Basic reconstruction with nabu

In this notebook, we see how to use the different building blocks of nabu to parse a dataset and perform a basic reconstruction.

This notebook uses a dataset of a bamboo stick (acquired at ESRF ID19, courtesy Ludovic Broche). The projections were binned by a factor of 4 in each dimension, and the angular range was also subsampled by 4.

Browse the dataset

We use nabu utility analyze_dataset which builds a data structure with all information on the dataset.

import numpy as np
from tempfile import mkdtemp
from nabu.resources.dataset_analyzer import analyze_dataset
from nabu.resources.nxflatfield import update_dataset_info_flats_darks
from nabu.testutils import get_file

WARNING:silx.DEPRECATION:Module silx.third_party is deprecated since silx version 2.0.0. Use 'fabio' instead.
  File "/home/pierre/.venv/py311/lib/python3.11/site-packages/silx/third_party/__init__.py", line 36, in <module>
    deprecated_warning(
WARNING:silx.DEPRECATION:Module silx.third_party.EdfFile is deprecated since silx version 2.0.0. Use 'fabio.open and fabio.edfimage.EdfImage' instead.
  File "/home/pierre/.venv/py311/lib/python3.11/site-packages/silx/third_party/EdfFile.py", line 118, in <module>
    deprecated_warning(

print("Getting dataset (downloading if necessary) ...")
data_path = get_file("bamboo_reduced.nx")
print("... OK")
output_dir = mkdtemp(prefix="nabu_reconstruction")

Getting dataset (downloading if necessary) ...
... OK

# Parse the ".nx" file. This NX file is our entry point when it comes to data,
# as it's only the format which is remotely stable
# From this .nx file, build a data structure with readily available information
dataset_info = analyze_dataset(data_path)

# A tomography scan usually contains a series of darks/flats.
# These darks/flats are then averaged (the legacy pipeline called them 'refHST' and 'darkend')
# The flat-field normalization should be done only with reduced flats/darks.
update_dataset_info_flats_darks(dataset_info, True, darks_flats_dir=output_dir)

Could not load darks from /tmp/nabu_reconstructionstm_lna6/bamboo_reduced_darks.hdf5
Could not load flats from /tmp/nabu_reconstructionstm_lna6/bamboo_reduced_flats.hdf5
Loaded darks from /tmp/nabu_testdata_pierre/bamboo_reduced_darks.hdf5
Loaded flats from /tmp/nabu_testdata_pierre/bamboo_reduced_flats.hdf5

Estimate the center of rotation

from nabu.pipeline.estimators import CORFinder

cor_finder = CORFinder(
    "sliding-window",
    dataset_info,
    do_flatfield=True,
)
cor = cor_finder.find_cor()
print("Estimated center of rotation: %.2f" % cor)

Estimating center of rotation
CenterOfRotationSlidingWindow.find_shift({'window_width': None, 'roi_yxhw': None, 'median_filt_shape': None, 'padding_mode': None, 'peak_fit_radius': 1, 'high_pass': None, 'low_pass': None, 'return_validity': False})
Estimated center of rotation: 339.49

Define how the data should be processed

Instantiate the various components of the pipeline.

from nabu.io.reader import ChunkReader
from nabu.preproc.flatfield import FlatFieldDataUrls
from nabu.preproc.phase import PaganinPhaseRetrieval
from nabu.reconstruction.sinogram import SinoBuilder
from nabu.reconstruction.fbp import Backprojector

# Define the sub-region to read (and reconstruct)
# In each radio: will read (start_x, end_x, start_y, end_y)
sub_region = (None, None, 270, 289)

reader = ChunkReader(
    dataset_info.projections,
    sub_region=sub_region,
    convert_float=True,
)
radios = reader.data

flatfield = FlatFieldDataUrls(
    radios.shape,
    dataset_info.flats,
    dataset_info.darks,
    radios_indices=sorted(list(dataset_info.projections.keys())),
    sub_region=sub_region
)

paganin = PaganinPhaseRetrieval(
    radios[0].shape,
    distance=dataset_info.distance,
    energy=dataset_info.energy,
    delta_beta=250., # should be tuned
    pixel_size=dataset_info.pixel_size * 1e-6,
)

sino_builder = SinoBuilder(
    radios_shape=radios.shape,
    rot_center=cor,
    halftomo=dataset_info.is_halftomo
)
sino_shape = sino_builder.output_shape[1:]

reconstruction = Backprojector(
    sino_shape,
    angles=dataset_info.rotation_angles,
    rot_center=cor,
    padding_mode="edges",
    scale_factor=1/(dataset_info.pixel_size * 1e-4), # mu/cm
    extra_options={"centered_axis": True, "clip_outer_circle": True}
)

/home/pierre/.venv/py311/lib/python3.11/site-packages/skcuda/cublas.py:284: UserWarning: creating CUBLAS context to get version number
  warnings.warn('creating CUBLAS context to get version number')

Read data

reader.load_data(overwrite=True)

Pre-processing

flatfield.normalize_radios(radios) # in-place
print()

radios_phase = np.zeros_like(radios)
for i in range(radios.shape[0]):
    paganin.retrieve_phase(radios[i], output=radios_phase[i])

Build sinogram

# For 180 degrees scan, it's simply a transposition.
# For 360 degrees scan + half-acquisition, this duplicates data - not elegant but fine in this case
sinos = sino_builder.get_sinos(radios_phase)

Reconstruction

volume = np.zeros((sinos.shape[0], sinos.shape[-1], sinos.shape[-1]), "f")

for i in range(sinos.shape[0]):
    volume[i] = reconstruction.fbp(sinos[0])

import matplotlib.pyplot as plt

plt.figure()
plt.imshow(volume[0], cmap="gray")
plt.show()

Going further: GPU processing

All the above components have a Cuda backend: SinoBuilder becomes CudaSinoBuilder, PaganinPhaseRetrieval becomes CudaPaganinPhaseRetrieval, and so on. Importantly, the cuda counterpart of these classes have the same API.