Ready-to-use pipelines

Ready-to-use pipelines#

These complete HTTomo pipelines are starting points for common workflows. Select one using Choose a pipeline, then adapt its loader and method parameters to the input data. See Pipelines for the underlying concepts and Configure a pipeline for configuration guidance.

Warning

These examples track the current HTTomo development version. For a tagged release, use the matching pipeline from Versioned downloads.

GPU pipelines#

These pipelines combine GPU methods from HTTomolibGPU with CPU output methods from HTTomolib. Reconstruction methods also require TomoBAR. See Processing libraries for the role of each library.

FBP3d with find_center_vo centring and image output

# This pipeline should be supported by the latest developments of HTTomo. Use module load httomo/latest module at Diamond.
# --- Standard tomography loader for NeXus files. ---
- method: standard_tomo
  module_path: httomo.data.hdf.loaders
  parameters:
    data_path: auto
    image_key_path: auto
    rotation_angles: auto
    preview:
      detector_x:   # horizontal data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
      detector_y:   # vertical data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
    darks: null
    flats: null
    continuous_scan_subset: null
# --- Removing unresponsive/dead pixels in the data, aka zingers. Use if sharp streaks are present in the reconstruction. To be applied before normalisation. ---
- method: remove_outlier
  module_path: httomolibgpu.misc.corr
  parameters:
    kernel_size: 3  # The size of the 3D neighbourhood surrounding the voxel. Odd integer.
    dif: 1000 # A difference between the outlier value and the median value of neighbouring pixels.
# --- Flat-field and dark-field projection correction. --- 
- method: dark_flat_field_correction
  module_path: httomolibgpu.prep.normalize
  parameters:
    flats_multiplier: 1.0
    darks_multiplier: 1.0
    upper_bound: null
    lower_bound: null
# --- Center of Rotation auto-finding. Required for reconstruction bellow. ---
- method: find_center_vo
  module_path: httomolibgpu.recon.rotation
  parameters:
    ind: null  # A vertical slice (sinogram) index to calculate CoR, 'mid' can be used for middle
    average_radius: 0 # Average several sinograms to improve SNR, one can try 3-5 range
    cor_initialisation_value: null  # Use if an approximate CoR is known
    smin: -50
    smax: 50
    srad: 6.0
    step: 0.5
    ratio: 0.5
    drop: 20
  id: centering
  side_outputs:
    cor: centre_of_rotation  # An estimated CoR value provided as a side output
# --- Method to remove stripe artefacts in the data that lead to ring artefacts in the reconstruction. --- 
- method: remove_all_stripe
  module_path: httomolibgpu.prep.stripe
  parameters:
    snr: 3.0
    la_size: 61
    sm_size: 21
    dim: 1
# --- Negative log is required for reconstruction to convert raw intensity measurements into the line integrals of attenuation. --- 
- method: minus_log
  module_path: httomolibgpu.prep.normalize
  parameters: {}
# --- Reconstruction method. ---
- method: FBP3d_tomobar
  module_path: httomolibgpu.recon.algorithm
  parameters:
    center: ${{centering.side_outputs.centre_of_rotation}}  # Reference to center of rotation side output, a float number or 'null' for a middle of the horizontal dimension.
    detector_pad: false # Horizontal detector padding to minimise circle/arc-type artifacts in the reconstruction. Set to 'true' to enable automatic padding or an integer
    filter_freq_cutoff: 0.35
    recon_size: null
    recon_mask_radius: 0.95 # Zero pixels outside the mask-circle radius. Make radius equal to 2.0 to remove the mask effect.
# --- Calculate global statistics on the reconstructed volume, required for data rescaling. ---
- method: calculate_stats
  module_path: httomo.methods
  parameters: {}
  id: statistics
  side_outputs:
    glob_stats: glob_stats
# --- Rescaling the data using min/max obtained from `calculate_stats`. ---
- method: rescale_to_int
  module_path: httomolib.misc.rescale
  parameters:
    perc_range_min: 0.0
    perc_range_max: 100.0
    bits: 8
    glob_stats: ${{statistics.side_outputs.glob_stats}}
# --- Saving data into images. ---
- method: save_to_images
  module_path: httomolib.misc.images
  parameters:
    subfolder_name: images
    axis: auto
    file_format: tif  # `tif` or `jpeg` can be used.
    asynchronous: true

FBP3d with phase-correlation centring and downsampling

# This pipeline should be supported by the latest developments of HTTomo. Use module load httomo/latest module at Diamond.
# --- Standard tomography loader for NeXus files. ---
- method: standard_tomo
  module_path: httomo.data.hdf.loaders
  parameters:
    data_path: auto
    image_key_path: auto
    rotation_angles: auto
    preview:
      detector_x:   # horizontal data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
      detector_y:   # vertical data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
    darks: null
    flats: null
    continuous_scan_subset: null
# --- Flat-field and dark-field projection correction. --- 
- method: dark_flat_field_correction
  module_path: httomolibgpu.prep.normalize
  parameters:
    flats_multiplier: 1.0
    darks_multiplier: 1.0
    upper_bound: null
    lower_bound: null
# --- Center of Rotation auto-finding. Required for reconstruction bellow. ---
- method: find_center_pc
  module_path: httomolibgpu.recon.rotation
  parameters:
    proj1: auto
    proj2: auto
    tol: 0.5
    rotc_guess: null
  id: centering
  side_outputs:
    cor: centre_of_rotation  # An estimated CoR value provided as a side output
# --- Method to remove stripe artefacts in the data that lead to ring artefacts in the reconstruction. --- 
- method: remove_stripe_ti
  module_path: httomolibgpu.prep.stripe
  parameters:
    beta: 0.1
# --- Negative log is required for reconstruction to convert raw intensity measurements into the line integrals of attenuation. --- 
- method: minus_log
  module_path: httomolibgpu.prep.normalize
  parameters: {}
# --- Reconstruction method. ---
- method: FBP3d_tomobar
  module_path: httomolibgpu.recon.algorithm
  parameters:
    center: ${{centering.side_outputs.centre_of_rotation}}  # Reference to center of rotation side output, a float number or 'null' for a middle of the horizontal dimension.
    detector_pad: false # Horizontal detector padding to minimise circle/arc-type artifacts in the reconstruction. Set to 'true' to enable automatic padding or an integer
    filter_freq_cutoff: 0.35
    recon_size: null
    recon_mask_radius: 0.95 # Zero pixels outside the mask-circle radius. Make radius equal to 2.0 to remove the mask effect.
# --- Down/up sampling the data. --- 
- method: data_resampler
  module_path: httomolibgpu.misc.morph
  parameters:
    newshape: REQUIRED  # Provide a new shape for a 2D slice, e.g. [256, 256].
    axis: auto
    interpolation: linear
# --- Calculate global statistics on the reconstructed volume, required for data rescaling. ---
- method: calculate_stats
  module_path: httomo.methods
  parameters: {}
  id: statistics
  side_outputs:
    glob_stats: glob_stats
# --- Rescaling the data using min/max obtained from `calculate_stats`. ---
- method: rescale_to_int
  module_path: httomolib.misc.rescale
  parameters:
    perc_range_min: 0.0
    perc_range_max: 100.0
    bits: 8
    glob_stats: ${{statistics.side_outputs.glob_stats}}
# --- Saving data into images. ---
- method: save_to_images
  module_path: httomolib.misc.images
  parameters:
    subfolder_name: images
    axis: auto
    file_format: tif  # `tif` or `jpeg` can be used.
    asynchronous: true

LPRec3d reconstruction

# This pipeline should be supported by the latest developments of HTTomo. Use module load httomo/latest module at Diamond.
# --- Standard tomography loader for NeXus files. ---
- method: standard_tomo
  module_path: httomo.data.hdf.loaders
  parameters:
    data_path: auto
    image_key_path: auto
    rotation_angles: auto
    preview:
      detector_x:   # horizontal data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
      detector_y:   # vertical data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
    darks: null
    flats: null
    continuous_scan_subset: null
# --- Removing unresponsive/dead pixels in the data, aka zingers. Use if sharp streaks are present in the reconstruction. To be applied before normalisation. ---
- method: remove_outlier
  module_path: httomolibgpu.misc.corr
  parameters:
    kernel_size: 3  # The size of the 3D neighbourhood surrounding the voxel. Odd integer.
    dif: 1000 # A difference between the outlier value and the median value of neighbouring pixels.
# --- Flat-field and dark-field projection correction. --- 
- method: dark_flat_field_correction
  module_path: httomolibgpu.prep.normalize
  parameters:
    flats_multiplier: 1.0
    darks_multiplier: 1.0
    upper_bound: null
    lower_bound: null
# --- Center of Rotation auto-finding. Required for reconstruction bellow. ---
- method: find_center_vo
  module_path: httomolibgpu.recon.rotation
  parameters:
    ind: null  # A vertical slice (sinogram) index to calculate CoR, 'mid' can be used for middle
    average_radius: 0 # Average several sinograms to improve SNR, one can try 3-5 range
    cor_initialisation_value: null  # Use if an approximate CoR is known
    smin: -50
    smax: 50
    srad: 6.0
    step: 0.5
    ratio: 0.5
    drop: 20
  id: centering
  side_outputs:
    cor: centre_of_rotation  # An estimated CoR value provided as a side output
# --- Method to remove stripe artefacts in the data that lead to ring artefacts in the reconstruction. --- 
- method: remove_all_stripe
  module_path: httomolibgpu.prep.stripe
  parameters:
    snr: 3.0
    la_size: 61
    sm_size: 21
    dim: 1
# --- Negative log is required for reconstruction to convert raw intensity measurements into the line integrals of attenuation. --- 
- method: minus_log
  module_path: httomolibgpu.prep.normalize
  parameters: {}
# --- Reconstruction method. ---
- method: LPRec3d_tomobar
  module_path: httomolibgpu.recon.algorithm
  parameters:
    center: ${{centering.side_outputs.centre_of_rotation}}  # Reference to center of rotation side output, a float number or 'null' for a middle of the horizontal dimension.
    detector_pad: false # Horizontal detector padding to minimise circle/arc-type artifacts in the reconstruction. Set to 'true' to enable automatic padding or an integer
    filter_type: shepp
    filter_freq_cutoff: 1.0
    recon_size: null
    recon_mask_radius: 0.95 # Zero pixels outside the mask-circle radius. Make radius equal to 2.0 to remove the mask effect.
# --- Calculate global statistics on the reconstructed volume, required for data rescaling. ---
- method: calculate_stats
  module_path: httomo.methods
  parameters: {}
  id: statistics
  side_outputs:
    glob_stats: glob_stats
# --- Rescaling the data using min/max obtained from `calculate_stats`. ---
- method: rescale_to_int
  module_path: httomolib.misc.rescale
  parameters:
    perc_range_min: 0.0
    perc_range_max: 100.0
    bits: 8
    glob_stats: ${{statistics.side_outputs.glob_stats}}
# --- Saving data into images. ---
- method: save_to_images
  module_path: httomolib.misc.images
  parameters:
    subfolder_name: images
    axis: auto
    file_format: tif  # `tif` or `jpeg` can be used.
    asynchronous: true

FBP3d followed by total-variation denoising

# This pipeline should be supported by the latest developments of HTTomo. Use module load httomo/latest module at Diamond.
# --- Standard tomography loader for NeXus files. ---
- method: standard_tomo
  module_path: httomo.data.hdf.loaders
  parameters:
    data_path: auto
    image_key_path: auto
    rotation_angles: auto
    preview:
      detector_x:   # horizontal data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
      detector_y:   # vertical data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
    darks: null
    flats: null
    continuous_scan_subset: null
# --- Removing unresponsive/dead pixels in the data, aka zingers. Use if sharp streaks are present in the reconstruction. To be applied before normalisation. ---
- method: remove_outlier
  module_path: httomolibgpu.misc.corr
  parameters:
    kernel_size: 3  # The size of the 3D neighbourhood surrounding the voxel. Odd integer.
    dif: 1000 # A difference between the outlier value and the median value of neighbouring pixels.
# --- Flat-field and dark-field projection correction. --- 
- method: dark_flat_field_correction
  module_path: httomolibgpu.prep.normalize
  parameters:
    flats_multiplier: 1.0
    darks_multiplier: 1.0
    upper_bound: null
    lower_bound: null
# --- Center of Rotation auto-finding. Required for reconstruction bellow. ---
- method: find_center_vo
  module_path: httomolibgpu.recon.rotation
  parameters:
    ind: null  # A vertical slice (sinogram) index to calculate CoR, 'mid' can be used for middle
    average_radius: 0 # Average several sinograms to improve SNR, one can try 3-5 range
    cor_initialisation_value: null  # Use if an approximate CoR is known
    smin: -50
    smax: 50
    srad: 6.0
    step: 0.5
    ratio: 0.5
    drop: 20
  id: centering
  side_outputs:
    cor: centre_of_rotation  # An estimated CoR value provided as a side output
# --- Method to remove stripe artefacts in the data that lead to ring artefacts in the reconstruction. --- 
- method: remove_all_stripe
  module_path: httomolibgpu.prep.stripe
  parameters:
    snr: 3.0
    la_size: 61
    sm_size: 21
    dim: 1
# --- Negative log is required for reconstruction to convert raw intensity measurements into the line integrals of attenuation. --- 
- method: minus_log
  module_path: httomolibgpu.prep.normalize
  parameters: {}
# --- Reconstruction method. ---
- method: FBP3d_tomobar
  module_path: httomolibgpu.recon.algorithm
  parameters:
    center: ${{centering.side_outputs.centre_of_rotation}}  # Reference to center of rotation side output, a float number or 'null' for a middle of the horizontal dimension.
    detector_pad: false # Horizontal detector padding to minimise circle/arc-type artifacts in the reconstruction. Set to 'true' to enable automatic padding or an integer
    filter_freq_cutoff: 0.35
    recon_size: null
    recon_mask_radius: 0.95 # Zero pixels outside the mask-circle radius. Make radius equal to 2.0 to remove the mask effect.
# --- Using denoising method to reduce noise. ---
- method: total_variation_PD
  module_path: httomolibgpu.misc.denoise
  parameters:
    regularisation_parameter: 1.0e-05
    iterations: 1000
    isotropic: true
    nonnegativity: false
    lipschitz_const: 8.0
    half_precision: false
# --- Calculate global statistics on the reconstructed volume, required for data rescaling. ---
- method: calculate_stats
  module_path: httomo.methods
  parameters: {}
  id: statistics
  side_outputs:
    glob_stats: glob_stats
# --- Rescaling the data using min/max obtained from `calculate_stats`. ---
- method: rescale_to_int
  module_path: httomolib.misc.rescale
  parameters:
    perc_range_min: 0.0
    perc_range_max: 100.0
    bits: 8
    glob_stats: ${{statistics.side_outputs.glob_stats}}
# --- Saving data into images. ---
- method: save_to_images
  module_path: httomolib.misc.images
  parameters:
    subfolder_name: images
    axis: auto
    file_format: tif  # `tif` or `jpeg` can be used.
    asynchronous: true

FISTA3d with total-variation regularisation

This iterative example is intended for noisy or undersampled data.

# This pipeline should be supported by the latest developments of HTTomo. Use module load httomo/latest module at Diamond.
# --- Standard tomography loader for NeXus files. ---
- method: standard_tomo
  module_path: httomo.data.hdf.loaders
  parameters:
    data_path: auto
    image_key_path: auto
    rotation_angles: auto
    preview:
      detector_x:   # horizontal data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
      detector_y:   # vertical data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
    darks: null
    flats: null
    continuous_scan_subset: null
# --- Flat-field and dark-field projection correction. --- 
- method: dark_flat_field_correction
  module_path: httomolibgpu.prep.normalize
  parameters:
    flats_multiplier: 1.0
    darks_multiplier: 1.0
    upper_bound: null
    lower_bound: null
# --- Center of Rotation auto-finding. Required for reconstruction bellow. ---
- method: find_center_vo
  module_path: httomolibgpu.recon.rotation
  parameters:
    ind: null  # A vertical slice (sinogram) index to calculate CoR, 'mid' can be used for middle
    average_radius: 0 # Average several sinograms to improve SNR, one can try 3-5 range
    cor_initialisation_value: null  # Use if an approximate CoR is known
    smin: -50
    smax: 50
    srad: 6.0
    step: 0.5
    ratio: 0.5
    drop: 20
  id: centering
  side_outputs:
    cor: centre_of_rotation  # An estimated CoR value provided as a side output
# --- Negative log is required for reconstruction to convert raw intensity measurements into the line integrals of attenuation. --- 
- method: minus_log
  module_path: httomolibgpu.prep.normalize
  parameters: {}
# --- Reconstruction method. ---
- method: FISTA3d_tomobar
  module_path: httomolibgpu.recon.algorithm
  parameters:
    center: ${{centering.side_outputs.centre_of_rotation}}  # Reference to center of rotation side output, a float number or 'null' for a middle of the horizontal dimension.
    detector_pad: false # Horizontal detector padding to minimise circle/arc-type artifacts in the reconstruction. Set to 'true' to enable automatic padding or an integer
    recon_size: null
    recon_mask_radius: 0.95 # Zero pixels outside the mask-circle radius. Make radius equal to 2.0 to remove the mask effect.
    iterations: 20
    subsets_number: 6
    data_fidelity: LS
    regularisation_type: PD_TV
    regularisation_parameter: 1.0e-06
    regularisation_iterations: 50
    regularisation_half_precision: true
    nonnegativity: true
# --- Calculate global statistics on the reconstructed volume, required for data rescaling. ---
- method: calculate_stats
  module_path: httomo.methods
  parameters: {}
  id: statistics
  side_outputs:
    glob_stats: glob_stats
# --- Rescaling the data using min/max obtained from `calculate_stats`. ---
- method: rescale_to_int
  module_path: httomolib.misc.rescale
  parameters:
    perc_range_min: 0.0
    perc_range_max: 100.0
    bits: 8
    glob_stats: ${{statistics.side_outputs.glob_stats}}
# --- Saving data into images. ---
- method: save_to_images
  module_path: httomolib.misc.images
  parameters:
    subfolder_name: images
    axis: auto
    file_format: tif  # `tif` or `jpeg` can be used.
    asynchronous: true

Tutorial pipelines#

These pipelines accompany Tutorials, where the input data is provided or generated.

Diamond-specific pipelines#

These examples implement processing strategies used at Diamond Light Source. Required parameters and calibration files can differ between beamlines.

Convert a 360° scan to 180°, apply Paganin filtering and reconstruct

# This pipeline should be supported by the latest developments of HTTomo. Use module load httomo/latest module at Diamond.
# --- Standard tomography loader for NeXus files. ---
- method: standard_tomo
  module_path: httomo.data.hdf.loaders
  parameters:
    data_path: auto
    image_key_path: auto
    rotation_angles: auto
    preview:
      detector_x:   # horizontal data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
      detector_y:   # vertical data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
    darks: null
    flats: null
    continuous_scan_subset: null
# --- Flat-field and dark-field projection correction. --- 
- method: dark_flat_field_correction
  module_path: httomolibgpu.prep.normalize
  parameters:
    flats_multiplier: 1.0
    darks_multiplier: 1.0
    upper_bound: null
    lower_bound: null
# --- Center of Rotation auto-finding. Required for reconstruction bellow. ---
- method: find_center_360
  module_path: httomolibgpu.recon.rotation
  parameters:
    ind: null  # A vertical slice (sinogram) index to calculate CoR, 'mid' can be used for middle
    win_width: 10
    side: null # Choose 'left' to stitch to the left side, 'right' to the right side, or null for automated determination
    denoise: true
    norm: false
    use_overlap: false
  id: centering
  side_outputs:
    cor: centre_of_rotation  # An estimated CoR value provided as a side output
    overlap: overlap # An overlap to use for converting 360 degrees scan to 180 degrees scan.
    side: side
    overlap_position: overlap_position
# --- Using the overlap and side provided, converting 360 degrees scan to 180 degrees scan. --- 
- method: sino_360_to_180
  module_path: httomolibgpu.misc.morph
  parameters:
    overlap: ${{centering.side_outputs.overlap}}
    side: ${{centering.side_outputs.side}}
# --- Method to remove stripe artefacts in the data that lead to ring artefacts in the reconstruction. --- 
- method: remove_stripe_based_sorting
  module_path: httomolibgpu.prep.stripe
  parameters:
    size: 11
    dim: 1
# --- Apply a phase contrast filter to improve image contrast. --- 
- method: paganin_filter
  module_path: httomolibgpu.prep.phase
  parameters:
    pixel_size: 1.28  # Detector pixel size (resolution) in MICRON units.
    distance: 1.0 # Propagation distance of the wavefront from sample to detector in METRE units.
    energy: 53.0 # Beam energy in keV.
    ratio_delta_beta: 250  # The ratio of delta/beta for filter strength control. Larger values lead to more smoothing.
    calculate_padding_value_method: next_power_of_2 # Select type of padding from 'next_power_of_2', 'next_fast_length' and 'use_pad_x_y'.
    pad_x_y: null # Manual padding is enabled when 'calculate_padding_value_method' is set to 'use_pad_x_y'.
# --- Reconstruction method. ---
- method: FBP3d_tomobar
  module_path: httomolibgpu.recon.algorithm
  parameters:
    center: ${{centering.side_outputs.centre_of_rotation}}  # Reference to center of rotation side output, a float number or 'null' for a middle of the horizontal dimension.
    detector_pad: false # Horizontal detector padding to minimise circle/arc-type artifacts in the reconstruction. Set to 'true' to enable automatic padding or an integer
    filter_freq_cutoff: 0.35
    recon_size: null
    recon_mask_radius: 0.95 # Zero pixels outside the mask-circle radius. Make radius equal to 2.0 to remove the mask effect.
# --- Calculate global statistics on the reconstructed volume, required for data rescaling. ---
- method: calculate_stats
  module_path: httomo.methods
  parameters: {}
  id: statistics
  side_outputs:
    glob_stats: glob_stats
# --- Rescaling the data using min/max obtained from `calculate_stats`. ---
- method: rescale_to_int
  module_path: httomolib.misc.rescale
  parameters:
    perc_range_min: 0.0
    perc_range_max: 100.0
    bits: 8
    glob_stats: ${{statistics.side_outputs.glob_stats}}
# --- Saving data into images. ---
- method: save_to_images
  module_path: httomolib.misc.images
  parameters:
    subfolder_name: images
    axis: auto
    file_format: tif  # `tif` or `jpeg` can be used.
    asynchronous: true

Correct distortion, convert a 360° scan and reconstruct

# This pipeline should be supported by the latest developments of HTTomo. Use module load httomo/latest module at Diamond.
# --- Standard tomography loader for NeXus files. ---
- method: standard_tomo
  module_path: httomo.data.hdf.loaders
  parameters:
    data_path: auto
    image_key_path: auto
    rotation_angles: auto
    preview:
      detector_x:   # horizontal data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
      detector_y:   # vertical data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
    darks: null
    flats: null
    continuous_scan_subset: null
# --- Applying optical distortion correction to projections. --- 
- method: distortion_correction_proj_discorpy
  module_path: httomolibgpu.prep.alignment
  parameters:
    metadata_path: REQUIRED  # Provide an absolute path to the text file with distortion coefficients.
    order: 3
    mode: constant
# --- Flat-field and dark-field projection correction. --- 
- method: dark_flat_field_correction
  module_path: httomolibgpu.prep.normalize
  parameters:
    flats_multiplier: 1.0
    darks_multiplier: 1.0
    upper_bound: null
    lower_bound: null
# --- Center of Rotation auto-finding. Required for reconstruction bellow. ---
- method: find_center_360
  module_path: httomolibgpu.recon.rotation
  parameters:
    ind: null  # A vertical slice (sinogram) index to calculate CoR, 'mid' can be used for middle
    win_width: 10
    side: null # Choose 'left' to stitch to the left side, 'right' to the right side, or null for automated determination
    denoise: true
    norm: false
    use_overlap: false
  id: centering
  side_outputs:
    cor: centre_of_rotation  # An estimated CoR value provided as a side output
    overlap: overlap # An overlap to use for converting 360 degrees scan to 180 degrees scan.
    side: side
    overlap_position: overlap_position
# --- Using the overlap and side provided, converting 360 degrees scan to 180 degrees scan. --- 
- method: sino_360_to_180
  module_path: httomolibgpu.misc.morph
  parameters:
    overlap: ${{centering.side_outputs.overlap}}
    side: ${{centering.side_outputs.side}}
# --- Method to remove stripe artefacts in the data that lead to ring artefacts in the reconstruction. --- 
- method: remove_stripe_based_sorting
  module_path: httomolibgpu.prep.stripe
  parameters:
    size: 11
    dim: 1
# --- Negative log is required for reconstruction to convert raw intensity measurements into the line integrals of attenuation. --- 
- method: minus_log
  module_path: httomolibgpu.prep.normalize
  parameters: {}
# --- Reconstruction method. ---
- method: FBP3d_tomobar
  module_path: httomolibgpu.recon.algorithm
  parameters:
    center: ${{centering.side_outputs.centre_of_rotation}}  # Reference to center of rotation side output, a float number or 'null' for a middle of the horizontal dimension.
    detector_pad: false # Horizontal detector padding to minimise circle/arc-type artifacts in the reconstruction. Set to 'true' to enable automatic padding or an integer
    filter_freq_cutoff: 0.35
    recon_size: null
    recon_mask_radius: 0.95 # Zero pixels outside the mask-circle radius. Make radius equal to 2.0 to remove the mask effect.
# --- Calculate global statistics on the reconstructed volume, required for data rescaling. ---
- method: calculate_stats
  module_path: httomo.methods
  parameters: {}
  id: statistics
  side_outputs:
    glob_stats: glob_stats
# --- Rescaling the data using min/max obtained from `calculate_stats`. ---
- method: rescale_to_int
  module_path: httomolib.misc.rescale
  parameters:
    perc_range_min: 0.0
    perc_range_max: 100.0
    bits: 8
    glob_stats: ${{statistics.side_outputs.glob_stats}}
# --- Saving data into images. ---
- method: save_to_images
  module_path: httomolib.misc.images
  parameters:
    subfolder_name: images
    axis: auto
    file_format: tif  # `tif` or `jpeg` can be used.
    asynchronous: true

Parameter-sweep pipelines#

Sweep runs automatically add image output after each swept method. Do not add a separate image-saving method there. See Parameter Sweeping for syntax and execution details.

Sweep selected Paganin ratio values with !Sweep

# This pipeline should be supported by the latest developments of HTTomo. Use module load httomo/latest module at Diamond.
# --- Standard tomography loader for NeXus files. ---
- method: standard_tomo
  module_path: httomo.data.hdf.loaders
  parameters:
    data_path: auto
    image_key_path: auto
    rotation_angles: auto
    preview:
      detector_x:   # horizontal data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
      detector_y:   # vertical data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
    darks: null
    flats: null
    continuous_scan_subset: null
# --- Flat-field and dark-field projection correction. --- 
- method: dark_flat_field_correction
  module_path: httomolibgpu.prep.normalize
  parameters:
    flats_multiplier: 1.0
    darks_multiplier: 1.0
    upper_bound: null
    lower_bound: null
# --- Center of Rotation auto-finding. Required for reconstruction bellow. ---
- method: find_center_vo
  module_path: httomolibgpu.recon.rotation
  parameters:
    ind: null  # A vertical slice (sinogram) index to calculate CoR, 'mid' can be used for middle
    average_radius: 0 # Average several sinograms to improve SNR, one can try 3-5 range
    cor_initialisation_value: null  # Use if an approximate CoR is known
    smin: -50
    smax: 50
    srad: 6.0
    step: 0.5
    ratio: 0.5
    drop: 20
  id: centering
  side_outputs:
    cor: centre_of_rotation  # An estimated CoR value provided as a side output
# --- Apply a phase contrast filter to improve image contrast. --- 
- method: paganin_filter
  module_path: httomolibgpu.prep.phase
  parameters:
    pixel_size: 1.28  # Detector pixel size (resolution) in MICRON units.
    distance: 1.0 # Propagation distance of the wavefront from sample to detector in METRE units.
    energy: 53.0 # Beam energy in keV.
    ratio_delta_beta: !Sweep  # The ratio of delta/beta for filter strength control. Larger values lead to more smoothing.
    - 10
    - 150
    - 350
    calculate_padding_value_method: next_power_of_2 # Select type of padding from 'next_power_of_2', 'next_fast_length' and 'use_pad_x_y'.
    pad_x_y: null # Manual padding is enabled when 'calculate_padding_value_method' is set to 'use_pad_x_y'.
# --- Reconstruction method. ---
- method: FBP3d_tomobar
  module_path: httomolibgpu.recon.algorithm
  parameters:
    center: ${{centering.side_outputs.centre_of_rotation}}  # Reference to center of rotation side output, a float number or 'null' for a middle of the horizontal dimension.
    detector_pad: false # Horizontal detector padding to minimise circle/arc-type artifacts in the reconstruction. Set to 'true' to enable automatic padding or an integer
    filter_freq_cutoff: 0.35
    recon_size: null
    recon_mask_radius: 0.95 # Zero pixels outside the mask-circle radius. Make radius equal to 2.0 to remove the mask effect.

CPU pipeline#

Use the TomoPy example when a CUDA-capable GPU is unavailable. Performance depends on the input, method parameters, CPU resources and process count.

TomoPy gridrec with automatic centring

# This pipeline should be supported by the latest developments of HTTomo. Use module load httomo/latest module at Diamond.
# --- Standard tomography loader for NeXus files. ---
- method: standard_tomo
  module_path: httomo.data.hdf.loaders
  parameters:
    data_path: auto
    image_key_path: auto
    rotation_angles: auto
    preview:
      detector_x:   # horizontal data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
      detector_y:   # vertical data previewing/cropping.
        # when null, the full data dimension is used, i.e., no previewing
        start: null
        stop: null
    darks: null
    flats: null
    continuous_scan_subset: null
# --- Removing unresponsive/dead pixels in the data, aka zingers. Use if sharp streaks are present in the reconstruction. To be applied before normalisation. ---
- method: remove_outlier
  module_path: tomopy.misc.corr
  parameters:
    dif: 0.1  # A difference between the outlier value and the median value of neighbouring pixels.
    size: 3
    axis: auto
# --------------------------------------------------------#
- method: normalize
  module_path: tomopy.prep.normalize
  parameters:
    cutoff: null
    averaging: mean
# --- Negative log is required for reconstruction to convert raw intensity measurements into the line integrals of attenuation. --- 
- method: minus_log
  module_path: tomopy.prep.normalize
  parameters: {}
# --- Center of Rotation auto-finding. Required for reconstruction bellow. ---
- method: find_center_vo
  module_path: tomopy.recon.rotation
  parameters:
    ind: null  # A vertical slice (sinogram) index to calculate CoR, 'mid' can be used for middle
    smin: -50
    smax: 50
    srad: 6
    step: 0.25
    ratio: 0.5
    drop: 20
  id: centering
  side_outputs:
    cor: centre_of_rotation  # An estimated CoR value provided as a side output
# --- Reconstruction method. ---
- method: recon
  module_path: tomopy.recon.algorithm
  parameters:
    center: ${{centering.side_outputs.centre_of_rotation}}  # Reference to center of rotation side output, a float number or 'null' for a middle of the horizontal dimension.
    sinogram_order: false
    algorithm: gridrec  # Select the required algorithm, e.g. 'gridrec'
    init_recon: null
# --- Calculate global statistics on the reconstructed volume, required for data rescaling. ---
- method: calculate_stats
  module_path: httomo.methods
  parameters: {}
  id: statistics
  side_outputs:
    glob_stats: glob_stats
# --- Rescaling the data using min/max obtained from `calculate_stats`. ---
- method: rescale_to_int
  module_path: httomolib.misc.rescale
  parameters:
    perc_range_min: 0.0
    perc_range_max: 100.0
    bits: 8
    glob_stats: ${{statistics.side_outputs.glob_stats}}
# --- Saving data into images. ---
- method: save_to_images
  module_path: httomolib.misc.images
  parameters:
    subfolder_name: images
    axis: auto
    file_format: tif  # `tif` or `jpeg` can be used.
    asynchronous: true