def main(): input_parser = InputArgparser( description="Show data/slice coverage over specified reconstruction " "space.", ) input_parser.add_filenames(required=True) input_parser.add_reconstruction_space(required=True) input_parser.add_output(required=True) input_parser.add_dir_input_mc() input_parser.add_slice_thicknesses() input_parser.add_verbose(default=0) args = input_parser.parse_args() input_parser.print_arguments(args) data_reader = dr.MultipleImagesReader( file_paths=args.filenames, dir_motion_correction=args.dir_input_mc, stacks_slice_thicknesses=args.slice_thicknesses, ) data_reader.read_data() stacks = data_reader.get_data() reconstruction_space_sitk = sitk.ReadImage(args.reconstruction_space) slice_coverage = sc.SliceCoverage( stacks=stacks, reconstruction_sitk=reconstruction_space_sitk, ) slice_coverage.run() coverage_sitk = slice_coverage.get_coverage_sitk() dw.DataWriter.write_mask(coverage_sitk, args.output) if args.verbose: niftis = [ args.reconstruction_space, args.output, ] ph.show_niftis(niftis)
def main(): time_start = ph.start_timing() # Set print options for numpy np.set_printoptions(precision=3) # Read input input_parser = InputArgparser( description="Script to study reconstruction parameters and their " "impact on the volumetric reconstruction quality. " "This script can only be used to sweep through one single parameter, " "e.g. the regularization parameter 'alpha'. ") input_parser.add_filenames(required=True) input_parser.add_filenames_masks() input_parser.add_suffix_mask(default="_mask") input_parser.add_dir_input_mc() input_parser.add_dir_output(required=True) input_parser.add_reconstruction_space() input_parser.add_reference( help="Path to reference NIfTI image file. If given the volumetric " "reconstructed is performed in this physical space. " "Either a reconstruction space or a reference must be provided", required=False) input_parser.add_reference_mask(default=None) input_parser.add_study_name() input_parser.add_reconstruction_type(default="TK1L2") input_parser.add_measures( default=["PSNR", "MAE", "RMSE", "SSIM", "NCC", "NMI"]) input_parser.add_tv_solver(default="PD") input_parser.add_iterations(default=50) input_parser.add_rho(default=0.1) input_parser.add_iter_max(default=10) input_parser.add_minimizer(default="lsmr") input_parser.add_log_config(default=1) input_parser.add_use_masks_srr(default=0) input_parser.add_verbose(default=1) input_parser.add_slice_thicknesses(default=None) input_parser.add_argument( "--append", "-append", action='store_true', help="If given, results are appended to previously executed parameter " "study with identical parameters and study name store in the output " "directory.") # Range for parameter sweeps input_parser.add_alphas(default=list(np.linspace(0.01, 0.5, 5))) input_parser.add_data_losses(default=["linear"] # default=["linear", "arctan"] ) input_parser.add_data_loss_scales(default=[1] # default=[0.1, 0.5, 1.5] ) args = input_parser.parse_args() input_parser.print_arguments(args) if args.reference is None and args.reconstruction_space is None: raise IOError("Either reference (--reference) or reconstruction space " "(--reconstruction-space) must be provided.") 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)) if args.reference is not None: reference = st.Stack.from_filename(file_path=args.reference, file_path_mask=args.reference_mask, extract_slices=False) reconstruction_space = stacks[0].get_resampled_stack(reference.sitk) reconstruction_space = \ reconstruction_space.get_stack_multiplied_with_mask() x_ref = sitk.GetArrayFromImage(reference.sitk).flatten() x_ref_mask = sitk.GetArrayFromImage(reference.sitk_mask).flatten() else: reconstruction_space = st.Stack.from_filename( file_path=args.reconstruction_space, extract_slices=False) reconstruction_space = stacks[0].get_resampled_stack( reconstruction_space.sitk) reconstruction_space = \ reconstruction_space.get_stack_multiplied_with_mask() x_ref = None x_ref_mask = None # ----------------------------Set Up Parameters---------------------------- parameters = {} parameters["alpha"] = args.alphas if len(args.data_losses) > 1: parameters["data_loss"] = args.data_losses if len(args.data_loss_scales) > 1: parameters["data_loss_scale"] = args.data_loss_scales # --------------------------Set Up Parameter Study------------------------- ph.print_title("Run Parameter Study") if args.study_name is None: name = args.reconstruction_type else: name = args.study_name reconstruction_info = { "shape": reconstruction_space.sitk.GetSize()[::-1], "origin": reconstruction_space.sitk.GetOrigin(), "spacing": reconstruction_space.sitk.GetSpacing(), "direction": reconstruction_space.sitk.GetDirection(), } # Create Tikhonov solver from which all information can be extracted # (also for other reconstruction types) tmp = tk.TikhonovSolver( stacks=stacks, reconstruction=reconstruction_space, alpha=args.alphas[0], iter_max=args.iter_max, data_loss=args.data_losses[0], data_loss_scale=args.data_loss_scales[0], reg_type="TK1", minimizer=args.minimizer, verbose=args.verbose, use_masks=args.use_masks_srr, ) solver = tmp.get_solver() parameter_study_interface = \ deconv_interface.DeconvolutionParameterStudyInterface( A=solver.get_A(), A_adj=solver.get_A_adj(), D=solver.get_B(), D_adj=solver.get_B_adj(), b=solver.get_b(), x0=solver.get_x0(), alpha=solver.get_alpha(), x_scale=solver.get_x_scale(), data_loss=solver.get_data_loss(), data_loss_scale=solver.get_data_loss_scale(), iter_max=solver.get_iter_max(), minimizer=solver.get_minimizer(), iterations=args.iterations, measures=args.measures, dimension=3, L2=16. / reconstruction_space.sitk.GetSpacing()[0]**2, reconstruction_type=args.reconstruction_type, rho=args.rho, dir_output=args.dir_output, parameters=parameters, name=name, reconstruction_info=reconstruction_info, x_ref=x_ref, x_ref_mask=x_ref_mask, tv_solver=args.tv_solver, verbose=args.verbose, append=args.append, ) parameter_study_interface.set_up_parameter_study() parameter_study = parameter_study_interface.get_parameter_study() # Run parameter study parameter_study.run() print("\nComputational time for Deconvolution Parameter Study %s: %s" % (name, parameter_study.get_computational_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_dir_input() input_parser.add_filenames() input_parser.add_image_selection() input_parser.add_dir_output(required=True) input_parser.add_prefix_output(default="SRR_") input_parser.add_suffix_mask(default="_mask") input_parser.add_target_stack_index(default=0) input_parser.add_extra_frame_target(default=10) input_parser.add_isotropic_resolution(default=None) 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=0.02 # 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_subfolder_comparison() input_parser.add_provide_comparison(default=0) input_parser.add_log_script_execution(default=1) input_parser.add_verbose(default=0) args = input_parser.parse_args() input_parser.print_arguments(args) # Write script execution call if args.log_script_execution: input_parser.write_performed_script_execution( os.path.abspath(__file__)) # --------------------------------Read Data-------------------------------- ph.print_title("Read Data") # Neither '--dir-input' nor '--filenames' was specified if args.filenames is not None and args.dir_input is not None: raise IOError("Provide input by either '--dir-input' or '--filenames' " "but not both together") # '--dir-input' specified elif args.dir_input is not None: data_reader = dr.ImageSlicesDirectoryReader( path_to_directory=args.dir_input, suffix_mask=args.suffix_mask, image_selection=args.image_selection) # '--filenames' specified elif args.filenames is not None: data_reader = dr.MultipleImagesReader(args.filenames, suffix_mask=args.suffix_mask) else: raise IOError("Provide input by either '--dir-input' or '--filenames'") if args.reconstruction_type not in ["TK1L2", "TVL2", "HuberL2"]: raise IOError("Reconstruction type unknown") data_reader.read_data() stacks = data_reader.get_data() ph.print_info("%d input stacks read for further processing" % len(stacks)) # Reconstruction space is given isotropically resampled target stack if args.reconstruction_space is None: recon0 = \ stacks[args.target_stack_index].get_isotropically_resampled_stack( resolution=args.isotropic_resolution, extra_frame=args.extra_frame_target) # 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[args.target_stack_index].get_resampled_stack(recon0.sitk) recon0 = recon0.get_stack_multiplied_with_mask() 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=args.iter_max, reg_type="TK1", minimizer=args.minimizer, data_loss=args.data_loss, data_loss_scale=args.data_loss_scale, # verbose=args.verbose, ) SRR0.run() recon = SRR0.get_reconstruction() recon.set_filename(SRR0.get_setting_specific_filename(args.prefix_output)) recon.write(args.dir_output) # List to store SRRs recons = [] for i in range(0, len(stacks)): recons.append(stacks[i]) recons.insert(0, recon) 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, verbose=args.verbose, ) SRR.run() recon = SRR.get_reconstruction() recon.set_filename( SRR.get_setting_specific_filename(args.prefix_output)) recons.insert(0, recon) recon.write(args.dir_output) 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, verbose=args.verbose, ) SRR.run() recon = SRR.get_reconstruction() recon.set_filename( SRR.get_setting_specific_filename(args.prefix_output)) recons.insert(0, recon) recon.write(args.dir_output) if args.verbose and not args.provide_comparison: sitkh.show_stacks(recons) # Show SRR together with linearly resampled input data. # Additionally, a script is generated to open files if args.provide_comparison: sitkh.show_stacks( recons, show_comparison_file=args.provide_comparison, dir_output=os.path.join(args.dir_output, args.subfolder_comparison), ) 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 main(): time_start = ph.start_timing() # Set print options for numpy np.set_printoptions(precision=3) # Read input input_parser = InputArgparser( description="Script to study reconstruction parameters and their " "impact on the volumetric reconstruction quality.", ) input_parser.add_dir_input() input_parser.add_filenames() input_parser.add_image_selection() input_parser.add_dir_output(required=True) input_parser.add_suffix_mask(default="_mask") input_parser.add_reconstruction_space() input_parser.add_reference( help="Path to reference NIfTI image file. If given the volumetric " "reconstructed is performed in this physical space. " "Either a reconstruction space or a reference must be provided", required=False) input_parser.add_reference_mask(default=None) input_parser.add_study_name() input_parser.add_reconstruction_type(default="TK1L2") input_parser.add_measures(default=["PSNR", "RMSE", "SSIM", "NCC", "NMI"]) input_parser.add_tv_solver(default="PD") input_parser.add_iterations(default=50) input_parser.add_rho(default=0.1) input_parser.add_iter_max(default=10) input_parser.add_minimizer(default="lsmr") input_parser.add_alpha(default=0.01) input_parser.add_data_loss(default="linear") input_parser.add_data_loss_scale(default=1) input_parser.add_log_script_execution(default=1) input_parser.add_verbose(default=1) # Range for parameter sweeps input_parser.add_alpha_range(default=[0.001, 0.05, 20]) # TK1L2 # input_parser.add_alpha_range(default=[0.001, 0.003, 10]) # TVL2, HuberL2 input_parser.add_data_losses( # default=["linear", "arctan"] ) input_parser.add_data_loss_scale_range( # default=[0.1, 1.5, 2] ) args = input_parser.parse_args() input_parser.print_arguments(args) if args.reference is None and args.reconstruction_space is None: raise IOError("Either reference (--reference) or reconstruction space " "(--reconstruction-space) must be provided.") # Write script execution call if args.log_script_execution: input_parser.write_performed_script_execution( os.path.abspath(__file__)) # --------------------------------Read Data-------------------------------- ph.print_title("Read Data") # Neither '--dir-input' nor '--filenames' was specified if args.filenames is not None and args.dir_input is not None: raise IOError( "Provide input by either '--dir-input' or '--filenames' " "but not both together") # '--dir-input' specified elif args.dir_input is not None: data_reader = dr.ImageSlicesDirectoryReader( path_to_directory=args.dir_input, suffix_mask=args.suffix_mask, image_selection=args.image_selection) # '--filenames' specified elif args.filenames is not None: data_reader = dr.MultipleImagesReader( args.filenames, suffix_mask=args.suffix_mask) else: raise IOError( "Provide input by either '--dir-input' or '--filenames'") data_reader.read_data() stacks = data_reader.get_data() ph.print_info("%d input stacks read for further processing" % len(stacks)) if args.reference is not None: reference = st.Stack.from_filename( file_path=args.reference, file_path_mask=args.reference_mask, extract_slices=False) reconstruction_space = stacks[0].get_resampled_stack(reference.sitk) reconstruction_space = \ reconstruction_space.get_stack_multiplied_with_mask() x_ref = sitk.GetArrayFromImage(reference.sitk).flatten() x_ref_mask = sitk.GetArrayFromImage(reference.sitk_mask).flatten() else: reconstruction_space = st.Stack.from_filename( file_path=args.reconstruction_space, extract_slices=False) reconstruction_space = stacks[0].get_resampled_stack( reconstruction_space.sitk) reconstruction_space = \ reconstruction_space.get_stack_multiplied_with_mask() x_ref = None x_ref_mask = None # ----------------------------Set Up Parameters---------------------------- parameters = {} parameters["alpha"] = np.linspace( args.alpha_range[0], args.alpha_range[1], int(args.alpha_range[2])) if args.data_losses is not None: parameters["data_loss"] = args.data_losses if args.data_loss_scale_range is not None: parameters["data_loss_scale"] = np.linspace( args.data_loss_scale_range[0], args.data_loss_scale_range[1], int(args.data_loss_scale_range[2])) # --------------------------Set Up Parameter Study------------------------- if args.study_name is None: name = args.reconstruction_type else: name = args.study_name reconstruction_info = { "shape": reconstruction_space.sitk.GetSize()[::-1], "origin": reconstruction_space.sitk.GetOrigin(), "spacing": reconstruction_space.sitk.GetSpacing(), "direction": reconstruction_space.sitk.GetDirection(), } # Create Tikhonov solver from which all information can be extracted # (also for other reconstruction types) tmp = tk.TikhonovSolver( stacks=stacks, reconstruction=reconstruction_space, alpha=args.alpha, iter_max=args.iter_max, data_loss=args.data_loss, data_loss_scale=args.data_loss_scale, reg_type="TK1", minimizer=args.minimizer, verbose=args.verbose, ) solver = tmp.get_solver() parameter_study_interface = \ deconv_interface.DeconvolutionParameterStudyInterface( A=solver.get_A(), A_adj=solver.get_A_adj(), D=solver.get_B(), D_adj=solver.get_B_adj(), b=solver.get_b(), x0=solver.get_x0(), alpha=solver.get_alpha(), x_scale=solver.get_x_scale(), data_loss=solver.get_data_loss(), data_loss_scale=solver.get_data_loss_scale(), iter_max=solver.get_iter_max(), minimizer=solver.get_minimizer(), iterations=args.iterations, measures=args.measures, dimension=3, L2=16./reconstruction_space.sitk.GetSpacing()[0]**2, reconstruction_type=args.reconstruction_type, rho=args.rho, dir_output=args.dir_output, parameters=parameters, name=name, reconstruction_info=reconstruction_info, x_ref=x_ref, x_ref_mask=x_ref_mask, tv_solver=args.tv_solver, verbose=args.verbose, ) parameter_study_interface.set_up_parameter_study() parameter_study = parameter_study_interface.get_parameter_study() # Run parameter study parameter_study.run() print("\nComputational time for Deconvolution Parameter Study %s: %s" % (name, parameter_study.get_computational_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