.sitk, interpolator="NearestNeighbor", )) intensity_corrector.run_linear_intensity_correction() stacks[i] = intensity_corrector.get_intensity_corrected_stack() print("done (c1 = %g) " % intensity_corrector.get_intensity_correction_coefficients()) # -------------------------Volumetric Reconstruction----------------------- ph.print_title("Volumetric Reconstruction") # Reconstruction space is given isotropically resampled target stack if args.reconstruction_space is None: recon0 = stacks[target_stack_index].get_isotropically_resampled_stack( resolution=args.isotropic_resolution, extra_frame=args.extra_frame_target) recon0 = recon0.get_cropped_stack_based_on_mask( boundary_i=args.extra_frame_target, boundary_j=args.extra_frame_target, boundary_k=args.extra_frame_target, unit="mm", ) # Reconstruction space was provided by user else: recon0 = st.Stack.from_filename(args.reconstruction_space, extract_slices=False) # Change resolution for isotropic resolution if provided by user if args.isotropic_resolution is not None: recon0 = recon0.get_isotropically_resampled_stack( args.isotropic_resolution) # Use image information of selected target stack as recon0 serves # as initial value for reconstruction recon0 = stacks[target_stack_index].get_resampled_stack(recon0.sitk) recon0 = recon0.get_stack_multiplied_with_mask() ph.print_info("Reconstruction space defined with %s mm3 resolution" % " x ".join(["%.2f" % s for s in recon0.sitk.GetSpacing()])) if args.sda: ph.print_title("Compute SDA reconstruction") SDA = sda.ScatteredDataApproximation(stacks, recon0, sigma=args.alpha, sda_mask=args.mask) SDA.run() recon = SDA.get_reconstruction() if args.mask: dw.DataWriter.write_mask(recon.sitk_mask, args.output) else: dw.DataWriter.write_image(recon.sitk, args.output) if args.verbose: show_niftis.insert(0, args.output) else: if args.reconstruction_type in ["TVL2", "HuberL2"]: ph.print_title("Compute Initial value for %s" % args.reconstruction_type) SRR0 = tk.TikhonovSolver( stacks=stacks, reconstruction=recon0, alpha=args.alpha, iter_max=np.min([5, args.iter_max]), reg_type="TK1", minimizer="lsmr", data_loss="linear", use_masks=args.use_masks_srr, # verbose=args.verbose, ) else: ph.print_title("Compute %s reconstruction" % args.reconstruction_type) SRR0 = tk.TikhonovSolver( stacks=stacks, reconstruction=recon0, alpha=args.alpha, iter_max=args.iter_max, reg_type="TK1", minimizer=args.minimizer, data_loss=args.data_loss, data_loss_scale=args.data_loss_scale, use_masks=args.use_masks_srr, # verbose=args.verbose, ) SRR0.run() recon = SRR0.get_reconstruction() if args.reconstruction_type in ["TVL2", "HuberL2"]: output = ph.append_to_filename(args.output, "_initTK1L2") else: output = args.output if args.mask: mask_estimator = bm.BinaryMaskFromMaskSRREstimator(recon.sitk) mask_estimator.run() mask_sitk = mask_estimator.get_mask_sitk() dw.DataWriter.write_mask(mask_sitk, output) else: dw.DataWriter.write_image(recon.sitk, output) if args.verbose: show_niftis.insert(0, output) if args.reconstruction_type in ["TVL2", "HuberL2"]: ph.print_title("Compute %s reconstruction" % args.reconstruction_type) if args.tv_solver == "ADMM": SRR = admm.ADMMSolver( stacks=stacks, reconstruction=st.Stack.from_stack( SRR0.get_reconstruction()), minimizer=args.minimizer, alpha=args.alpha, iter_max=args.iter_max, rho=args.rho, data_loss=args.data_loss, iterations=args.iterations, use_masks=args.use_masks_srr, verbose=args.verbose, ) else: SRR = pd.PrimalDualSolver( stacks=stacks, reconstruction=st.Stack.from_stack( SRR0.get_reconstruction()), minimizer=args.minimizer, alpha=args.alpha, iter_max=args.iter_max, iterations=args.iterations, alg_type=args.pd_alg_type, reg_type="TV" if args.reconstruction_type == "TVL2" else "huber", data_loss=args.data_loss, use_masks=args.use_masks_srr, verbose=args.verbose, ) SRR.run() recon = SRR.get_reconstruction() if args.mask: mask_estimator = bm.BinaryMaskFromMaskSRREstimator(recon.sitk) mask_estimator.run() mask_sitk = mask_estimator.get_mask_sitk() dw.DataWriter.write_mask(mask_sitk, args.output) else: dw.DataWriter.write_image(recon.sitk, args.output) if args.verbose: show_niftis.insert(0, args.output) if args.verbose: ph.show_niftis(show_niftis, viewer=args.viewer) ph.print_line_separator() elapsed_time = ph.stop_timing(time_start) ph.print_title("Summary") print("Computational Time for Volumetric Reconstruction: %s" % (elapsed_time)) return 0
def run(self): time_start = ph.start_timing() # if no mask is provided, use unity stacks for all masks is_unity_mask = np.alltrue([s.is_unity_mask() for s in self._stacks]) if is_unity_mask: ph.print_info( "Keep unity masks for all stacks. " "It is recommended to provide anatomical masks for increased " "accuracy.") # Segmentation propagation if self._segmentation_propagator is not None and not is_unity_mask: stacks_to_propagate_indices = [] for i in range(0, self._N_stacks): if self._stacks[i].is_unity_mask(): stacks_to_propagate_indices.append(i) stacks_to_propagate_indices = \ list(set(stacks_to_propagate_indices) - set([self._target_stack_index])) # Set target mask target = self._stacks[self._target_stack_index] # Propagate masks self._segmentation_propagator.set_template(target) for i in stacks_to_propagate_indices: ph.print_info( "Propagate mask from stack '%s' to '%s'" % (target.get_filename(), self._stacks[i].get_filename())) self._segmentation_propagator.set_stack(self._stacks[i]) self._segmentation_propagator.run_segmentation_propagation() self._stacks[i] = \ self._segmentation_propagator.get_segmented_stack() # self._stacks[i].show(1) # Crop to mask if self._use_cropping_to_mask and not is_unity_mask: ph.print_info("Crop stacks to their masks") for i in range(0, self._N_stacks): self._stacks[i] = self._stacks[ i].get_cropped_stack_based_on_mask( boundary_i=self._boundary_i, boundary_j=self._boundary_j, boundary_k=self._boundary_k, unit=self._unit) # N4 Bias Field Correction if self._use_N4BiasFieldCorrector: bias_field_corrector = n4bfc.N4BiasFieldCorrection() for i in range(0, self._N_stacks): ph.print_info( "Perform N4 Bias Field Correction for stack %d ... " % (i + 1), newline=False) bias_field_corrector.set_stack(self._stacks[i]) bias_field_corrector.run_bias_field_correction() self._stacks[i] = \ bias_field_corrector.get_bias_field_corrected_stack() print("done") # Linear Intensity Correction if self._use_intensity_correction: stacks_to_intensity_correct = list( set(range(0, self._N_stacks)) - set([self._target_stack_index])) intensity_corrector = ic.IntensityCorrection() intensity_corrector.use_individual_slice_correction(False) intensity_corrector.use_reference_mask(True) intensity_corrector.use_verbose(True) for i in stacks_to_intensity_correct: stack = self._stacks[i] intensity_corrector.set_stack(stack) intensity_corrector.set_reference( target.get_resampled_stack(resampling_grid=stack.sitk)) # intensity_corrector.run_affine_intensity_correction() intensity_corrector.run_linear_intensity_correction() self._stacks[i] = \ intensity_corrector.get_intensity_corrected_stack() self._computational_time = ph.stop_timing(time_start)
def main(): time_start = ph.start_timing() # Set print options np.set_printoptions(precision=3) pd.set_option('display.width', 1000) input_parser = InputArgparser( description=".", ) input_parser.add_filenames() input_parser.add_filenames_masks() input_parser.add_dir_input_mc() input_parser.add_suffix_mask(default="_mask") input_parser.add_reference(required=True) input_parser.add_reference_mask() input_parser.add_dir_output(required=False) input_parser.add_log_config(default=1) input_parser.add_measures( default=["PSNR", "MAE", "RMSE", "SSIM", "NCC", "NMI"]) input_parser.add_verbose(default=0) input_parser.add_target_stack(default=None) input_parser.add_intensity_correction(default=1) input_parser.add_slice_thicknesses(default=None) input_parser.add_option( option_string="--use-reference-mask", type=int, default=1) input_parser.add_option( option_string="--use-slice-masks", type=int, default=1) args = input_parser.parse_args() input_parser.print_arguments(args) if args.log_config: input_parser.log_config(os.path.abspath(__file__)) # --------------------------------Read Data-------------------------------- ph.print_title("Read Data") data_reader = dr.MultipleImagesReader( file_paths=args.filenames, file_paths_masks=args.filenames_masks, suffix_mask=args.suffix_mask, dir_motion_correction=args.dir_input_mc, stacks_slice_thicknesses=args.slice_thicknesses, ) data_reader.read_data() stacks = data_reader.get_data() ph.print_info("%d input stacks read for further processing" % len(stacks)) # Specify target stack for intensity correction and reconstruction space if args.target_stack is None: target_stack_index = 0 else: filenames = ["%s.nii.gz" % s.get_filename() for s in stacks] filename_target_stack = os.path.basename(args.target_stack) try: target_stack_index = filenames.index(filename_target_stack) except ValueError as e: raise ValueError( "--target-stack must correspond to an image as provided by " "--filenames") # ---------------------------Intensity Correction-------------------------- if args.intensity_correction: ph.print_title("Intensity Correction") intensity_corrector = ic.IntensityCorrection() intensity_corrector.use_individual_slice_correction(False) intensity_corrector.use_stack_mask(True) intensity_corrector.use_reference_mask(True) intensity_corrector.use_verbose(False) for i, stack in enumerate(stacks): if i == target_stack_index: ph.print_info("Stack %d (%s): Reference image. Skipped." % ( i + 1, stack.get_filename())) continue else: ph.print_info("Stack %d (%s): Intensity Correction ... " % ( i + 1, stack.get_filename()), newline=False) intensity_corrector.set_stack(stack) intensity_corrector.set_reference( stacks[target_stack_index].get_resampled_stack( resampling_grid=stack.sitk, interpolator="NearestNeighbor", )) intensity_corrector.run_linear_intensity_correction() stacks[i] = intensity_corrector.get_intensity_corrected_stack() print("done (c1 = %g) " % intensity_corrector.get_intensity_correction_coefficients()) # ----------------------- Slice Residual Similarity ----------------------- reference = st.Stack.from_filename(args.reference, args.reference_mask) ph.print_title("Slice Residual Similarity") residual_evaluator = res_ev.ResidualEvaluator( stacks=stacks, reference=reference, measures=args.measures, use_reference_mask=args.use_reference_mask, use_slice_masks=args.use_slice_masks, ) residual_evaluator.compute_slice_projections() residual_evaluator.evaluate_slice_similarities() residual_evaluator.write_slice_similarities(args.dir_output) elapsed_time = ph.stop_timing(time_start) ph.print_title("Summary") print("Computational Time for Slice Residual Evaluation: %s" % (elapsed_time)) return 0